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 and SGLang are closely matched on ownership (93 vs 91) — this one comes down to pricing and to which trade-offs below you can live with.
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
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.
| vLLM | SGLang | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 93 | 91 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first data | 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. |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | 24 GB VRAM minimum for useful production serving | 24 GB VRAM for useful production serving |
| Realistic running costWhat the box costs each month if you run it yourself. | $300–900/mo for a rented A100 or L40S, against managed inference with a platform margin on every token | $300–900/mo for a rented A100 or L40S |
| Setup timeHonest first-install estimate, not the marketing quickstart. | A day including CUDA | A day including CUDA |
| Ongoing maintenanceThe part nobody budgets for. | Moderate. CUDA and driver versions are the recurring pain, not vLLM itself. | Moderate. CUDA and driver versions are the recurring pain, not SGLang itself. |
vLLM is Macrostack's recommended Replicate alternative, so it's our pick here.
Weighing both against staying on Replicate? Is Replicate free? What it actually costs →
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
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose vLLM
if a lower exit cost matters more to you than any single feature, and highest throughput per GPU of the mainstream engines.
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 — you own the GPU, the drivers and the on-call rota, 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 vLLM
24 GB VRAM minimum for useful production serving RAM · A day including CUDA
$300–900/mo for a rented A100 or L40S, against managed inference with a platform margin on every token
Self-hosting SGLang
24 GB VRAM for useful production serving RAM · A day including CUDA
$300–900/mo for a rented A100 or L40S
vLLM vs SGLang — common questions
Is vLLM a better fit than SGLang for model serving & inference?
It depends on what you are optimising for, and the honest split is this: vLLM scores 93 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?
vLLM 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 vLLM 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 vLLM 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.