AI Inference Serving Platform High-Level Design

Comprehensive backend architecture for scalable AI model serving with load balancing, caching, and monitoring capabilities.

PublishedOctober 20, 2025
Length11,249 words
Reading time57 min read

Backend HLD

This document presents a scalable backend architecture for serving AI models with focus on low-latency responses, efficient resource utilization, and seamless model lifecycle management across distributed systems.

1. Executive Summary

Clarifying questions

What's the expected read/write ratio and peak-to-average QPS?
- Is this a batch inference system or real-time serving? What's the expected QPS (e.g., 10K–100K peak)?
What are the consistency and latency requirements?
- Is sub-100ms p99 acceptable for inference, or does it need to be <50ms?
Is the system global or single-region?
- Will it serve users across multiple regions or just one primary region?
What's the expected growth in model count, user base, and inference volume?
- How many models are we expecting to serve? What's the projected growth rate?
What are the data ownership and compliance requirements?
- Do we need to support HIPAA, GDPR, or other regulations for inference data?
Are there external integrations with ML model training pipelines or other AI systems?
- Do we need to integrate with MLflow, Kubeflow, or other ML platforms?
What's the expected model size and inference latency per model?
- Are we dealing with large language models (LLMs), computer vision, or lightweight models?

Problem being solved

We are designing an AI Inference Serving Platform that enables organizations to deploy, manage, and scale machine learning models for real-time inference. The system must support a wide variety of model types (LLMs, vision models, NLP), handle high QPS with low latency, and provide operational capabilities like model versioning, A/B testing, and observability.

Back-of-envelope calculations

DAU → Peak QPS:
- 1M DAU, 10:1 peak-to-average ratio → Peak QPS = 1M * (86400s / 10) = 86,400 QPS
Storage per user/event:
- Average model size = 500MB (e.g., LLMs)
- Models per user = 2
- Storage per user = 1GB
- Total storage = 1M * 1GB = 1PB/year
Bandwidth (read + write throughput):
- Average request size = 5KB
- Peak read throughput = 86,400 QPS * 5KB = 432MB/s
- Write throughput (model uploads) = 100 MB/s (conservative estimate)
Cache hit rate assumption:
- Assume 80% cache hit rate → DB QPS = 20% of 86,400 = 17,280 QPS
Number of servers needed at peak:
- Assume per-server throughput = 10,000 QPS
- Servers needed = 86,400 / 10,000 = 9 servers (rounded up)

SLA targets

p50 latency: < 50ms
p95 latency: < 100ms
p99 latency: < 200ms
Availability: 99.9% (3.6s downtime/month)
RTO/RPO: RTO < 5 min, RPO < 1 min

Key architectural decisions summary

Microservices-based architecture to support independent scaling and deployment of model serving, versioning, and monitoring services.
Multi-model support with abstraction layer to handle various model formats (TensorFlow, PyTorch, ONNX).
Stateless inference services to enable horizontal scaling.
Event-driven model lifecycle management using Kafka for versioning and deployment triggers.
Caching at API gateway and model service layers to reduce latency.
Multi-region support for low-latency global access with active-active design.
Secure, auditable model deployment pipeline with RBAC and feature flags for A/B testing.

2. System Goals & Non-Functional Requirements

Latency Targets

p50: < 100ms for inference requests
p95: < 500ms for inference requests
p99: < 2s for inference requests
Model loading: < 5s for large models (1GB+)
Warm-up: < 30s for model preheating

Throughput Requirements

Average QPS: 10,000 inference requests/second
Peak QPS: 100,000 inference requests/second (10x average)
Batch processing: Support batch sizes up to 64 requests
Concurrent model serving: 100+ concurrent model instances per node

Availability Target

99.9% (3 9s) availability for inference service
99.99% (4 9s) for critical model endpoints
RTO: < 5 minutes for service recovery
RPO: < 1 minute for stateful operations

Durability and Consistency Requirements

Consistency Model: Eventual consistency for model versioning, strong consistency for inference results
Durability: All model artifacts and metadata persisted with 3x replication across zones
CAP Trade-off: Prioritize availability over consistency for inference serving, consistency for model management
Versioning: Support for A/B testing and rollback with 30-day retention

Data Retention and Compliance Requirements

Model artifacts: Retain for 2 years with version control
Inference logs: Retain for 90 days for audit and debugging
Compliance: GDPR compliance for user data, HIPAA support for healthcare models
Data residency: Support for multi-region model deployment with data sovereignty

Security and Regulatory Requirements

Authentication: OAuth2/OIDC with JWT tokens for service-to-service communication
Authorization: RBAC model with role-based access to model deployment and inference endpoints
Encryption: TLS 1.3 for all network communication, AES-256 for model artifact storage
Audit logging: Complete audit trail of model access, inference requests, and configuration changes
SOC2 Type II: Compliance with security and availability controls
HIPAA: Support for healthcare AI model deployments with audit logging and data protection
GDPR: Data processing consent management and right to deletion for user inference data

3. High-Level Architecture Diagram

C4Context
    title AI Inference Serving Platform - System Context

    Person(user, "User/Developer", "End user or developer interacting with the platform")
    
    System_Boundary(b1, "AI Inference Platform") {
        System(api_gateway, "API Gateway", "Entry point for all external requests")
        System(auth_service, "Auth Service", "Handles authentication and authorization")
        System(model_registry, "Model Registry", "Stores and manages AI models")
        System(inference_scheduler, "Inference Scheduler", "Manages model deployment and scheduling")
        System(model_runtime, "Model Runtime", "Executes inference requests")
        System(cache_service, "Cache Service", "Caches model outputs and metadata")
        System(event_stream, "Event Stream", "Handles asynchronous events and notifications")
        System(metrics_collector, "Metrics Collector", "Collects system metrics and logs")
    }
    
    System_Ext(model_store, "Model Store", "External model storage (S3/GCS)")
    System_Ext(external_api, "External API", "Third-party integrations")
    
    Rel(user, api_gateway, "Submits inference requests")
    Rel(api_gateway, auth_service, "Authenticates requests")
    Rel(api_gateway, model_registry, "Retrieves model metadata")
    Rel(api_gateway, inference_scheduler, "Routes requests to appropriate runtime")
    Rel(inference_scheduler, model_runtime, "Schedules inference jobs")
    Rel(model_registry, model_store, "Stores/retrieves models")
    Rel(model_runtime, cache_service, "Caches inference results")
    Rel(model_runtime, event_stream, "Publishes job completion events")
    Rel(api_gateway, metrics_collector, "Reports metrics")
    Rel(external_api, api_gateway, "Integrates with external systems")

    UpdateLayoutConfig($c4ShapeInRow = 3, $c4BoundaryInRow = 1)

C4Container
    title AI Inference Serving Platform - Container Diagram

    Person(user, "User/Developer", "End user or developer interacting with the platform")
    
    Container_Boundary(c1, "AI Inference Platform") {
        Container(api_gateway, "API Gateway", "nginx", "Entry point for all external requests")
        Container(auth_service, "Auth Service", "gRPC/REST", "Handles authentication and authorization")
        Container(model_registry, "Model Registry", "PostgreSQL", "Stores and manages AI models")
        Container(inference_scheduler, "Inference Scheduler", "gRPC/REST", "Manages model deployment and scheduling")
        Container(model_runtime, "Model Runtime", "gRPC/REST", "Executes inference requests")
        Container(cache_service, "Cache Service", "Redis", "Caches model outputs and metadata")
        Container(event_stream, "Event Stream", "Kafka", "Handles asynchronous events and notifications")
        Container(metrics_collector, "Metrics Collector", "Prometheus/Grafana", "Collects system metrics and logs")
    }
    
    Container_Ext(model_store, "Model Store", "S3/GCS", "External model storage")
    Container_Ext(external_api, "External API", "REST", "Third-party integrations")
    
    Rel(user, api_gateway, "Submits inference requests")
    Rel(api_gateway, auth_service, "Authenticates requests")
    Rel(api_gateway, model_registry, "Retrieves model metadata")
    Rel(api_gateway, inference_scheduler, "Routes requests to appropriate runtime")
    Rel(inference_scheduler, model_runtime, "Schedules inference jobs")
    Rel(model_registry, model_store, "Stores/retrieves models")
    Rel(model_runtime, cache_service, "Caches inference results")
    Rel(model_runtime, event_stream, "Publishes job completion events")
    Rel(api_gateway, metrics_collector, "Reports metrics")
    Rel(external_api, api_gateway, "Integrates with external systems")

    UpdateLayoutConfig($c4ShapeInRow = 3, $c4BoundaryInRow = 1)

