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 and SGLang are closely matched on ownership (95 vs 91) — this one comes down to pricing and to which trade-offs below you can live with.
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
10 points of comparison, every one read from a verified field. Green marks the side that wins a row outright. A dash means we do not hold that fact — never that it is zero.
| Ollama | SGLang | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 95 | 91 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first data | Yes | Yes |
| License | MIT | Apache-2.0 |
| Pricing | Free. Runs on hardware you already have. | Free and unlimited; hardware costs are yours. |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | 8 GB VRAM for a 7B model at usable speed; 24 GB for 30B-class | 24 GB VRAM for useful production serving |
| Realistic running costWhat the box costs each month if you run it yourself. | $0 on hardware you own. $150–400/mo for a rented 24 GB GPU, against per-token API billing that is cheaper below roughly 2M tokens a month. | $300–900/mo for a rented A100 or L40S |
| Setup timeHonest first-install estimate, not the marketing quickstart. | 10 minutes | A day including CUDA |
| Ongoing maintenanceThe part nobody budgets for. | Very low. Model updates are a pull. | Moderate. CUDA and driver versions are the recurring pain, not SGLang itself. |
Ollama edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Weighing both against staying on Replicate? Is Replicate free? What it actually costs →
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
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose Ollama
if a lower exit cost matters more to you than any single feature, and genuinely one command from nothing to a served model.
Choose SGLang
if prefix caching is a genuine multiple on agent and chat workloads.
Neither, yet
if both carry a real cost you should weigh first — lower throughput than vLLM under concurrent load, and younger project with a smaller operational community. If either of those is a dealbreaker for your team, the shortlist is wrong rather than the choice.
What it takes to run these yourself
Real requirements and honest running costs, not the vendor quickstart.
Self-hosting Ollama
8 GB VRAM for a 7B model at usable speed; 24 GB for 30B-class RAM · 10 minutes
$0 on hardware you own. $150–400/mo for a rented 24 GB GPU, against per-token API billing that is cheaper below roughly 2M tokens a month.
Self-hosting SGLang
24 GB VRAM for useful production serving RAM · A day including CUDA
$300–900/mo for a rented A100 or L40S
Ollama vs SGLang — common questions
Is Ollama a better fit than SGLang for model serving & inference?
It depends on what you are optimising for, and the honest split is this: Ollama scores 95 to SGLang's 91 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. SGLang earns its place on a different axis — prefix caching is a genuine multiple on agent and chat workloads. Neither is a wrong answer for every team; the table above is the actual comparison.
What happens if we want to switch later?
Ollama keeps its data local or in open formats, so leaving is an export rather than a negotiation. SGLang is still self-hostable, so the files stay on your server either way — but it is not local-first by design, so check what its export produces before you rely on it.
Can I self-host Ollama or SGLang?
Both can be self-hosted. The difference is what it costs you in time rather than whether it is possible — see the setup and maintenance rows above.
Are Ollama and SGLang both alternatives to Replicate?
Yes — both appear in our Replicate comparison, which is why they are worth putting side by side. People usually arrive here already having decided to move off Replicate and now choosing between the two replacements, which is a narrower and much easier question.
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Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.