vLLM
Fast, memory-optimized inference platform for serving large language models
- Also known as
- vllm
Available worldwide
What is vLLM?
An inference and serving system designed for high-throughput LLM deployment, supporting multiple hardware backends and distributed serving across GPU clusters.
vLLM pricing
We don't have vLLM's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
What vLLM does
The capabilities that matter for mlops & llmops tools, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Tool role
- Model serving
- Self-hostable / OSS core
- ✓
- Managed cloud available
- ✗
- On-prem / VPC deployment
- ✓
- LLM tracing / observability
- ✓
- Evaluation (offline / LLM-judge / human)
- -
- Prompt management + versioning
- -
- Experiment tracking / model registry
- -
- Model serving / inference endpoint
- ✓
- OpenTelemetry / OpenLLMetry compatible
- ✓
- Framework-agnostic
- ✓
- Multi-provider model support
- ✓
- No-train-on-customer-data guarantee
- -
Platform & deployment
Independently observed- CLI
- macOS
- Linux
- Cloud / SaaS
- On-premise
- Self-hosted
Integrations (32)
Independently observed- Hugging Face
- NVIDIA Dynamo
- OpenAI-compatible API
- Anthropic Messages API
- FlashAttention
- FlashInfer
- CUTLASS
- torch.compile
- gRPC
- GPTQ
- AWQ
- GGUF
- ModelOpt
- TorchAO
- OpenAI API
- Kubernetes
- PyTorch
- Ray
- OpenTelemetry
- Prometheus
- FastAPI
- Transformers
- Outlines
- Google Cloud TPU
- Intel Gaudi
- AMD Instinct
- Apple Silicon
- IBM Spyre
- Huawei Ascend
- Rebellions NPU
- TRTLLM-GEN
- CuTeDSL
Security & compliance
Known vulnerabilities: 70 (45 in the last 12 months), max severity CRITICAL sourcea count reflects scale & disclosure, not quality
vLLM FAQ
Common questions about vLLM, answered from independent, dated evidence.
What is vLLM?
An open-source framework that provides optimized LLM inference with low latency and high throughput. It includes continuous batching, memory-efficient attention mechanisms, quantization support, and distributed serving across diverse hardware platforms. It is indexed under MLOps & LLMOps Tools.
Source: https://docs.vllm.ai
Is vLLM free to use?
vLLM is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.
Source: https://docs.vllm.ai
What platforms does vLLM support?
vLLM supports Linux and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.
Source: https://docs.vllm.ai
Can vLLM be self-hosted?
Yes. vLLM can be deployed cloud / SaaS, on-premise and self-hosted, so it does not have to run on the vendor's infrastructure.
Source: https://docs.vllm.ai
What does vLLM integrate with?
We have confirmed 32 integrations for vLLM, including Hugging Face, NVIDIA Dynamo, OpenAI-compatible API, Anthropic Messages API, FlashAttention, FlashInfer, CUTLASS and torch.compile, plus 24 more. This is what we could verify from public sources, so the vendor may support others we have not indexed.
Source: https://docs.vllm.ai
Is vLLM open source?
Yes. vLLM is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.
vLLM alternatives
Other mlops & llmops tools we track, ranked by the same independent score.
- PortkeyAn API gateway for routing requests across thousands of language models with built-in safety guardrailshigh · 76%
- LangChainA platform for building, testing, and operating AI agents at scalemedium · 70%
- BasetenAn inference platform for deploying and running AI models at scalemedium · 73%
- Together AICloud platform for deploying and running open-source AI models with optimized inferencemedium · 61%
- OllamaA platform for running open-source language models locally or in the cloud with cost-effective access.medium · 57%
- RayDistributed computing infrastructure for scaling machine learning applicationsmedium · 65%
Compare vLLM
Side by side against other mlops & llmops tools, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for vLLM, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Package downloads | 79 | 0.14 | 10.7 | ✓ |
| Capabilities | 100 | 0.07 | 7.0 | ✓ |
| Development activity | 63 | 0.09 | 5.9 | ✓ |
| Release cadence | 95 | 0.05 | 4.9 | ✓ |
| Integrations | 43 | 0.08 | 3.3 | ✓ |
| Stars | 94 | 0.03 | 2.4 | ✓ |
| Dependent projects | 13 | 0.06 | 0.8 | ✓ |
| Security posture | 0 | 0.06 | 0.0 | - |
| Security score | 0 | 0.04 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 100 | mediumsource · 2026-09-13 · 65% |
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 6 items | mediumsource · 2026-09-10 · 66% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Role: serving · Open source: Yes · Managed cloud: No · Model serving: Yes · Self hostable: Yes · Multi provider: Yes | mediumsource · 2026-09-13 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 29 | mediumsource · 2026-08-21 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-09-13 · 98% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-09-13 · 95% |
Market
| Attribute | Value | Evidence |
|---|---|---|
| Availability | PrimaryMarkets: … · AvailabilityScope: global · AvailableCountries: … · NotAvailableCountries: … | highsource · 2026-08-21 · 75% |
Pricing
Release
Security
| Attribute | Value | Evidence |
|---|---|---|
| Vulnerabilities | Count: 70 · Source: https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=vllm&per_page=100 · Last 12m: 45 · Max severity: CRITICAL | highsource · 2026-09-13 · 90% |
| Trust center | https://docs.vllm.ai/en/latest/usage/security/ | mediumsource · 2026-09-13 · 60% |
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