vLLM vs SGLang
Both are alternatives to Replicate. Here's how they stack up — verified facts, no spin.
Also searched as SGLang vs vLLM — same comparison, one verdict.
vLLM
TOP PICKThe throughput king. OpenAI-compatible, Apache-2.0, built for serious serving.
vLLM is a high-throughput inference engine born out of UC Berkeley, built around PagedAttention — a memory-management technique borrowed from operating-system paging that lets it batch far more concurrent requests onto the same GPU than naive serving does. It exposes an OpenAI-compatible server, so most existing client code works by changing a base URL. It is the default choice for teams serving one model to real traffic, and the engine underneath a large share of the inference providers you would otherwise pay.
SGLang
Structured generation and prefix caching — the fast one for complex prompts.
SGLang is a serving framework designed around the observation that real LLM workloads are not single independent prompts — they are agents, multi-turn chats and structured extractions that share huge amounts of prefix. Its RadixAttention cache reuses that shared prefix across requests, which produces large speedups on exactly the workloads that cost the most. It also has strong constrained-decoding support, so JSON-schema output is enforced rather than hoped for. Same Apache-2.0 posture as vLLM, and an OpenAI-compatible server.
Side by side
| vLLM | SGLang | |
|---|---|---|
| Sovereignty Score | 93 | 91 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | Apache-2.0 |
| Pricing | Free and unlimited. You pay only for the hardware you run it on. | Free and unlimited; hardware costs are yours. |
vLLM is Macrostack's recommended Replicate alternative, so it's our pick here.
vLLM
Strengths
- +Highest throughput per GPU of the mainstream engines
- +OpenAI-compatible API — client code often needs no change
- +Apache-2.0 with no feature gates or usage limits
- +Continuous batching keeps the GPU busy under mixed load
Trade-offs
- −You own the GPU, the drivers and the on-call rota
- −No scale-to-zero — an idle GPU still costs whatever it costs
- −Setup assumes comfort with CUDA and Python environments
SGLang
Strengths
- +Prefix caching is a genuine multiple on agent and chat workloads
- +Constrained decoding makes structured JSON output reliable
- +OpenAI-compatible API, Apache-2.0, no gates
- +Competitive with or ahead of vLLM on several benchmark shapes
Trade-offs
- −Younger project with a smaller operational community
- −Advantage is workload-dependent — little gain on one-shot prompts
- −Documentation assumes more ML background than Ollama's
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Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.