What is vLLM?
Open-source LLM inference and serving engine providing offline inference, online serving, and optimizations including automatic prefix caching, speculative decoding, structured outputs, and quantization.
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
- Unknown
Platform & deployment
Independently observed- CLI
- Linux
- Cloud / SaaS
- On-premise
- Self-hosted
Integrations (14)
Independently observed- LangChain
- LlamaIndex
- Claude Code
- LiteLLM
- BentoML
- AnythingLLM
- Streamlit
- NVIDIA Triton
- KServe
- Haystack
- Hugging Face
- Modal
- RunPod
- SkyPilot
vLLM alternatives
Other mlops & llmops tools we track, ranked by the same independent score.
- OllamaThe easiest way to build with open modelslow · 22%
- OllamaThe easiest way to build with open modelslow · 45%
- BasetenInference is everythinglow · 47%
- PortkeyProduction Stack for Gen AI Builderslow · 46%
- MLflowOpen Source AI Platform for Agents, LLMs & Modelslow · 47%
- Weights & BiasesThe AI developer platformlow · 25%
The Vioscale 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 |
|---|---|---|---|---|
| Capabilities | 87 | 32.00 | 2775.0 | ✓ |
| Package Downloads | 79 | 26.00 | 2046.2 | ✓ |
| Github Activity | 63 | 18.00 | 1135.8 | ✓ |
| Release Cadence | 93 | 10.00 | 933.3 | ✓ |
| Integrations | 34 | 18.00 | 608.8 | ✓ |
| Github Stars | 93 | 5.00 | 466.3 | ✓ |
| Security Posture | 0 | 12.00 | 0.0 | - |
| Stackoverflow Activity | 0 | 12.00 | 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 · 2026-08-01 · 65% |
Adoption
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | {"role":"serving","evaluation":false,"managed_cloud":false,"model_serving":true,"self_hostable":true,"multi_provider":true,"vpc_deployment":true,"otel_compatible":true,"no_train_on_data":"unknown","llm_observability":true,"prompt_management":false,"framework_agnostic":true,"experiment_tracking":false} | mediumsource · 2026-08-01 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 14 | mediumsource · 2026-08-01 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-08-01 · 98% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-08-01 · 95% |
Pricing
Release
| Attribute | Value | Evidence |
|---|---|---|
| Cadence days | 12 | mediumsource · 2026-08-01 · 70% |