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Xinference
FreeOpen SourceAPISelf-HostedAgentic
Xinference is a self-hosted inference platform that lets you run open-source language and multimodal models locally with an OpenAI-compatible API.
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Get This Tool
License: Apache-2.0 Any use incl. commercial
Local-run terms: Users can freely deploy, modify, and distribute Xinference under Apache 2.0 terms. Commercial use is permitted without restrictions.
Pricing
- Model
- Free
Summary
Xinference solves the problem of deploying multiple model types across heterogeneous infrastructure without vendor lock-in. You install it on your own hardware—laptop, on-premises servers, or cloud instances—point it at open-source models, and get an API that talks like OpenAI's, making it straightforward to swap in your own models without rewriting client code. The catch: you own the operational burden. Performance hinges on your hardware choices and which inference backend (vLLM, llama.cpp, etc.) you pair with each model. Setup requires more hands-on work than a managed service, and the community and docs lag behind more established inference platforms.
Bottom line: *Use when data cannot leave your infrastructure or you need multi-model serving in one system; skip if you want managed simplicity over control.*
Hosted & API Pricing
The model is free to self-host. These are the creator's hosted/API options.Xinference Cloud (Managed Service)
via Xorbits
Custom
Hosted Xinference service with zero setup required
- Managed infrastructure
- Zero setup required
- Jupyter notebook access
Pricing may have changed since last verified. Check the official site for current plans.
Find your fit
Before you choose Xinference
More options in Model Hosting APIs. Compare pricing, platforms, and features side by side.
Agent Router
Free
Developers using multiple LLM backends
Pros
- OpenAI-compatible API reduces migration effort from OpenAI services
- Supports multiple model types and inference backends in one platform
- Flexible deployment options: local, on-premises, cloud, or distributed
- Seamless third-party integration with LangChain, LlamaIndex, and others
- Production-ready with auto-batching and distributed inference support
Cons
- Requires more setup and configuration compared to managed cloud services
- Performance depends heavily on hardware and chosen inference backend
- Documentation and community smaller than some established alternatives like vLLM
About
- Platforms
- Linux, Windows, macOS; Docker; Kubernetes
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- May 6, 2026
Best For
Who it's for
- Organizations requiring data privacy and private model deployment
- Teams building multi-model inference systems
- Researchers experimenting with open-source models
- Enterprises seeking cost-effective LLM deployment
- Developers integrating models with existing frameworks like LangChain
What it does well
- Private language model deployment and inference
- Speech recognition and audio processing
- Multimodal model serving (text, image, audio, video)
- AI application development with LLM integration
- Distributed model inference across multiple machines
Integrations
Frequently Asked Questions
- Is Xinference free?
- Xinference offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is Xinference open source?
- Yes. Xinference is open source — the source repository is at https://github.com/xorbitsai/inference.
- Does Xinference have an API?
- Yes. Xinference exposes a developer API. See the official documentation at https://xorbits.ai for details.
- Can I self-host Xinference?
- Yes. Xinference supports self-hosting on your own infrastructure.
- What platforms does Xinference support?
- Xinference is available on: Linux, Windows, macOS; Docker; Kubernetes.
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Xinference is a self-hosted inference platform that lets you run open-source language and multimodal models locally with an OpenAI-compatible API.
Xinference solves the problem of deploying multiple model types across heterogeneous infrastructure without vendor lock-in. You install it on your own hardware—laptop, on-premises servers, or cloud instances—point it at open-source models, and get an API that talks like OpenAI's, making it straightforward to swap in your own models without rewriting client code. The catch: you own the operational burden. Performance hinges on your hardware choices and which inference backend (vLLM, llama.cpp, etc.) you pair with each model. Setup requires more hands-on work than a managed service, and the community and docs lag behind more established inference platforms.
*Use when data cannot leave your infrastructure or you need multi-model serving in one system; skip if you want managed simplicity over control.*
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