SGLang
Structured generation and prefix caching — the fast one for complex prompts.
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sovereigntySGLang 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.
OPEN SOURCEApache-2.0SELF-HOSTLOCAL-FIRST
LicenseApache-2.0
PricingFree and unlimited; hardware costs are yours.
Open sourceYes
Self-hostableYes
Local-first dataYes
What it does well
- +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
Where it falls short
- −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
SGLang as an alternative to
Where SGLang shows up in our comparisons, and how it ranked.
SGLang head-to-head
Straight comparisons against the tools people weigh it against.