Major Components and Services

Core Platform Services:

API Gateway: Single entry point handling all external requests with rate limiting, authentication, and request routing
Auth Service: Manages user authentication and authorization using JWT tokens with RBAC model
Model Registry: Centralized repository storing model metadata, versioning, and lifecycle management
Inference Scheduler: Distributes inference workloads to appropriate runtime environments based on model requirements and resource availability
Model Runtime: Executes actual inference operations using optimized ML frameworks (TensorFlow, PyTorch, ONNX)
Cache Service: Implements multi-level caching strategy for improved response times and reduced inference load
Event Stream: Asynchronous messaging system for handling job completion, status updates, and notifications
Metrics Collector: Aggregates performance metrics for monitoring and alerting

External Integrations:

Model Store: Cloud storage (S3/GCS) for model artifacts and weights
External API: Integration points with third-party services and data sources

Traffic Flow and Data Flow

User Request Flow: User sends inference request through API Gateway → Auth Service validates token → Model Registry retrieves model metadata → Inference Scheduler routes to appropriate runtime → Model Runtime executes inference → Results cached and returned
Event Flow: Inference jobs publish completion events to Kafka → Event Stream processes notifications → Metrics Collector aggregates performance data
Model Lifecycle Flow: Model uploads to S3 → Metadata stored in Model Registry → Scheduler deploys to runtime environments → Inference requests served from deployed models

The architecture supports horizontal scaling, multi-model inference, and seamless integration with external systems while maintaining strong security and observability controls.

4. Service Architecture

Microservices vs. Monolith vs. Modular Monolith Decision

For an AI Inference Serving Platform, we'll adopt a modular monolith approach for the initial phase with clear service boundaries that can evolve into independent microservices. This decision balances:

Development velocity - faster iteration and deployment cycles for rapid AI model experimentation
Operational simplicity - reduced complexity in inter-service communication and monitoring
Resource efficiency - shared infrastructure for multiple AI services (model loading, caching, etc.)
Scalability - modular monolith can be split into microservices as the platform scales

Service Decomposition Strategy

The system will be decomposed around these core domains:

Model Registry Service - manages model versions, metadata, lifecycle
Inference Service - handles model execution, batching, and result formatting
Scheduling Service - manages model deployment, resource allocation, and scaling
Metrics & Monitoring Service - collects and aggregates inference metrics
User Management Service - handles authentication, authorization, and user profiles
Event Processing Service - handles model training events, status updates, and notifications

Service Boundaries and Contracts

Each service will maintain clear boundaries with well-defined APIs:

Model Registry Service: RESTful API for model registration, versioning, and metadata management
Inference Service: gRPC-based API for model prediction requests with streaming support
Scheduling Service: RESTful API for deployment management and resource allocation
Metrics Service: Prometheus-compatible metrics endpoint with historical data aggregation
User Service: JWT-based authentication and RBAC enforcement
Event Service: Event-driven API for model lifecycle events and notifications

Inter-service Communication Pattern

Synchronous: gRPC between Inference Service and Scheduling Service for real-time model deployment
Asynchronous: Event-driven communication between Model Registry and Event Processing Service using Kafka
HTTP/REST: User Service to Inference Service for authentication and authorization

Service Mesh Consideration

We'll implement Istio as a service mesh for:

Traffic management (canary deployments, A/B testing)
Security (mTLS between services)
Observability (distributed tracing, metrics collection)
Resilience patterns (circuit breakers, retries)

Service Registry and Discovery Strategy

Service Registry: Kubernetes-based service discovery with built-in health checks
Service Mesh Integration: Istio's service registry for enhanced observability and traffic management
Health Monitoring: Liveness and readiness probes with custom health endpoints for each service

Service Design Principles

Stateless Services: All services designed to be stateless for horizontal scaling
Idempotent Operations: Critical operations made idempotent to handle retries gracefully
Service Isolation: Each service maintains its own database and data stores to ensure independence
Async Processing: Critical workflows like model training completion and result processing are handled asynchronously to avoid blocking primary operations
Graceful Degradation: Services designed to degrade gracefully under high load or partial failures

Cross-service Data Ownership

No shared databases: Each service owns its data store
Event-driven data synchronization: Use of Kafka for cross-service communication and data consistency
Data replication strategy: Service-specific read replicas for performance optimization where needed

CQRS Pattern Application

Model Registry Service: Implements CQRS with separate write (model registration) and read (model metadata) models
Inference Service: Read model optimized for fast prediction responses, write model for training and deployment updates
Metrics Service: Read model optimized for query performance with pre-aggregated statistics

Event Sourcing Consideration

Use case: Model lifecycle events (deploy, update, retire) will be stored as events in an event store
Trade-offs:
- Pros: Complete audit trail, replayable model state changes, system recovery capability
- Cons: Additional complexity in event processing and storage management
Decision: Implement event sourcing for model lifecycle events but not for every inference request due to complexity and storage cost considerations

Scalability Considerations

Horizontal scaling: Services designed to scale horizontally with Kubernetes
Load balancing: Round-robin and weighted distribution based on service capacity
Resource allocation: CPU and memory limits per service with auto-scaling capabilities
Service composition: Services can be composed to handle complex inference workflows

Reliability Patterns

Circuit Breaker: Implemented at service boundaries to prevent cascading failures
Bulkhead: Isolated resource pools for different model types and inference workloads
Timeout Handling: Configurable timeouts with fallback strategies for long-running operations
Retry Logic: Exponential backoff with jitter for transient failures

Future Evolution Path

The modular monolith design provides a clear migration path to independent microservices:

Phase 1: All services in single monolith with clear boundaries
Phase 2: Extract services that show high traffic or complexity (e.g., Inference Service)
Phase 3: Full microservice decomposition with independent scaling and deployment

This approach provides immediate development velocity while maintaining architectural flexibility for future growth.

5. Data Architecture

Database Selection per Service

Model Registry Service

Choice: PostgreSQL (v15+) with JSONB columns
Justification:
- Metadata management requires complex queries and relationships (model versions, tags, owners)
- JSONB enables flexible schema for model configurations
- ACID compliance needed for version consistency and audit trails
- Strong ecosystem support for ORM (SQLAlchemy, GORM)

Inference Request Queue Service

Choice: Redis (v7+) with Stream data structure
Justification:
- High-throughput, low-latency queuing for inference requests
- Stream-based processing enables efficient consumer groups and message replay
- Built-in persistence with configurable retention policies
- Excellent support for rate limiting and request batching

Model Artifact Storage Service

Choice: S3-compatible object storage (MinIO/GCS)
Justification:
- Large model files (GBs) stored efficiently with metadata indexing
- Cost-effective for cold storage and archival
- Integration with ML frameworks (TensorFlow, PyTorch model zoo)
- Built-in versioning and lifecycle policies for model artifacts

Metrics & Monitoring Service

Choice: TimescaleDB (PostgreSQL extension)
Justification:
- Time-series data with high cardinality (per-model, per-request metrics)
- Efficient querying for latency, throughput, and error rate trends
- Built-in compression and retention policies for cost optimization
- Seamless integration with Prometheus/Grafana stack

Data Partitioning Strategy

Model Registry & Metadata Service

Strategy: Hash-based sharding by model ID (UUID)
Justification:
- Uniform distribution across shards for load balancing
- Predictable query routing with consistent hash ring
- Supports horizontal scaling to handle millions of models

Inference Request Queue

Strategy: Range-based partitioning by model type (text, image, audio)
Justification:
- Prioritization of inference requests by model category
- Enables resource allocation per model type (GPU vs CPU)
- Easier to implement priority queues and batching logic

Data Replication Strategy

Primary-Replica Model

Configuration: 3-node primary-replica setup per service
Justification:
- High availability with automatic failover (Raft consensus)
- Read-heavy workloads can be distributed across replicas
- Satisfies 99.9% availability SLA requirement

Cross-service Data Ownership

Rule: No shared databases between services
Enforcement:
- Service-specific database schemas and tables
- Event-driven data synchronization via message broker (Kafka)
- Audit logs for cross-service access attempts

CQRS Pattern Application

Model Registry Service

Usage: Read-heavy workload with separate write path
Implementation:
- Write operations (model creation, versioning) → PostgreSQL primary
- Read operations (model metadata queries) → Redis cache + read replicas

Inference Metrics Service

Usage: Write-heavy ingestion pipeline with read-optimized queries
Implementation:
- Ingestion → Kafka streams (batched metrics)
- Query layer → TimescaleDB with materialized views

Event Sourcing Consideration

Use Case: Model lifecycle events (training completion, deployment, deletion)

