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It's a split decision.
Choose LocalAI if you want a self-hosted runtime with OpenAI, Anthropic, Ollama and ElevenLabs API compatibility, plus swappable engines and distributed routing across your machines. Choose Ollama if you want a straightforward CLI, official Python and JavaScript SDKs, and the option to use hosted cloud models. Both are MIT-licensed and can run locally for free. Your choice mainly depends on whether you need LocalAI’s engine and routing options or Ollama’s model-management tools and cloud plans.
01Side by side
| LocalAI | Ollama | |
|---|---|---|
| Pricing model | Free self-hosted runtime; hardware and operations are yours | Free local use; paid cloud plans and token usage |
| Free tier | Unlimited self-hosted usage at $0 per month | $0 subscription for local models; three concurrent requests |
| Open source and licence | Open source, MIT licence | Open source, MIT licence |
| Self-hosting | Run on your hardware, from laptop to GPU room | Run locally, offline, on-premises, or in self-hosted cloud |
| API and SDKs | OpenAI, Anthropic, Ollama, ElevenLabs APIs | OpenAI and Anthropic endpoints; official Python and JavaScript SDKs |
| CLI and docs | Documentation; one-line engine configuration | CLI for model management; official documentation |
| Integrations | MCP servers, plugins, skills | 100+ community integrations, including Open WebUI and LangChain |
| Deployment model | Self-hosted runtime with distributed routing across machines | Local runtime plus hosted cloud models |
LocalAI Choose LocalAI if
- Choose LocalAI if you need swappable model engines behind compatible APIs.
- Choose LocalAI if you want to route workloads across your own machines.
- Choose LocalAI if you need voice, vision, image, video, or 3D workloads.
Ollama Choose Ollama if
- Choose Ollama if you want official Python and JavaScript SDKs.
- Choose Ollama if you want CLI tools for managing models.
- Choose Ollama if you want local inference plus hosted cloud options.
02Pricing at real usage
LocalAI’s self-hosted MIT software costs $0 per month and includes unlimited self-hosted usage. That does not cover your hardware or operating costs; LocalAI lists no paid plan or hardware pricing. Ollama’s local models also have a $0 subscription option. For cloud use, its Pay as you go plan has no subscription, charges per token and includes three concurrent requests. The pricing page does not list token rates.
Ollama Pro costs $20 monthly or $200 billed annually and includes $60 in monthly usage credits. Max costs $100 monthly with $300 in credits; Team costs $500 monthly with $1,000 in shared credits. Team usage beyond those credits is billed as you go. The pricing page does not list prices for purchasing additional credits. For a local deployment, compare hardware and operating costs; for hosted inference, model the credit plan against expected token usage.
03API, SDKs and integration
LocalAI runs a single binary with an OpenAI-compatible API. Its homepage also lists Anthropic, Ollama and ElevenLabs API compatibility. Existing clients can often be pointed at its URL, while model configuration selects an engine such as llama.cpp, vLLM, SGLang or MLX. LocalAI also supports streaming voice, transcription and synthesis over WebRTC, plus MCP servers, plugins and skills.
Ollama runs a local REST API on port 11434, with endpoints for completions, chat and embeddings. It offers OpenAI-compatible and Anthropic-compatible endpoints, official client SDKs for Python and JavaScript, and a CLI to pull, run, list, copy, stop and delete models. Modelfiles configure weights, prompts, inference settings, templates and LoRA adapters. Its documentation catalogs more than 100 community integrations, including Open WebUI, Continue, LangChain and LlamaIndex. If you need an official language SDK, Ollama documents two; LocalAI’s listed integration approach is API compatibility.
04Open source, self-hosting and lock-in
Both projects are open source under the MIT licence and support self-hosting. LocalAI is designed to run on your hardware, from a laptop to multiple machines, and says every feature has a CPU path. Its compatible APIs and swappable engines give you options for changing model backends, though engine and model configuration still need to be managed.
Ollama runs locally, offline, on-premises or in a self-hosted cloud environment, and also offers hosted cloud models. Its Modelfiles and support for GGUF, PyTorch and Safetensors give developers documented ways to configure models and work with weights. Ollama says local prompts and outputs are not stored or used for model training. Both let you keep local inference on your own infrastructure. Neither product page describes a dedicated data-export workflow, so plan to manage model files and configuration as artifacts you control.
Questions people ask
Is LocalAI better than Ollama for local inference?
Neither is universally better. LocalAI fits teams that want multiple compatible APIs, swappable engines and routing across machines. Ollama fits developers who want a model-management CLI, official Python and JavaScript SDKs, and a path to hosted cloud models. Both support local inference.
Can Ollama use the OpenAI API?
Ollama provides OpenAI-compatible endpoints, as well as Anthropic-compatible endpoints. Applications using compatible clients can point requests at Ollama. LocalAI also supports OpenAI-compatible APIs, alongside Anthropic, Ollama and ElevenLabs APIs.
Can LocalAI run without a GPU?
Yes. LocalAI’s homepage says every feature has a CPU path and that a GPU is optional. Performance will depend on your hardware and workload; the vendor's site does not provide comparative performance figures for LocalAI and Ollama.
Does Ollama offer cloud models?
Yes. Ollama offers hosted cloud models through its unified API, alongside local model execution. Its listed cloud options include a pay-as-you-go plan and paid subscriptions with usage credits. LocalAI is presented as a runtime for workloads on your own hardware.