Switching engines later can require API, deployment, and observability redesign. Weight this against reliability and operator burden, not speed alone.

Choose the simplest engine that satisfies your throughput and latency envelope. Benchmark with your real request mix before committing.

Pick with a 6-12 month horizon in mind. Early decisions should minimize migration risk while leaving room for scale and feature growth.

Continuous batching, efficient KV cache, and broad ecosystem adoption. Document tradeoffs explicitly so future migrations are easier.

You still need strong traffic shaping and runtime governance. Revisit engine fit when SLOs or traffic shape change materially.

vLLM typically shines when request volume and concurrency are substantial enough to benefit from advanced batching behavior. Weight this against reliability and operator burden, not speed alone.

Operational familiarity, mature deployment patterns, and enterprise-friendly workflows. Benchmark with your real request mix before committing.

Throughput characteristics differ by workload and tuning choices; benchmark before committing. Document tradeoffs explicitly so future migrations are easier.

TGI can reduce integration friction for teams already centered on Hugging Face model workflows and operational tooling. Revisit engine fit when SLOs or traffic shape change materially.

Excellent latency and throughput for tuned deployments on supported hardware. Weight this against reliability and operator burden, not speed alone.

Optimization workflows and platform constraints can increase complexity. Benchmark with your real request mix before committing.

TensorRT-LLM is strongest when your infrastructure, performance targets, and team expertise align closely with NVIDIA-first optimization paths. Document tradeoffs explicitly so future migrations are easier.

Useful for workflows needing specific decoding control or agent-like structured outputs. Revisit engine fit when SLOs or traffic shape change materially.

Feature maturity and ecosystem integration may vary by team requirements. Weight this against reliability and operator burden, not speed alone.

Choose specialized engines when a concrete workload requirement justifies the added complexity, not because feature lists look larger. Benchmark with your real request mix before committing.

Throughput, p95 latency, hardware fit, API compatibility, observability, and operator effort. Document tradeoffs explicitly so future migrations are easier.

Replay representative traffic and evaluate failure handling, not just happy-path benchmark speed. Revisit engine fit when SLOs or traffic shape change materially.

Weight reliability and operator effort alongside raw performance. Stable operations usually deliver more value than marginal benchmark gains.

Define SLOs, shortlist engines, benchmark in staging, run canary in production, then standardize runbooks. Weight this against reliability and operator burden, not speed alone.

Faster selection with fewer surprises and clearer tradeoff visibility. Benchmark with your real request mix before committing.

Reopen engine selection when traffic shape, model mix, or compliance requirements change materially. Engine choice is a living architecture decision.