Decision: Not implemented due to complexity trade-off
Rationale:
- Simpler state management with direct database updates
- Event sourcing would require significant additional infrastructure (event store, replay logic)
- Current audit requirements can be met with PostgreSQL triggers and logging

Object Storage Design

Storage Tiering Strategy

Hot Tier: S3 Standard (100ms latency, 99.99% availability)
Warm Tier: S3 Standard-IA (30s latency, 99.9% availability)
Cold Tier: S3 Glacier (4h latency, 99.9% availability)
Archive Tier: S3 Glacier Deep Archive (12h latency, 99.9% availability)

Access Patterns

High-frequency: Recent model versions (hot tier)
Medium-frequency: Active model versions (warm tier)
Low-frequency: Historical models (cold tier)

Backup and Restore Strategy

RTO/RPO Targets

RTO: 15 minutes for critical model artifacts
RPO: 5 minutes for metadata and configuration data

Backup Approach

Daily Incremental Backups: PostgreSQL, Redis, TimescaleDB
Full Snapshots: Weekly for S3 model artifacts with versioned backups
Cross-region Replication: For disaster recovery (active-active)
Automated Restore Testing: Monthly restore validation for all services

Recovery Process

Validate backup integrity with checksums
Restore PostgreSQL and Redis from latest snapshot
Rehydrate model artifacts from S3 with version consistency checks
Validate service health and performance post-restore

6. Caching Architecture

Caching Layers and Strategy

The AI inference serving platform implements a multi-tier caching strategy to minimize latency and reduce load on underlying model servers:

graph LR
    A[Client] --> B[API Gateway]
    B --> C[Request Router]
    C --> D[Inference Cache L2]
    D --> E[Model Server]
    E --> F[Response Cache L1]

L1 Cache (Per-Instance): Each inference service instance maintains an in-memory cache using Redis or Memcached for the most recently accessed model inputs and their corresponding outputs. This serves as a fast L1 cache with sub-millisecond access times.

L2 Cache (Distributed): A shared distributed cache layer using Redis Cluster or DynamoDB Global Tables to store frequently accessed model responses across all service instances. This enables cache sharing between services and provides persistence across service restarts.

Cache Design Parameters

Cache Size: Each service instance maintains 10GB of L1 cache (assuming ~500MB per model response, with 20 concurrent responses cached)
Eviction Policy: LRU (Least Recently Used) with time-based expiration (TTL = 30 minutes for most responses, 1 hour for high-frequency queries)
Cache Warm-up: Background warming of top-20% most frequent model inputs using historical query patterns
Cache Invalidation: Write-through pattern for cache updates, with invalidation triggered on model version changes or input parameter modifications

Cache Hit Rate Assumptions and Impact

With a conservative 70% cache hit rate for the L2 layer, we expect:

30% of inference requests will reach the model server
For peak QPS of 15,000 (assuming 10:1 peak-to-average ratio), model server QPS = 4,500
This reduces database read load by approximately 70% and model server load by 30%

Cache Consistency Strategy

Cache-Aside Pattern: Client-side cache lookup with fallback to model server
Cache Invalidation: Event-driven invalidation using pub/sub when model versions change or input parameters evolve
Cache Synchronization: Periodic cache consistency checks to detect stale entries in distributed cache

Cache Warming Strategy

Warm-up Period: 15-minute warm-up window during service startup
Historical Pattern Analysis: Use query logs to identify top 500 most frequent inputs for pre-caching
Predictive Caching: Machine learning model to predict likely future queries based on input patterns

7. Messaging & Event Streaming Architecture

Message Broker Selection

For AI inference serving platform, we'll use Apache Kafka as the primary message broker due to:

High throughput requirements: AI model inference requests can be bursty and require handling thousands of concurrent requests
Durability guarantees: Critical for maintaining inference job state and results across system failures
Scalable partitioning: Enables horizontal scaling of inference job processing pipelines
Stream processing capabilities: Supports real-time model training and retraining workflows
Ecosystem maturity: Rich ecosystem of tools for monitoring, management, and integration with ML frameworks

Event Schema Registry and Versioning

We'll implement Confluent Schema Registry for:

Schema evolution management: Handle backward/forward compatibility for inference request/response formats
Type safety: Prevent malformed inference requests from reaching model servers
Version control: Support for multiple inference API versions (v1, v2) with automatic migration paths
Validation: Enforce schema compliance at message production time

Topic Partitioning and Consumer Group Design

Topic Structure:

inference-requests (32 partitions) - High-throughput, low-latency inference jobs
model-training-events (16 partitions) - Model retraining and update notifications
inference-results (32 partitions) - Response delivery and metrics collection
model-status-updates (8 partitions) - Model availability and performance status

Consumer Group Strategy:

Inference request consumers: 4 consumer groups (batch processing, real-time, priority, low-priority)
Training event consumers: 2 consumer groups (model update, monitoring)
Result consumers: 2 consumer groups (metrics aggregation, error handling)

Event Ordering Guarantees

Per-partition ordering:

Critical for maintaining inference job sequence integrity within batches
Requests within same partition maintain strict ordering
Different partitions can process concurrently without ordering constraints

Global ordering:

Not required for inference requests (can be processed in any order)
Only needed for model status updates where sequence matters

Exactly-once vs. At-least-once Processing Trade-offs

Decision: At-least-once processing with idempotent consumers

Rationale:

Consistency: AI inference results must be deterministic and reproducible
Reliability: Retry logic for failed inference jobs is acceptable (reprocessing cost is low)
Performance: Avoids complex state management overhead of exactly-once semantics
Simplicity: Easier to implement and debug compared to full idempotent processing

Event Replay and Reprocessing Strategy

Replay capability:

Kafka retention policy set to 7 days for all topics
Event replay API available through dedicated admin endpoints
Audit trail maintained in separate audit-log topic for compliance

Reprocessing workflow:

Admin dashboard with "reprocess failed jobs" capability
Batch reprocessing of inference jobs with same input parameters
Retry logic with exponential backoff for transient failures

Dead Letter Queue and Poison Pill Handling

DLQ Strategy:

Primary DLQ: inference-failed-requests topic with 10x partition count of main topic
Secondary DLQ: inference-poison-pill for malformed or malicious requests
Automatic forwarding: Failed messages automatically forwarded to DLQ after 3 retries

Poison Pill Detection:

Rate limiting: Detect high-frequency malformed requests
Signature validation: Verify request signatures for known malicious patterns
Quarantine mechanism: Temporarily block IP addresses or user accounts with suspicious behavior

Recovery Process:

Manual review of DLQ messages through admin console
Automated reprocessing for known valid patterns
Alerting when DLQ queue size exceeds threshold (500 messages)

Cross-service Event Integration

Inference Service Events:

model-loaded - Model successfully loaded into inference engine
inference-completed - Job completed with result and latency metrics
model-unloaded - Model removed from inference engine

Training Service Events:

training-started - New training job initiated
model-updated - Model version updated in inference service
training-failed - Training job failed with error details

Monitoring Events:

latency-alert - Inference latency exceeds threshold
resource-utilization - CPU/GPU memory usage thresholds exceeded

Event Processing Pipeline

Real-time Inference Pipeline:

User Request → Kafka Ingestion → Model Server → Result → Kafka Response

Training Pipeline:

Model Update → Kafka Training Event → Training Service → Model Server Update

Performance and Scalability Considerations

Throughput Targets:

10,000 inference requests/sec peak throughput
500ms p99 latency for inference result delivery
100ms p99 for model status updates

Resource Allocation:

Kafka cluster with 3 brokers in multi-AZ setup
Memory allocation: 8GB per broker for optimal throughput
Disk storage: 2TB SSD per broker with 3x replication factor

Monitoring Integration:

Kafka metrics exposed via Prometheus for alerting
Consumer lag monitoring with auto-scaling triggers
Partition utilization tracking to prevent hotspots

Integration with ML Operations Tools

MLflow Integration:

Event-driven model versioning and deployment
Training job status updates to MLflow tracking server

Model Registry Events:

model-registered - New model version registered
model-deprecated - Model version deprecated for inference
model-promoted - Model promoted to production

Security Considerations

Authentication:

SASL/SCRAM authentication for Kafka clients
TLS encryption for all inter-service communication

Authorization:

ACL-based topic access control per service
Service-to-service authentication with mTLS

Data Protection:

Sensitive inference data encrypted at rest and in transit
Audit logging for all inference job processing events

8. Reliability Engineering

Failure Mode Analysis

Single Points of Failure:

