AI Inference & Serving

Model serving, inference optimization, routing, caching, and scaling infrastructure.

Inference is the line item that decides whether AI products ship or die. Knowing batching, routing, and caching is the difference between a viable serving stack and a CFO conversation.

What you’ll be able to do

Deploy model serving infrastructure with low-latency guarantees
Optimize inference with batching, quantization, and caching
Build multi-model routing and A/B testing systems
Scale inference infrastructure with observability and cost control

Curriculum

Phase 1: Serving Foundations

Stand up a working serving API, deploy a model behind it, and see why a naive deploy costs 10× what an optimized one does — the floor every serving stack starts on.

Serving Foundations

Online vs batch inference, request/response lifecycle, stateless vs stateful serving, API gateway patterns — and the first serving API you'll build before any optimization matters.

Model Serving & Deployment

Hands-on with vLLM, API-based serving, containerization, REST vs gRPC endpoint design, and the model-versioning strategy that lets you ship without breaking clients.

Phase 2: Optimization & Routing

Batching, routing, and caching — the three levers that decide whether your serving stack is profitable. Get these wrong and inference becomes the line item that kills the AI roadmap.

Inference Optimization

Latency vs throughput tradeoffs, dynamic batching, token streaming, prompt optimization, and KV-cache internals — the four levers that drive 5–10× cost differences in production.

Routing & Multi-Model Serving

Model routing strategies, fast vs accurate model selection, fallback and retry mechanisms, A/B testing in production, and canary deployments without taking the SLA down.

Caching & Performance

Response caching fundamentals, embedding-cache design, invalidation strategies, Redis-layer architecture, and the semantic cache that quietly absorbs 30–60% of inference traffic.

Phase 3: Production Infrastructure

Scale, stream, and stay alive on-call. Autoscaling, streaming UX, and the observability you need before traffic finds the cracks in your serving platform.

Scaling Infrastructure

Autoscaling worker pools, load balancing for inference, queue-based serving, GPU vs CPU cost tradeoffs, and Ray Serve architecture for elastic multi-model platforms.

Streaming Inference

Server-Sent Events vs WebSockets, token-by-token response design, real-time UX considerations, and the backpressure and flow-control patterns chat surfaces always need.

Observability & Cost Management

Latency SLOs, distributed request tracing, per-request cost modeling, Grafana dashboards, and on-call runbooks — the observability stack that keeps inference alive past the launch high-five.

What you’ll build

vLLM-based serving API with dynamic batching and KV-cache tuning
Multi-model router with A/B testing, canary, and fallback paths
Semantic + response cache layer (Redis) wired to live traffic
SLO-grade observability stack (latency tracing + per-request cost)

Naive serving works at launch… and breaks the day traffic shows up.

Without the full stack, you risk:

GPU bills that 5× when usage doubles, not 2×
p99 latency that fails SLA even though p50 looks fine
One bad model rollout taking the whole serving fleet down
Inference costs that beat revenue and no one knows where it leaks

What is AI Inference & Serving?

AI inference serving is the infrastructure that deploys and runs ML models in production to serve predictions at scale. It covers model serving frameworks, inference optimization (batching, quantization, caching), multi-model routing, and scaling infrastructure. Used by companies like OpenAI, Anthropic, and Netflix to serve billions of predictions daily.

Why this matters in production

Inference costs dominate AI infrastructure spend. At Netflix, inference serving handles millions of recommendation requests per second with strict latency requirements. Production serving requires optimization that can reduce costs by 10x — proper batching, caching, and quantization are the difference between viable and unaffordable AI.

Common use cases

Deploying ML and LLM models with low-latency serving infrastructure
Optimizing inference with dynamic batching and model quantization
Building multi-model routing for A/B testing and canary deployments
Implementing inference caching to reduce compute costs and latency
Scaling GPU infrastructure for high-throughput AI workloads
Monitoring inference performance, costs, and model health

AI vs alternatives

AI vs vLLM

vLLM is a high-performance LLM serving engine. AI inference serving covers the broader infrastructure including routing, caching, and scaling. vLLM is one component of a production serving stack.

AI vs API Providers

Self-hosted inference offers lower costs at scale and data privacy. API providers (OpenAI, Anthropic) offer simplicity and rapid iteration. Most teams start with APIs and self-host for cost optimization.

AI vs Batch Inference

Real-time serving handles individual requests with low latency. Batch inference processes large volumes offline. Both are needed — real-time for user-facing features, batch for analytics and preprocessing.

Related skills

MLOps — Inference serving is part of the ML production lifecycle in
LLM Pipeline Engineering — LLM pipelines rely on serving infrastructure from
Cost Optimization — Inference cost management builds on cloud cost skills from

Why this skill matters

Inference serving is the operations spine of every production AI system. This skill puts you in the room where the GPU bill, the latency SLA, and the launch deadline all collide.

Common questions about AI

What is AI inference serving?

Inference serving deploys ML models to handle prediction requests in production. It covers model loading, request handling, batching, caching, and scaling to meet latency and throughput requirements.

Why is inference optimization important?

Inference compute is the largest cost in AI infrastructure. Optimization through batching, quantization, and caching can reduce costs by 5-10x while maintaining quality and latency targets.

How long does it take to learn inference serving?

Basic model deployment takes 1-2 weeks. Production optimization with batching, quantization, routing, and cost management takes 6-8 weeks of hands-on practice.

What tools are used for inference serving?

vLLM and TGI for LLM serving, TorchServe and Triton for general ML, Kubernetes for orchestration, and custom routing layers for multi-model serving. Most teams combine several tools.

Should I self-host or use API providers?

Use APIs for prototyping and low-volume workloads. Self-host for high-volume production, cost optimization, and data privacy requirements. The crossover point depends on your scale and latency needs.