vLLM vs BentoML
Both are alternatives to Modal. Here's how they stack up — verified facts, no spin.
Also searched as BentoML vs vLLM — same comparison, one verdict.
vLLM and BentoML are closely matched on ownership (95 vs 90) — this one comes down to pricing and to which trade-offs below you can live with.
vLLM
TOP PICKThe standard open inference engine — the thing under most serving platforms.
vLLM is the high-throughput LLM inference engine that effectively set the category standard, and its PagedAttention memory management is why it serves far more concurrent requests per GPU than a naive implementation. It exposes an OpenAI-compatible API, so an application already talking to OpenAI can be pointed at a vLLM endpoint by changing a base URL. It is Apache-2.0 and now sits under the PyTorch Foundation rather than a single company. The important thing to understand about the whole category: a large share of the managed platforms you might pay for are running vLLM underneath, so choosing it directly is not a downgrade from the commercial option — it is the commercial option without the margin.
BentoML
Package any model as a container and deploy it wherever you like.
BentoML is the packaging and serving framework around the engine: you define a service in Python, it builds an OCI image with the model, dependencies and API baked in, and that image runs on your Kubernetes cluster, a VM, or a managed platform without change. It works with vLLM as a backend, so you get vLLM's throughput plus a deployment story. Of everything here it is closest in spirit to what Modal does — decorated Python that becomes a running endpoint — with the difference that the artefact is a standard container you own rather than a platform you rent.
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 | BentoML | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 95 | 90 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first data | Yes | Yes |
| License | Apache-2.0 | Apache-2.0 |
| Pricing | Free and open source. You pay only for the GPUs you rent or own. | Free and open source. BentoCloud is an optional paid hosted tier. |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | 24 GB VRAM minimum for useful production serving | 4 GB plus model memory |
| 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 | $40–200/mo depending on hardware, against per-second serverless inference billing |
| Setup timeHonest first-install estimate, not the marketing quickstart. | A day including CUDA | Half a day |
| Ongoing maintenanceThe part nobody budgets for. | Moderate. CUDA and driver versions are the recurring pain, not vLLM itself. | Moderate. You own the serving infrastructure it produces. |
vLLM is Macrostack's recommended Modal alternative, so it's our pick here.
vLLM
Strengths
- +Highest throughput per GPU in general open benchmarks — PagedAttention is the reason
- +OpenAI-compatible API: swap a base URL, keep the application
- +Apache-2.0 under the PyTorch Foundation, not a single vendor
- +Runs the same on a rented H100, your own box, or a Kubernetes cluster
Trade-offs
- −You provide the GPU, the autoscaling and the uptime
- −No scale-to-zero — an idle GPU still costs whatever you rent it for
- −Tuning memory and batching well takes real understanding
- −Focused on text models; multimodal support lags the frontier
BentoML
Strengths
- +Output is a standard OCI container — deploy anywhere, no lock-in by design
- +Uses vLLM as an engine, so throughput does not suffer for the convenience
- +Handles batching, multi-model composition and adaptive request grouping
- +Familiar Python service definition, close to Modal's developer experience
Trade-offs
- −You still need somewhere to run the container and something to scale it
- −Another abstraction layer to learn on top of the engine
- −Smaller community than vLLM or Ray
- −The hosted tier is where the operational convenience actually lives
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 in general open benchmarks — PagedAttention is the reason.
Choose BentoML
if output is a standard OCI container — deploy anywhere, no lock-in by design.
Neither, yet
if both carry a real cost you should weigh first — you provide the GPU, the autoscaling and the uptime, and you still need somewhere to run the container and something to scale it. 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 BentoML
4 GB plus model memory RAM · Half a day
$40–200/mo depending on hardware, against per-second serverless inference billing
vLLM vs BentoML — common questions
Is vLLM a better fit than BentoML for model serving & inference?
It depends on what you are optimising for, and the honest split is this: vLLM scores 95 to BentoML's 90 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. BentoML earns its place on a different axis — output is a standard OCI container — deploy anywhere, no lock-in by design. 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. BentoML 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 BentoML?
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 BentoML both alternatives to Modal?
Yes — both appear in our Modal comparison, which is why they are worth putting side by side. People usually arrive here already having decided to move off Modal and now choosing between the two replacements, which is a narrower and much easier question.
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Facts verified 2026-07-30. Licenses and pricing change — spotted something out of date? That's a correction we want.