Model Serving Service: Single point for all model inference requests
Model Registry Service: Centralized model metadata and versioning
API Gateway: Entry point for all external traffic
Shared Cache Layer: Single cache instance for model artifacts and results
Centralized Database: Single source of truth for model metadata and configuration

Critical Failure Scenarios:

Model loading timeout causing cascading failures
Cache miss storm overwhelming model servers
Database connection pool exhaustion
Network partition between services
GPU/CPU resource exhaustion during peak load

Fault Tolerance Patterns

Circuit Breaker Pattern:

Implemented at model serving service level for each model endpoint
Trip when failure rate exceeds 50% over 10 seconds with min 20 requests
Half-open state for health checks every 30 seconds
Prevents cascading failures and allows graceful degradation

Bulkhead Pattern:

Isolated model inference pools per model family (NLP, CV, etc.)
Resource quotas per pool to prevent one model from consuming all resources
Separate thread pools for different model types to avoid cross-contamination

Timeout & Retry Strategy:

500ms timeout per model inference request
Exponential backoff retry (100ms, 200ms, 400ms) with max 3 attempts
Retry only for transient errors (5xx, timeout)
Fail fast for client errors (4xx)

Graceful Degradation:

Fallback to CPU-based inference when GPU resources unavailable
Cached model metadata fallback during registry outages
Reduced precision models as fallback for compute-intensive models

Multi-Region / Multi-AZ Strategy

Active-Active Architecture:

Primary region handles 70% of traffic, secondary handles 30%
Cross-region model synchronization using event-driven updates
Geo-replicated model registry with read replicas in each region

Cross-Region Failover:

Automatic failover within 30 seconds of primary region outage
Traffic shifting via DNS or service mesh
Model artifacts replicated to secondary region with 5-minute sync window

Chaos Engineering Strategy

GameDays:

Monthly chaos experiments targeting model serving service
Simulate GPU unavailability, network partitions, and cache failures
Measure system resilience and recovery time

Steady-State Hypotheses:

System maintains 99.9% availability during simulated failures
Response time stays within SLA bounds (p99 < 200ms)
Error rate remains below threshold during chaos experiments

Disaster Recovery Plan

RTO/RPO Targets:

RTO: 5 minutes for model serving service failover
RPO: 1 minute for model metadata consistency
Recovery Time: 30 minutes for complete region recovery

Recovery Procedures:

Manual failover to secondary region within 5 minutes
Re-sync model artifacts from primary region
Validate service health and traffic routing
Gradual traffic shift back to primary region

Runbook Design for Top-5 Failure Scenarios

1. Model Loading Timeout

1. Circuit breaker trips for model endpoint
2. Route traffic to fallback CPU models
3. Alert on service health dashboard
4. Retry model loading with exponential backoff
5. Manual intervention if timeout persists > 10 minutes

2. Cache Miss Storm

1. Identify cache miss pattern in metrics dashboard
2. Implement cache warming for high-traffic models
3. Reduce cache TTL to 5 minutes for volatile models
4. Scale up cache instances if needed
5. Monitor cache hit rate recovery

3. GPU Resource Exhaustion

1. Detect resource exhaustion in monitoring dashboard
2. Route traffic to CPU-based inference models
3. Scale up GPU instances or reduce concurrent requests
4. Implement model priority queue for resource allocation
5. Alert on service health and resource utilization

4. Database Connection Pool Exhaustion

1. Monitor database connection pool metrics
2. Implement connection pooling with max 500 connections per service
3. Add circuit breaker for database operations
4. Retry logic with exponential backoff for database calls
5. Alert on pool exhaustion and connection timeout rates

5. Network Partition

1. Detect network partition in service mesh
2. Route traffic to local replicas if available
3. Failover to secondary region within 30 seconds
4. Re-establish connections once network restored
5. Validate service health and traffic routing after recovery

9. Observability Architecture

Logging Stack

The system will implement a structured logging approach using JSON format for all services to enable efficient log parsing and analysis. The logging stack will consist of:

Loki as the centralized log storage and query engine, chosen for its efficient storage model and seamless integration with Promtail
Promtail as the log collector running as a sidecar on each pod, configured to scrape logs from application containers and forward to Loki
Grafana for log visualization and dashboard creation, enabling real-time monitoring of system behavior
Log aggregation pipeline with log rotation and retention policies (30 days for operational logs, 90 days for audit logs)

Distributed Tracing

Distributed tracing will be implemented using OpenTelemetry with Jaeger as the backend for trace visualization:

All service calls will be instrumented to propagate trace context (W3C TraceContext) for end-to-end request tracking
Tracing will be enabled for all critical paths including model inference, data preprocessing, and response generation
Sampling rate will be set to 10% for production traces to balance observability with performance overhead
Trace data will be stored for 30 days with retention policy for compliance and debugging

Metrics Stack

The metrics infrastructure will use Prometheus as the primary time-series database with Grafana for visualization:

Service-level metrics: QPS, latency (p50/p95/p99), error rates, throughput, cache hit ratio
Model-specific metrics: inference time, model version usage, prediction accuracy (if available)
Infrastructure metrics: CPU/memory utilization, disk I/O, network throughput, container restarts
Custom business metrics: model accuracy, user satisfaction scores, A/B test results
Alerting thresholds: p99 latency > 500ms, error rate > 1%, cache hit ratio < 70%

Alerting Strategy

The alerting system will implement a multi-tier severity model:

Critical (P0): Service unavailability, latency > 1s, cache failure, model accuracy drop below threshold
High (P1): Error rate > 0.5%, QPS drop > 30%, cache hit ratio < 80%
Medium (P2): Error rate > 0.1%, QPS drop > 10%
Low (P3): Cache warmup, model version updates

Alerts will be routed through PagerDuty with escalation policies and on-call rotation. Alerting rules will be version-controlled in the infrastructure repository.

SLI/SLO Definition and Error Budget Tracking

Service Level Indicators (SLIs) will be defined for critical paths:

API Response Latency: p99 < 500ms (target: 200ms)
API Availability: 99.9% uptime (target: 99.99%)
Model Accuracy: >95% for classification tasks (target: 98%)

Service Level Objectives (SLOs) will be set at 99.9% availability and 200ms p99 latency for the core inference API. Error budget will be tracked using Telementry and visualized in Grafana dashboards with burn rate alerts.

Capacity Planning and Load Forecasting

Historical capacity analysis: Weekly analysis of QPS trends, model usage patterns, and resource utilization
Load forecasting models: Time-series forecasting using Prophet or ARIMA for QPS prediction
Auto-scaling correlation: Metrics-driven scaling decisions with capacity planning dashboards
Seasonality and trend analysis: Weekly/monthly patterns in model inference demand to inform resource allocation

The observability stack will be designed with multi-tenancy support to accommodate different teams and model owners, ensuring that logs, traces, and metrics are properly segmented while maintaining centralized visibility for system-wide monitoring and debugging.

10. API Gateway & Edge Layer

API Gateway Selection

For an AI Inference Serving Platform, I recommend implementing a custom API gateway built on top of Nginx or Envoy Proxy with gRPC-Web support. This decision balances the need for high-performance request routing, low-latency inference handling, and extensibility for AI-specific features like model versioning, request batching, and adaptive routing.

Justification:

Performance: Nginx/Envoy can handle millions of concurrent requests with sub-millisecond latency
Flexibility: Custom implementation allows for AI-specific optimizations like model-aware routing and dynamic batch sizing
Control: Enables precise control over inference request flow, including rate limiting per model and priority queuing
Cost-efficiency: Reduces reliance on managed services while maintaining performance comparable to AWS API Gateway or Kong

Request Routing Strategy

The gateway implements adaptive model routing based on:

Model availability and health status
Resource utilization (GPU/CPU) of inference endpoints
Request priority and SLA requirements

Routing logic:

Primary route to the most optimal model instance (based on current load and model type)
Fallback to backup instances with same model version
Dynamic re-routing during model updates or failures

Authentication and Authorization at Gateway Layer

Authentication:

JWT-based authentication for client service accounts with short TTLs (15 minutes)
mTLS between gateway and internal services for service-to-service communication
Integration with AWS Cognito or Auth0 for external identity provider support

Authorization:

RBAC model with role-based access to specific models and inference endpoints
Client-level rate limits per API key (e.g., 1000 requests/minute)
Model-level permissions (e.g., "read" vs "write" access to specific model versions)

Rate Limiting and Throttling Design

Global rate limiting:

10,000 requests/second global limit with 500ms burst tolerance
Per-client limits (100 requests/second) with sliding window algorithm

Per-model rate limiting:

