I'm building an AI app and need a serving stack that won't break under traffic

If you want a serving stack that stays reliable under traffic, design for simple, stateless, autoscaled, and observable. A good default is:

Recommended serving stack

• API layer: FastAPI or gRPC
• Inference server:
  • vLLM for LLMs
  • Triton Inference Server for multi-model or non-LLM workloads
  • Text Generation Inference (TGI) if you want a production-ready LLM server
• Orchestration: Kubernetes
• Autoscaling: HPA + custom metrics, or KEDA if queue-based
• Queueing: Redis / RabbitMQ / Kafka depending on latency needs
• Caching: Redis for prompts, embeddings, and repeated responses
• Observability: Prometheus + Grafana + OpenTelemetry + Loki
• Load balancing: NGINX, Envoy, or cloud LB
• Storage: object store for model artifacts, Postgres for app state

What usually keeps systems from breaking

1. Keep model servers stateless

   • No session state on the server
   • Store conversation/session data externally

2. Use batching where it helps

   • Dynamic batching can dramatically improve throughput
   • Especially important for GPU-bound inference

3. Protect the model with backpressure

   • Put limits on concurrent requests
   • Reject or queue excess load instead of letting everything timeout

4. Set strict timeouts and retries

   • Short client timeouts
   • Limited retries with jitter
   • Don’t retry expensive inference blindly

5. Autoscale on the right signals

   • GPU utilization alone is not enough
   • Scale on queue depth, request latency, and tokens/sec

6. Cache aggressively

   • Embeddings
   • Common prompts
   • Retrieval results
   • Final answers when appropriate

7. Separate critical paths

   • Sync path for low-latency inference
   • Async path for slow tasks like document ingestion or batch generation

A robust architecture pattern

Client → API Gateway → Request Queue / Router → Model Workers → Cache / DB → Observability

This gives you:

• controlled concurrency
• graceful degradation
• better scaling
• fewer cascading failures

If you’re serving LLMs specifically

A strong production setup is:

• FastAPI at the edge
• vLLM behind it
• Redis for cache + rate limiting
• Kubernetes with GPU node pools
• Prometheus/Grafana for metrics
• OpenTelemetry for traces
• Cloud load balancer in front

Operational guardrails

• Circuit breakers
• Rate limiting per tenant/user
• Dead-letter queues for failed jobs
• Health checks: liveness, readiness, startup
• Canary deploys and rollbacks
• Model versioning and feature flags

If you want the safest “starter” stack

If I had to pick one practical stack for traffic resilience:

• FastAPI
• vLLM
• Redis
• Postgres
• Kubernetes
• Prometheus/Grafana
• OpenTelemetry
• Cloud load balancer

If you want, I can also give you:

1. a minimal production architecture for your use case,
2. a Kubernetes deployment template, or
3. a comparison of vLLM vs Triton vs TGI.