AI Infrastructure & MLOps Free

vLLM

High-throughput open-source inference and OpenAI-compatible model serving

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PricingFree
Free planYes
Free trialYes
APIYes
Open sourceYes
DeploymentSelf Hosted
Last verifiedAugust 3, 2026
Overview

Tool overview

vLLM is listed under AI Infrastructure & MLOps AI tools.

Summary

What is vLLM?

vLLM is an Apache-2.0 inference and serving engine for large language and multimodal models. It provides OpenAI-compatible APIs, continuous batching, memory-efficient attention, distributed execution, official containers, and broad accelerator and model support.

Best fit

Best for

AI platform teams serving open models in production

Audience

Who is it for?

Machine-learning engineersAI infrastructure teamsBackend developersResearchers
Recommendation

Decision note

Rebuilt from the original export under the complete V412/V411 factual-source-verification workflow. Preview only. Apply remains blocked until Failures = 0, Warnings = 0, Unmapped = 0, Missing = 0; explicit clears are reviewed; image import is disabled or V380 accepts the asset; a representative WordPress edit screen is compared with the export and proposed row; and a post-Apply zero-change Preview succeeds.

Capabilities

Key features

OpenAI-compatible chat, completions, embeddings, and related endpoints

PagedAttention and continuous batching

Tensor, pipeline, data, and expert parallel serving

Official Docker images and distributed deployment support

Workflows

Use cases

Serve open models behind standard APIs

Run high-throughput GPU inference

Build internal model endpoints

Scale models across multiple accelerators and nodes

Strengths

Pros

  • Free under Apache-2.0
  • Large model and hardware ecosystem
  • High throughput and memory efficiency
Considerations

Cons

  • Production operation requires accelerator and systems expertise
  • Rapid releases can introduce compatibility changes
Considerations

Limitations

vLLM focuses on inference rather than model training.

Teams remain responsible for weights, licenses, safety, authentication, monitoring, capacity, and infrastructure cost.

Cost

Pricing details

Pricing modelFree
Free planYes
Free trialYes
Pricing context

Billing options

Free softwareUser-paid GPU or cloud infrastructureStorage, networking, observability, and support costs
Pricing context

Pricing note

vLLM has no software subscription fee under Apache-2.0. Users pay for GPUs or accelerators, cloud or local infrastructure, model storage, networking, monitoring, and operational support.

Compatibility

Supported languages

  • English
Connectivity

Integrations

OpenAI SDKs

Hugging Face models

Ray

Kubernetes

Prometheus

Docker

Specs

Technical details

PlatformsDesktop, API
Multilingual supportUnknown
Login requiredNo
Open sourceYes
LicenseApache-2.0
DeploymentSelf Hosted
CompanyvLLM Project
Current version0.23.0
Models / versionsGenerative models Embedding models Multimodal models Pooling models
Editions / plansOpen-source project Python package Official Docker images
Data confidenceHigh
Last verifiedAugust 3, 2026
Documentation → Source code → Release notes → Data source →
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Answers

Frequently asked questions

vLLM is an Apache-2.0 inference and serving engine for large language and multimodal models. It provides OpenAI-compatible APIs, continuous batching, memory-efficient attention, distributed execution, official containers, and broad accelerator and model support.
AI platform teams serving open models in production
The listed pricing model for vLLM is Free. Pricing can change, so users should verify the latest plan details on the official website.
Yes. The current profile indicates that a free trial is available.
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