Ollama vs SGLang
Both are alternatives to Replicate. Here's how they stack up — verified facts, no spin.
Also searched as SGLang vs Ollama — same comparison, one verdict.
Ollama
One command to a running model. The easiest way to stop paying per token.
Ollama packages local model serving into a single binary and a Docker-like command vocabulary: `ollama run llama3` downloads the weights and gives you a prompt. It exposes both its own REST API and an OpenAI-compatible endpoint, runs on macOS, Linux and Windows, and handles GPU acceleration automatically where it can. It is not the fastest engine under heavy concurrency and does not try to be — it is the one that gets a model serving in under five minutes.
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
| Ollama | SGLang | |
|---|---|---|
| Sovereignty Score | 95 | 91 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | Apache-2.0 |
| Pricing | Free. Runs on hardware you already have. | Free and unlimited; hardware costs are yours. |
Ollama edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Ollama
Strengths
- +Genuinely one command from nothing to a served model
- +OpenAI-compatible endpoint alongside its own API
- +Runs well on a laptop — no cloud account needed at all
- +MIT licensed, no telemetry required to use it
Trade-offs
- −Lower throughput than vLLM under concurrent load
- −Model library curated by Ollama — custom weights take extra steps
- −Not designed as a multi-tenant production serving layer
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
More model serving & inference comparisons
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.