vLLM alternatives
5 substitutes · 5 in inference · 0 adjacent
The short answer
5 tools on this index are recorded as a substitute for vLLM. 5 of these run the same way you already deploy vLLM (self-hosted), and 5 are permissively licensed, so they do not change what you can ship. All of them sit in Inference & Serving, so the choice is a real one rather than a category change. There is no overall winner here: which one is right depends on traffic shape, operational budget and how much of vLLM you have already built around.
By Lattice editorial · Comparison reviewed · method
Better than this page
- vLLM vs SGLang vs TGI vs llama.cppThe four open inference runtimes compared on prefix reuse, hardware floor, structured output and operational complexity — with a recommendation for each traffic shape.
- Self-hosting vs routing vs measuring: cutting inference costThree ways to lower an inference bill — run the model yourself, send easy queries to a smaller model, or measure where spend goes — compared on savings and risk.
Real substitutes
in Inference & Serving- SGLangruntime · Apache-2.0
Structured generation runtime built on RadixAttention prefix reuse.
Use when A large share of requests share a long prefix, or you need constrained decoding.
Skip when You want the fewest moving parts and vLLM already clears your bar.
- TensorRT-LLMruntime · Apache-2.0
NVIDIA-optimized inference with inflight batching and FP8.
Use when You are committed to NVIDIA hardware and need the last of the throughput.
Skip when Hardware portability matters, or you have no GPUs to tune against.
- Text Generation Inferenceruntime · Apache-2.0
Hugging Face production server for LLMs with tensor parallelism.
Use when Multi-GPU tensor parallelism inside an existing Hugging Face stack.
Skip when The model fits on one GPU, where this is overkill.
- Triton Inference Serverruntime · BSD-3-Clause
Multi-framework inference server for ONNX, TensorRT and Python.
Use when One endpoint serving ONNX, TensorRT and Python models side by side.
Skip when You serve one model family and want peak LLM throughput.
- PowerInferruntime · Apache-2.0
CPU-first runtime that offloads hot paths to the GPU.
Use when CPU-first serving where the GPU accelerates rather than carries the load.
Skip when You have GPUs to spare — it trades peak throughput for reach.
This graph is bidirectional and unsponsored. A tool appears here if vLLM or another entry names it as a substitute, and SGLang, TensorRT-LLM, Text Generation Inference, Triton Inference Server and PowerInfer point at vLLM as well. How this index is compiled — including where it is wrong.