I need a low-latency inference stack for customer-facing apps

A good low-latency inference stack for customer-facing apps usually means optimizing for time-to-first-token / response time, p95/p99 latency, availability, and cost under bursty traffic.

A practical stack

1) Model serving layer

Pick one of these based on your model type:

• vLLM: great default for LLMs, strong throughput and low latency via paged attention.
• TensorRT-LLM: best when you want maximum GPU performance and can invest in optimization.
• TGI (Text Generation Inference): solid production-grade LLM serving with batching and streaming.
• NVIDIA Triton: best for multi-model, non-LLM, or mixed workloads.

Recommendation:

• LLM-heavy apps: vLLM or TensorRT-LLM
• Mixed ML workloads: Triton
• Need fast time-to-market: vLLM

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2) API gateway / edge

Put a thin gateway in front for auth, rate limiting, and routing:

• Envoy
• Kong
• NGINX
• Cloudflare Workers / Fastly Compute for edge-adjacent logic

Use it for:

• request authentication
• tenant isolation
• rate limits
• request shaping
• canary routing / A-B tests

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3) Autoscaling + orchestration

• Kubernetes for standard production deployment
• KEDA if you want scale-to-zero or queue-based scaling
• Horizontal Pod Autoscaler for GPU pods if you can use utilization-based scaling
• Use node pools dedicated to GPUs

For latency-sensitive apps, avoid aggressive scale-to-zero unless traffic is highly spiky and cold-starts are acceptable.

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4) Caching

Caching is often the biggest latency win.

• Redis for:
  • prompt/response caching
  • session state
  • rate limits
• CDN/edge cache for static or semi-static responses
• Semantic cache for repeated user intents if appropriate

For LLMs, cache:

• exact prompt → response
• embeddings → nearest previous answer
• tool results / retrieval results

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5) Retrieval and data access

If your app uses RAG:

• Vector DB: Pinecone, Weaviate, Milvus, pgvector
• Search: Elasticsearch/OpenSearch for hybrid retrieval
• Keep retrieval local/close to inference servers to reduce round trips
• Precompute embeddings where possible

Low latency usually improves more from better retrieval design than from squeezing model inference alone.

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6) Observability

Must-have for production:

• Prometheus + Grafana
• OpenTelemetry
• structured logs
• tracing from edge → retrieval → model → response

Track:

• p50/p95/p99 latency
• token throughput
• queue wait time
• GPU memory usage
• cache hit rate
• error rate
• tail latency during bursts

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If this is for LLM customer apps

A strong baseline stack is:

• Cloudflare / Fastly at the edge
• Envoy or NGINX gateway
• Kubernetes
• vLLM on GPU nodes
• Redis for caching and rate limits
• Postgres + pgvector or a vector DB for RAG
• Prometheus/Grafana + OpenTelemetry

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Latency optimization checklist

To keep response times low:

• use smaller/faster models where possible
• quantize models if quality allows
• keep context windows short
• use streaming responses
• batch carefully; don’t overbatch latency-critical traffic
• pin workloads to warm GPU workers
• co-locate retrieval and inference
• use KV cache reuse if supported
• prewarm models on deploy
• reduce network hops

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A simple production architecture

Client → CDN / Edge → API Gateway → Auth / Rate Limit → Retrieval Cache / Redis → Vector Search / DB → Model Server (vLLM / Triton / TensorRT-LLM) → Response Streaming back to client

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Suggested choices by scenario

Fastest to deploy

• Cloudflare
• Kubernetes
• vLLM
• Redis
• pgvector

Highest performance

• Envoy
• Kubernetes with GPU node pools
• TensorRT-LLM
• Redis
• dedicated retrieval service

Mixed workloads

• NGINX/Envoy
• Triton
• Redis
• Postgres + pgvector
• Prometheus/Grafana

If you want, I can turn this into a reference architecture, a cloud-specific stack (AWS/GCP/Azure), or a cost-aware setup for a target latency like <200 ms or streaming under 1 second.