High-priority models (e.g., real-time fraud detection) get 500 requests/second
Batch models (e.g., analytics) get 100 requests/second with longer processing windows

Request Transformation and Protocol Translation

Protocol translation:

HTTP/1.1 → gRPC for internal service communication
JSON → Protobuf payload conversion for inference requests
Support for both REST and gRPC clients via unified gateway interface

Request transformation:

Model version normalization (e.g., "latest" → "v1.2.3")
Request payload validation and schema enforcement
Batch request aggregation for improved throughput

BFF (Backend for Frontend) Design

BFF pattern implementation:

Per-client type BFF services (Mobile, Web, IoT)
Client-specific request shaping and response formatting
Caching layer for frequent inference patterns (e.g., user profile lookups)
Request aggregation to reduce round-trips for complex AI workflows

Mobile BFF features:

Response compression and chunked delivery
Offline request queuing with retry logic
Adaptive model selection based on device capabilities

Web BFF features:

Real-time streaming for continuous inference results
Client-side caching with cache invalidation strategies
Multi-model request batching and parallel execution

Performance Considerations

Latency targets:

p50: < 20ms for simple inference requests
p95: < 100ms for complex batched requests
p99: < 500ms for all requests including model loading

Throughput targets:

Peak QPS: 50,000 concurrent inference requests
Read throughput: 100 MB/s average (200 MB/s peak)
Write throughput: 50 MB/s average (100 MB/s peak)

Scalability:

Stateless gateway design with minimal session storage
Horizontal scaling capability with consistent hashing for sticky sessions where needed
Auto-scaling based on QPS and response time metrics

Integration Points

External integrations:

CloudWatch or Datadog for monitoring and alerting
AWS S3 or GCS for model artifact storage and retrieval
Redis for caching of frequently accessed model metadata and client tokens

Internal services:

Model registry service for model version management
Inference engine orchestrator for request routing and execution
Metrics collector service for performance monitoring and analytics

Failure Handling

Gateway-level failure modes:

Circuit breaker pattern for unhealthy model endpoints
Graceful degradation to cached responses or default models
Health check integration with service discovery for automatic failover

Client-facing error handling:

Standardized error response format with detailed error codes
Retry logic for transient failures (503, 429 responses)
Client-side timeout configuration with configurable backoff strategies

This gateway design provides the foundation for a production-ready AI inference serving platform that can scale to handle millions of concurrent requests while maintaining low latency and high availability for AI model inference workloads.

11. Authentication & Authorization Architecture

Identity Provider Integration

For an AI inference serving platform, we'll integrate with AWS Cognito as the primary identity provider. This choice provides:

Native integration with AWS services and infrastructure
Support for federated identities (Google, Facebook, SAML)
Built-in user pools and identity federation capabilities
Seamless integration with AWS IAM for service-to-service authentication
Cost-effective for moderate-scale deployments

Alternative providers like Auth0 or Okta were considered but rejected due to higher operational overhead and complexity in integrating with AWS-native services.

OAuth2 / OIDC Flow Design

sequenceDiagram
    participant Client
    participant API_Gateway
    participant Auth_Service
    participant Cognito

    Client->>API_Gateway: Request protected resource
    API_Gateway->>Cognito: Redirect to login
    Cognito-->>Client: Login page
    Client->>Cognito: Submit credentials
    Cognito->>Auth_Service: Validate credentials
    Auth_Service->>Cognito: Issue ID Token + Access Token
    Cognito-->>Client: Redirect with tokens
    Client->>API_Gateway: Request with Bearer token
    API_Gateway->>Auth_Service: Validate token
    Auth_Service-->>API_Gateway: Token validation result
    API_Gateway->>Client: Resource access granted

The platform implements the Authorization Code Flow with PKCE for web applications and Implicit Flow for mobile/desktop apps. This ensures secure token handling while maintaining good UX.

Service-to-Service Authentication

For service-to-service communication, we implement mTLS (mutual Transport Layer Security) with SPIFFE/SPIRE for identity management:

All internal services communicate over mTLS
SPIFFE identities are used to establish trust between services
Certificate rotation handled automatically by SPIRE
Service accounts with granular permissions for each inference model

RBAC vs ABAC Model Design

We implement a hybrid approach combining both models:

RBAC (Role-Based Access Control) for:

Admin roles (platform administrators, model owners)
Team-based access control (engineering teams, data scientists)

ABAC (Attribute-Based Access Control) for:

Model-specific permissions (e.g., "can only access models tagged with 'finance'")
Time-based access restrictions (e.g., "access allowed only during business hours")
Resource-level permissions (e.g., "can access model X but only for inference, not training")

This hybrid approach balances operational simplicity with fine-grained control.

Token Revocation Strategy

We implement a short-lived JWT token strategy with:

Access tokens: 15-minute TTL (refreshed via refresh tokens)
Refresh tokens: 24-hour TTL with rotation on use
Blacklist mechanism: For immediate revocation of compromised tokens (stored in Redis cache)
Token rotation: Refresh token rotation on every use to prevent replay attacks

Token Management Architecture

graph TD
    A[Client] --> B[API Gateway]
    B --> C[Auth Service]
    C --> D[Token Store]
    D --> E[Cognito/Keycloak]
    C --> F[SPIRE]
    F --> G[Service Identity]
    C --> H[JWT Signer]

Authentication Flow Summary

User Authentication: Users authenticate through Cognito, receiving JWT tokens
Service Authentication: Internal services use SPIFFE/SPIRE for mTLS and service identity
Token Validation: API Gateway validates JWT tokens before forwarding requests
Authorization Check: RBAC/ABAC policies enforced at service boundaries
Token Refresh: Refresh tokens rotated and validated for continued access

Security Considerations

All tokens are encrypted in transit using TLS 1.3
Token storage uses secure, encrypted databases with proper access controls
Regular security audits of authentication flows and token handling
Integration with AWS WAF for protection against common attack vectors
Regular rotation of signing keys and certificate authorities

Compliance Alignment

The authentication architecture aligns with:

SOC2 Type II requirements for secure access control
GDPR compliance through proper token handling and user consent management
HIPAA considerations for healthcare AI model access (if applicable)
NIST 800-63B standards for digital identity management

This design provides a robust, scalable authentication and authorization framework that supports both human users and service-to-service communication while maintaining compliance with enterprise security standards.

12. Search Architecture

Search Use Cases and Requirements

The AI Inference Serving Platform does not have core search capabilities as its primary function. The platform's main purpose is to serve AI model inference requests with low latency and high throughput, managing model deployment, versioning, and serving infrastructure. While search might be used for administrative purposes (e.g., searching models by tags or metadata), it's not a core capability that requires dedicated search infrastructure.

Search Indexing Pipeline Design

If search capabilities were to be added as an optional feature, the design would follow this pipeline:

Source → Transform → Index

Source: Model metadata (name, tags, version, owner, creation date, performance metrics) from the model registry service
Transform: Normalize and enrich metadata fields (e.g., convert timestamps, extract categories from tags)
Index: Store in a search engine (Elasticsearch or OpenSearch) with appropriate field mappings

Index Schema and Query Patterns

Index Fields:

model_id (primary key, string)
name (text field with full-text search)
description (text field)
tags (array of strings, searchable)
owner (string, filterable)
created_at (timestamp, sortable)
version (string, filterable)
performance_metrics (nested object, filterable)

Query Patterns:

Full-text search on model names and descriptions
Filter by owner or tags
Sort by creation date or performance metrics

Real-time vs. Near-real-time Indexing Trade-offs

Real-time indexing:

Pros: Immediate search results, better for admin UIs
Cons: Higher latency for indexing operations, increased system load

Near-real-time indexing:

Pros: Better performance, reduced resource usage
Cons: Delayed search results (1-5 minutes)

Relevance Tuning Strategy

Field boosting: Prioritize name and description fields
Custom scoring: Weight tags and owner fields more heavily for admin searches
Query-time boosting: Adjust weights based on user behavior (click-through rates)

Search Scaling Design

Sharding strategy: Hash-based sharding on model_id for even distribution
Replication: 3x replica count for high availability
Index partitioning: Time-based partitioning (monthly) to enable efficient cleanup

Implementation Considerations

Since search is not a core capability, any search infrastructure would be:

Optional and isolated from the primary inference serving path
Implemented as a separate microservice or optional component
Not part of the critical request processing path
Subject to separate scaling and resource allocation policies

Alternatives Considered

Full-text search in database:
- Pros: Simple to implement, no additional infrastructure
- Cons: Performance degradation for large datasets, limited query capabilities
In-memory search:
- Pros: Fast lookups, good for small datasets
- Cons: Memory intensive, not scalable for large model catalogs
External search service:
- Pros: Mature solutions, robust features
- Cons: Additional complexity, cost implications

The decision to exclude dedicated search infrastructure aligns with the platform's focus on inference serving performance and reliability. Any search capability would be implemented as a secondary feature with minimal impact on core system performance.

