vLLM vs RunPod Serverless
Both are alternatives to Modal. Here's how they stack up — verified facts, no spin.
Also searched as RunPod Serverless vs vLLM — same comparison, one verdict.
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.
RunPod Serverless
Scale-to-zero like Modal, at close to raw GPU rental prices.
If what you actually want from Modal is scale-to-zero rather than the programming model, RunPod Serverless offers the same shape at rates much closer to raw rental — H100 capacity around $1.99/hr against Modal's $3.95/hr. You supply a container with a handler rather than decorating your own source, which is slightly more setup and considerably less entanglement: the artefact is a standard image, so moving it elsewhere is a redeploy rather than a rewrite. It is the pragmatic middle of this comparison, and it pays 10% of referred spend through PartnerStack, which is disclosed here because we link to it.
Side by side
| vLLM | RunPod Serverless | |
|---|---|---|
| Sovereignty Score | 95 | 56 |
| Open source | Yes | No |
| Self-hostable | Yes | No |
| Local-first | Yes | No |
| License | Apache-2.0 | Proprietary (hosted service) |
| Pricing | Free and open source. You pay only for the GPUs you rent or own. | Per-second billing with scale-to-zero. H100 around $1.99/hr; no commitment. Rates observed 2026-07-30. |
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
RunPod Serverless
Strengths
- +Roughly half Modal's GPU rate for the same serverless behaviour
- +Scale-to-zero, so idle endpoints cost nothing
- +You ship a normal container — the artefact stays portable
- +Same account also rents persistent GPUs for training or interactive work
Trade-offs
- −Still a proprietary hosted platform — you do not own the endpoint
- −Developer experience is rougher than Modal's decorators
- −Cold starts on large models are a real latency cost
- −Enterprise compliance story is thin next to the hyperscalers
Related alternative guides
Facts verified 2026-07-30. Licenses and pricing change — spotted something out of date? That's a correction we want.