If you're comparing vLLM and TGI, you've already made the bigger decision: you're self-hosting an open-weight model on your own GPUs. That decision is the one worth pausing on, because it's more consequential than the engine you pick underneath it.
We build production AI systems and route most LLM traffic through hosted APIs on purpose. Self-hosting inference is a real lever, but it's a lever you pull for a reason — scale economics, data residency, or a specific open model — not by default.
The prerequisite question: should you self-host at all
Running your own inference isn't just a GPU bill. It's autoscaling, GPU memory management, model loading, batching config, upgrades, and the page when a node OOMs at peak. Below real scale, a hosted API almost always beats the fully-loaded cost of doing this yourself, because the API price includes all the ops you'd otherwise staff.
Self-host when the math flips: sustained high volume where cost-per-token on owned GPUs undercuts the API, a data-residency requirement that forbids sending prompts out, or an open model you specifically need. Once you're there — and only then — the engine choice becomes real.
vLLM: throughput is the whole point
vLLM exists to pack more concurrent requests onto a GPU. Its core ideas — PagedAttention for memory-efficient KV-cache handling, and continuous batching so new requests join in-flight instead of waiting for a batch to drain — are aimed squarely at throughput and, therefore, cost-per-token. If the reason you're self-hosting is economics at scale, this is the alignment you want: the engine's design goal is your business goal.
The trade is that you're closer to the metal. You'll tune batching, watch GPU memory, and own more of the serving stack.
TGI: batteries included, ecosystem fit
TGI (Text Generation Inference) is Hugging Face's production server. It's batteries-included and slots cleanly into the Hugging Face ecosystem, which means a gentler on-ramp: less assembly to get a supported server answering requests. You may leave some peak throughput on the table relative to a fully-tuned vLLM deployment, but you gain an operational path that's easier to stand up and keep supported.
For teams whose priority is "get a reliable self-hosted endpoint running without becoming inference-engine experts," that trade is often the right one.
Don't trust the benchmark you read
Both projects move fast, and published throughput numbers go stale between releases. Worse, they don't transfer: results depend on your model, your prompt shape (long context vs short, streaming vs batch), and your specific GPUs. Any comparison — including this one — that hands you a tokens-per-second figure is lying by omission. Benchmark on your own workload before you commit. This is the same discipline as measuring quality with evals in CI: the only number that matters is the one from your system.
Which should you pick
Pick vLLM when throughput and cost-per-token on your own GPUs are the point and you can operate closer to the metal — the classic "we self-host because the scale economics demand it" case. Pick TGI when you want a supported, batteries-included server with an easy operational on-ramp and a clean fit with the Hugging Face ecosystem, and you'll accept slightly less peak throughput for that.
And keep the orchestration layer separate in your head: the engine serves tokens, but what schedules and retries the work around it is a different decision entirely.