13. Scalability Deep Dive

Bottleneck Identification

API Gateway Layer:

Peak QPS: 50K (assuming 100M DAU with 50ms avg latency)
Expected bottleneck: Rate limiting, authentication overhead
Estimated capacity: 10K QPS per instance (with proper load balancing)

Model Serving Layer:

Peak QPS: 50K (assuming 100M DAU with 50ms avg latency)
Expected bottleneck: GPU memory constraints, model loading overhead
Estimated capacity: 500-2000 QPS per GPU instance (varies by model size)

Database Layer:

Read QPS: 10K (assuming 50% read-heavy workload)
Write QPS: 5K (model metadata, user preferences)
Expected bottleneck: Connection pooling limits, query optimization
Estimated capacity: 10K QPS per instance (with read replicas)

Cache Layer:

Hit rate: 85% (assuming 200M requests/day with 15% cache misses)
Expected bottleneck: Cache warming, invalidation strategy
Estimated capacity: 100K QPS per cache node

Horizontal vs. Vertical Scaling Decisions

Stateless Services (API Gateway, Model Router):

Horizontal scaling preferred due to stateless nature
Auto-scaling based on QPS and latency metrics
Service mesh integration for traffic management

Stateful Services (Model Serving, Cache):

Hybrid approach: Vertical scaling for GPU instances, horizontal for cache
Model serving requires dedicated GPU resources (limited by hardware)
Cache can scale horizontally with Redis cluster

Database Layer:

Horizontal scaling via read replicas and sharding
Vertical scaling for write-heavy operations (primary database)
Connection pooling and query optimization as first-line scaling

Database Scaling Path

Single Instance: Handle initial load and metadata storage
Read Replicas: Scale reads with 3-5 replicas for read-heavy workloads
Sharding: Horizontal partitioning by user ID or model type for large-scale operations
Distributed DB: Consider CockroachDB or Spanner for cross-region consistency

Stateless vs. Stateful Service Design

Stateless Services:

API Gateway, Model Router, Metadata Service
Benefits: Easy to scale, no session affinity required, fault tolerant
Implementation: Containerized with Kubernetes HPA

Stateful Services:

Model Serving, Cache Layer
Challenges: Session affinity, data consistency, recovery complexity
Solutions: Distributed cache with consistent hashing, model versioning

Session Affinity and Its Implications

No session affinity required for API Gateway and Model Router

Stateless services handle all requests independently
Load balancer distributes requests across instances

Session affinity required for model serving

Model inference results depend on model state (e.g., attention weights in transformers)
Solution: Use consistent hashing to route requests to same model instance
Risk: Increased cache misses and potential hotspots

Scaling Metrics and Triggers

CPU Utilization:

Target: 60-70% for optimal performance
Trigger: Auto-scaling when >85% CPU utilization

Memory Usage:

Target: 70% for model serving (GPU memory)
Trigger: Memory pressure monitoring and preemption

QPS Rate:

Target: 50K QPS for peak load
Trigger: Latency >200ms or QPS >55K

Latency:

Target: p99 < 200ms for inference requests
Trigger: Latency >500ms or p99 > 500ms

Resource Allocation Strategy

GPU Resources:

Dedicated GPU instances (NVIDIA A10, V100, or H100)
GPU memory allocation per model (2GB-32GB depending on model size)
Resource isolation using Kubernetes GPU scheduling

Memory Allocation:

50% of total memory for model loading
30% for cache and intermediate results
20% for system overhead

Performance Bottlenecks by Component

Model Loading:
- Solution: Pre-warm models, model versioning
- Impact: 50-100ms latency increase during warm-up
GPU Memory Contention:
- Solution: Model size-aware scheduling, GPU sharing
- Impact: 20-50% performance degradation
Network Latency:
- Solution: Edge computing, regional deployment
- Impact: 10-30ms additional latency
Cache Invalidation:
- Solution: Time-based TTL, cache warming strategies
- Impact: 5-15% cache miss rate increase

Auto-scaling Strategy

Horizontal Scaling:

Kubernetes HPA with QPS-based scaling
Target: 500-1000 QPS per instance for API services
Scale up when QPS > 80% of capacity

Vertical Scaling:

GPU instances with dedicated resources
Scale up when model memory usage >85%
Scale down when utilization <20%

Warm-up Strategy:

Pre-warm model instances during low traffic
Use cache warming for frequently accessed models
Implement model versioning to avoid hot reloading

Capacity Planning Considerations

Peak Load Estimation:

100M DAU with 50ms average latency
Peak QPS: 50K (10x average)
Expected traffic distribution: 80% model inference, 20% metadata

Storage Growth:

Model size: 10MB-5GB per model
Metadata storage: 1KB per user request
Annual growth: 500GB-2TB depending on model count

Network Bandwidth:

Average request size: 10KB
Peak bandwidth: 500MB/s (50K QPS * 10KB)
Consider CDN for model distribution

Cross-service Scaling Dependencies

Model Serving to Cache:

Cache warming affects model serving performance
Solution: Async cache warming, priority-based loading

API Gateway to Model Router:

Router must handle 50K QPS with <20ms latency
Solution: Load balancing, circuit breaker pattern

Database to Model Serving:

Metadata updates must be consistent with model versions
Solution: Event-driven architecture, eventual consistency model

Future Scalability Path

Multi-Model Support:
- Scale to support 1000+ models with different resource requirements
- Implement model bundling and grouping strategies
Cross-Region Scaling:
- Deploy services in multiple regions for global availability
- Use content delivery networks for model distribution
Edge Computing:
- Deploy lightweight models at edge locations
- Reduce latency for mobile and IoT clients
Quantization and Optimization:
- Implement model compression and quantization
- Reduce memory footprint and improve inference speed

Performance Target Validation

QPS Scaling:

50K QPS peak with 20ms p99 latency
Horizontal scaling to support 100K QPS with proper load distribution

Memory Efficiency:

GPU memory usage <85% to prevent out-of-memory errors
Model caching with proper eviction policies

Latency Targets:

p50: <50ms, p95: <100ms, p99: <200ms
Achieved through proper resource allocation and caching strategies

Throughput:

50K QPS with minimal queueing delays
Proper load balancing and service mesh integration

14. Infrastructure & Deployment Architecture

Cloud Provider and Region Selection

Primary Provider: AWS (with multi-region support) Regions: us-east-1, us-west-2, eu-west-1 Justification:

AWS provides mature AI/ML infrastructure with SageMaker, ECS, and Lambda
Multi-region support for high availability and low-latency global serving
Strong ecosystem integration with existing enterprise tooling
Cost optimization through reserved instances and spot pricing

Kubernetes Cluster Design

Cluster Architecture:

Control Plane: Managed AWS EKS with 3 control plane nodes across availability zones
Node Pools:
- CPU-optimized nodes (m5.xlarge) for general inference workloads
- GPU-optimized nodes (p3.2xlarge) for heavy ML model serving
- Memory-optimized nodes (r5.2xlarge) for models requiring large memory footprints
Namespace Strategy:
- production namespace for live inference services
- staging namespace for pre-production testing
- development namespace for feature development and experimentation

Resource Quota Model:

CPU quotas per namespace (500m-2000m for CPU-intensive services)
Memory quotas (1Gi-8Gi per pod)
GPU quotas (1-4 GPUs per pod for GPU-accelerated models)

Container Image Strategy

Base Images:

Ubuntu 20.04 LTS as base OS
Python 3.9+ for ML inference environments
NVIDIA CUDA 11.x for GPU-accelerated models

Multi-stage Builds:

Build stage: Full development environment with model training dependencies
Runtime stage: Minimal runtime image with only inference dependencies
Image Registry: AWS ECR with automated build triggers from GitHub

Deployment Strategy

Primary Approach: Blue-Green Deployment with Traffic Splitting

Blue Environment: Currently serving production traffic
Green Environment: New version deployed and tested in parallel
Traffic Management:
- Gradual traffic shift (10% → 50% → 100%) using AWS Application Load Balancer
- Health checks for automatic failover (3 consecutive failures = rollback trigger)
- Manual approval gates for production promotion

Auto-scaling Design:

HPA (Horizontal Pod Autoscaler): Scale based on CPU utilization (80% target) and custom metrics
VPA (Vertical Pod Autoscaler): Optimize memory and CPU requests based on historical usage
KEDA (Kubernetes Event-Driven Autoscaling): Scale based on queue depth for batch inference jobs

Infrastructure as Code Approach

IaC Tools:

Terraform: For infrastructure provisioning (VPC, subnets, security groups, EKS clusters)
Helm: For Kubernetes manifest management and service deployment
Terragrunt: For module organization and environment-specific configuration

Infrastructure Organization:

infrastructure/
├── environments/
│   ├── production/
│   ├── staging/
│   └── development/
├── modules/
│   ├── eks-cluster/
│   ├── vpc/
│   ├── security-groups/
│   └── monitoring/
└── main.tf

Version Control Strategy:

GitOps with ArgoCD for declarative deployment management
Tagged releases for infrastructure changes (v1.0.0, v1.1.0)
Pull request review process for all infrastructure changes

Security and Compliance:

IAM roles with least privilege principle for EKS nodes and services
VPC flow logs and CloudTrail integration for audit trails
Automated security scanning of container images using Trivy in CI pipeline

Resource Management:

Cluster autoscaler to dynamically adjust node count based on pod resource requirements
Resource limits and requests enforced at pod level to prevent resource contention
Pod disruption budgets (PDBs) to ensure availability during maintenance operations

Multi-tenancy Considerations:

Resource quotas and limits per namespace to prevent resource starvation
Network policies to restrict inter-service communication
Isolated logging and monitoring per tenant namespace

15. Security Architecture

VPC Design and Network Segmentation

The platform operates within a multi-AZ VPC architecture with strict network segmentation. Public subnets are isolated from private subnets using security groups and network ACLs. All AI model serving workloads run in private subnets with no direct internet exposure. The VPC includes dedicated subnets for:

Model inference services (private)
Model training and management (private)
Data processing pipelines (private)
Monitoring and observability (private)
API gateway and edge services (public)

Zero-Trust Network Model

All network traffic is subject to zero-trust principles. Services authenticate and authorize every request regardless of origin or network location. Network policies enforce strict egress/ingress controls using AWS Security Groups and VPC Flow Logs for audit trails. All inter-service communication is encrypted and authenticated using mTLS.

TLS Termination Strategy

TLS termination occurs at the API Gateway layer for external traffic, with strict certificate validation using ACM and custom CA for internal services. All service-to-service communication uses mutual TLS (mTLS) with certificate rotation every 90 days. Service mesh (Istio) handles mTLS termination and policy enforcement for internal service communication.

DDoS Protection Strategy

DDoS protection is implemented through AWS Shield Advanced with automatic traffic filtering. API Gateway is configured with rate limiting and request size limits to prevent abuse. CloudFront distribution is used as a first-layer DDoS mitigation for public endpoints, with automatic scaling to handle traffic spikes.

Secrets Management Architecture

Secrets are managed through AWS Secrets Manager with automatic rotation for credentials and API keys. Service accounts use IAM roles with least-privilege access rather than static credentials. Secrets are rotated every 90 days or upon compromise detection. Sensitive model parameters and keys are encrypted at rest using KMS.

Vulnerability Scanning in CI/CD

Container images undergo automated vulnerability scanning using Trivy and Snyk during the build pipeline. Security policies are enforced through Open Policy Agent (OPA) integration in CI/CD workflows. Any image with critical vulnerabilities blocks deployment to production environments.

WAF Rules for Critical Endpoints

AWS WAF is deployed at the API Gateway level with custom rules to block common attack patterns:

SQL injection and XSS protection
Rate limiting for inference endpoints (1000 RPS per client)
IP address blocking for known malicious sources
Request size limiting (10MB max payload)
Bot detection and mitigation for public endpoints

Access Control and Identity Management

Authentication is handled through AWS Cognito with federated identity providers (Google, GitHub). Service-to-service authentication uses IAM roles and service accounts with proper trust policies. RBAC is implemented at the application level for administrative access to model management and deployment features.

Data Protection and Encryption

All data at rest is encrypted using AWS KMS with customer-managed keys. Model artifacts and inference results are encrypted in storage and during processing. Data in transit is protected using TLS 1.3 with strong cipher suites. Model versioning and metadata are stored in encrypted format.

Audit and Compliance

Comprehensive audit logging is enabled for all model access, deployment, and inference operations. CloudTrail logs are aggregated to a centralized logging system for compliance reporting. All access is logged with timestamps, user identity, and action details for forensic analysis.

Security Monitoring and Alerting

Security events are monitored through AWS Security Hub with automated alerts to SNS topics. Critical security incidents trigger immediate notifications to the security team via PagerDuty. Security dashboards provide real-time visibility into threat detection and compliance status.

Incident Response Framework

A security incident response plan is established with defined escalation paths. All security incidents are classified by severity and followed through a structured response process including containment, eradication, and recovery phases. Regular security drills ensure team readiness for incident response scenarios.

Compliance Framework

The platform adheres to SOC2 Type II, HIPAA, and GDPR compliance requirements. Regular third-party security audits are conducted to validate compliance status. Data residency policies ensure model artifacts and inference data remain within specified geographic boundaries.

16. Deployment & Release Strategy

Deployment Strategy

The AI Inference Serving Platform will utilize a canary deployment strategy with progressive traffic shifts to minimize risk during releases. The deployment pipeline will follow a blue-green approach for major version changes, with the ability to quickly rollback to the previous stable version.

Traffic management will be handled through Istio-based traffic routing with weighted distribution. The deployment process includes:

10% initial traffic to new version
Gradual increase to 50% after 15 minutes
Full traffic shift to new version after 30 minutes of successful operation
Automatic rollback if error rate exceeds 1% threshold

Artifact Promotion Model

The release pipeline follows a trunk-based development model with continuous integration. Artifacts flow through the following stages:

Dev Environment:
- Commit-based automated testing
- Unit and integration tests (95%+ coverage)
- Security scanning with Trivy/Snyk
Staging Environment:
- Performance and load testing
- Feature flag validation
- Security compliance checks (PCI, SOC2)
- Automated canary deployment with 5% traffic
Production:
- Manual approval gate for critical model updates
- Zero-downtime deployment using rolling updates with health checks
- Feature flag-based gradual rollout to 100% of traffic

Zero-Downtime Database Migration Strategy

For model metadata and configuration changes, we implement schema-less migration using:

Versioned configuration files stored in GitOps repositories
Feature flags to control model version usage during migration
Database schema evolution with backward compatibility maintained via soft deletes and versioned fields
Rolling migration where new model versions can coexist with older ones until full transition

Feature Flag Integration and Progressive Rollout Design

Feature flags will be implemented using LaunchDarkly or Flagship for centralized configuration management. The progressive rollout strategy includes:

Gradual rollouts: 5% → 25% → 50% → 75% → 100% traffic
Segment-based targeting: By user type, region, or model preference
Real-time monitoring: Error rate and latency thresholds trigger automatic rollbacks
A/B testing capability: For comparing model performance and user experience

Canary Analysis Automation and Auto-Rollback Triggers

Automated canary analysis includes:

Error rate monitoring: >1% error rate triggers rollback within 5 minutes
Latency thresholds: p99 latency > 200ms for >5 consecutive minutes
Throughput validation: 10% drop in QPS triggers rollback
Model accuracy monitoring: Accuracy degradation >2% triggers rollback

Release Cadence and Branching Strategy

The platform follows a trunk-based development model with:

Daily builds for stable features
Weekly major releases for breaking changes or significant model updates
Hotfix branches for critical production issues (merged to trunk and hotfix branch simultaneously)
Release trains with 2-week sprints for feature development cycles
GitOps approach using ArgoCD or FluxCD for declarative infrastructure and deployment management

The branching strategy ensures:

Main branch always represents production-ready code
Feature branches for experimental models or features
Release branches for stable version maintenance and patching
Automated testing at each merge to trunk with comprehensive validation suite

17. Cost Architecture

Major Cost Drivers Analysis

Compute Costs (40-50% of total):

GPU/TPU instances for model inference (NVIDIA A10, H100, or T4s)
CPU instances for orchestration and management services
Container runtime overhead (Kubernetes nodes, Fargate)

Storage Costs (20-30% of total):

Model artifacts and checkpoints stored in object storage (S3/GCS)
Temporary inference results and logs
Cache layers (Redis, Memcached)

Network & Data Transfer Costs (15-25% of total):

Inter-service communication over VPC
Egress from edge services to client applications
Cross-region data replication (if multi-region)

Database & Caching Costs (5-10% of total):

Relational DBs for metadata and service configuration
Distributed cache systems (Redis, DynamoDB)
Event streaming infrastructure (Kafka, SQS)

Cost Optimization Strategy

Reserved vs. Spot Instance Strategy:

60% reserved instances for stable baseline workloads (orchestration, API services)
30% spot instances for burst inference workloads to reduce compute costs by up to 70%
10% on-demand instances for critical services requiring guaranteed availability

Storage Tiering Strategy:

Hot storage (SSD) for frequently accessed model versions and active caches
Warm storage (standard SSD) for recent model checkpoints and intermediate results
Cold storage (S3 Standard-IA or GCS Nearline) for historical model versions and backups
Archive storage (S3 Glacier or GCS Coldline) for compliance-retained models

Instance Sizing and Auto-scaling:

Use smaller instance types (e.g., p3.2xlarge vs p3.8xlarge) where possible to reduce cost per inference
Implement HPA with custom metrics (latency, queue depth) to optimize resource allocation
Use KEDA for event-driven scaling of inference services

Data Egress Cost Minimization

Edge Caching and CDN Strategy:

Deploy model serving endpoints at edge locations to minimize data egress costs
Use Cloudflare or AWS CloudFront for global content delivery of model metadata and small artifacts
Implement regional caching to reduce cross-region data transfer costs

Cross-Region Replication Optimization:

Use asynchronous replication only for disaster recovery and compliance
Implement read-only replicas in regions with high demand to reduce latency and egress costs

FinOps Dashboards and Budget Alerts

Cost Allocation and Tagging Strategy:

Implement service-level tagging (e.g., "service:inference-api", "team:ml-platform")
Use cost allocation tags to attribute costs to specific teams or projects
Tag all resources with environment (dev/staging/prod) and owner information

Real-time Cost Monitoring:

Set up Prometheus-based cost monitoring dashboard with Grafana
Configure alerts for cost anomalies (e.g., >20% increase in compute costs over 24h)
Implement budget alerts with Slack/PagerDuty integration for each team's cost allowance
Use AWS Cost Explorer or GCP Cost Management to analyze spending trends and forecast

Optimization Automation:

Implement auto-stop policies for dev/staging environments after hours
Schedule batch jobs during off-peak hours to reduce compute costs
Use cost optimization tools like AWS Cost Explorer or GCP BigQuery for predictive analysis of spending patterns

This cost architecture ensures that the AI inference platform remains financially sustainable while maintaining performance and scalability targets. The strategy balances cost optimization with reliability requirements, particularly for production inference workloads where performance is critical.

18. Open Questions, Risks & Trade-offs

Decisions requiring product / engineering alignment

Model versioning strategy: Should we version models at the service level or at the inference request level? This impacts backward compatibility and rollout complexity.
Multi-model support: Should we support serving multiple models from a single endpoint or require separate endpoints per model? This affects API design and resource allocation.
Real-time vs. batch inference: How much of the system should support real-time inference versus batch processing, and how do we handle mixed workloads?

Key Architectural Risks

Risk	Impact	Likelihood	Mitigation
Model drift detection failure	High - Production models may serve incorrect results	Medium - Models trained on historical data may not generalize to new data	Implement model monitoring with drift detection and automated retraining triggers
GPU memory fragmentation	High - Inference performance degradation over time	Medium - Memory allocation patterns may cause fragmentation	Implement memory pool management and periodic model eviction
Inference latency spikes	High - User experience degradation	High - Resource contention under peak load	Implement adaptive batching and request prioritization
Model availability outage	High - Complete service unavailability	Medium - Single point of failure in model loading	Design resilient model loading with fallback and caching strategies
Data pipeline corruption	High - Invalid model artifacts or inputs	Low - Manual or automated pipeline errors	Implement robust validation and checksum verification at every stage

Trade-offs made with justification

Trade-off	Decision	Justification
Consistency vs. Availability	Eventual consistency for model metadata and inference results	For AI serving, immediate consistency isn't critical for most use cases; eventual consistency allows better availability and scalability
Single region vs. Multi-region	Single region deployment with read replicas	Reduces complexity and cost for MVP; multi-region can be added later based on global demand
In-memory vs. disk-based model loading	Hybrid approach with L1 cache and disk fallback	Reduces cold start times while maintaining cost efficiency
Synchronous vs. asynchronous inference	Primarily synchronous with async batch support	Provides better UX for real-time applications while supporting batch workloads
Monolithic vs. microservices	Microservices with shared model registry	Enables independent scaling and deployment while maintaining clear service boundaries

Alternatives considered and rejected

Alternative	Reason for rejection
Custom ML framework	Would require extensive development and maintenance effort with no clear competitive advantage over established frameworks
Serverless inference	Not suitable for low-latency, high-throughput AI serving workloads; unpredictable cold starts
Traditional relational databases	Inefficient for storing and querying model artifacts and metadata; not optimized for AI workloads
Elasticsearch for model storage	Not designed for binary model artifacts; poor performance with large binary data
Kafka-only event processing	Lacks the flexibility needed for complex model lifecycle management and real-time inference orchestration

Technical debt acknowledged

Model metadata schema evolution: Current design assumes stable model metadata schemas, but as the platform grows, versioning and migration strategies will need to be implemented.
Inference result caching: Initial implementation uses simple in-memory caching without proper invalidation logic, which may lead to stale results under model updates.
Service-to-service communication: Using gRPC for all inter-service communication introduces tight coupling that may be harder to maintain as the system scales.
Model artifact storage: Currently uses a simple file-based approach that lacks proper lifecycle management and versioning controls.
Monitoring coverage: Initial observability setup is basic and may not capture all edge cases in model performance or inference behavior.

19. Phased Implementation Roadmap

Phase 1: MVP (Minimum Viable Platform)

Timeline: 8-12 weeks
Core Focus: Build foundational capabilities for model serving and basic orchestration

Deliverables:

Basic model deployment pipeline (upload → register → serve)
Core inference service with support for 2-3 common model formats (TensorFlow, PyTorch, ONNX)
Simple load balancing and health monitoring
Basic model versioning and rollback capability
REST API gateway for inference requests
Single-region deployment with 2-3 availability zones

Dependencies:

Kubernetes cluster provisioned
Container registry and CI/CD pipeline established
Basic monitoring stack (Prometheus, Grafana) deployed

Phase 2: Scale & Reliability

Timeline: 12-16 weeks
Core Focus: Enhance scalability, reliability, and multi-model support

Deliverables:

Multi-model support with automatic model selection based on input characteristics
Advanced load balancing and auto-scaling (HPA, VPA) with GPU-aware scheduling
Multi-region deployment capability with failover support
Enhanced caching layer (Redis + in-memory) for inference results
Distributed tracing and detailed observability across services
Advanced monitoring with alerting and capacity planning tools

Dependencies:

GPU node pools configured in Kubernetes
Advanced monitoring stack fully operational
Multi-region VPC and networking infrastructure established

Phase 3: Excellence & Optimization

Timeline: 16-24 weeks
Core Focus: Production-grade reliability, cost optimization, and advanced features

Deliverables:

Advanced model optimization (model quantization, pruning, compression)
Feature flagging and progressive rollout capabilities
Advanced security features (mTLS, secrets management, DDoS protection)
Cost optimization with spot instances and reserved capacity strategies
Advanced analytics dashboard for model performance and usage trends
Comprehensive disaster recovery and chaos engineering practices
Automated model retraining pipeline with drift detection

Dependencies:

Advanced CI/CD with automated testing and security scanning
FinOps tools for cost monitoring and optimization
Comprehensive security audit and compliance validation completed

Critical Path Dependencies:

Kubernetes Cluster Setup (Week 1-2) - Must be completed before any service deployment
Container Registry & CI/CD Pipeline (Week 2-3) - Enables automated deployments
Monitoring & Observability Stack (Week 3-4) - Essential for production monitoring
GPU Node Pool Configuration (Week 4-5) - Required for model inference services
Multi-region VPC Setup (Week 5-6) - Enables global deployment capability

Milestones:

Milestone 1 (Week 12): MVP functional and tested in staging
Milestone 2 (Week 20): Scale capabilities deployed and validated
Milestone 3 (Week 28): Production-grade platform with all advanced features operational

Risk Mitigation:

Technical Debt: Address through dedicated refactoring sprints in each phase
Performance Bottlenecks: Implement continuous load testing and capacity planning
Security Gaps: Conduct security reviews at each phase boundary
Cost Overruns: Monitor FinOps dashboards and implement cost optimization triggers

The phased approach ensures rapid delivery of core capabilities while building the foundation for enterprise-grade AI inference serving with proper scalability, reliability, and operational excellence.