Ollama
Run large language models on your own computer
- Category
- Coding & Development
- Pricing
- Free and open-source for local use; optional Cloud from $20/mo (Pro), $100/mo (Max), Team from $125/mo
- Best for
- Ollama is a strong fit for individual developers, privacy-conscious teams, and hobbyists who want to run open-source models locally for free and integrate them into scripts or agents via a simple API.
- Official site
- ollama.com
- Last updated
- August 2026
Ollama is an open-source tool for downloading, running, and managing large language models directly on a user's own machine — macOS, Windows, or Linux. It was founded in 2023 by Jeffrey Morgan and Michael Chiang, who previously built the Docker Desktop precursor Kitematic (acquired by Docker) and the developer-infrastructure startup Infra. Ollama's core insight was applying Docker-like simplicity to LLMs: instead of manually managing weights, dependencies, and hardware configuration, a developer can run a single command like `ollama pull llama3` or `ollama run gemma3` and have a working model in minutes. The project quickly became one of the most widely adopted tools for local AI, amassing over 170,000 GitHub stars and tens of millions of monthly model pulls, and it now underpins a large ecosystem of community tools, including chat UIs and agent frameworks that connect to it via its REST API.
While local inference remains completely free and unlimited (bounded only by the user's own hardware), Ollama has layered a paid Cloud offering on top for models too large to run on typical consumer GPUs. Ollama Cloud routes requests to larger, more powerful cloud-hosted models while keeping the same CLI, API, and desktop app workflow, and Ollama states it does not log or train on prompt or response data even in the cloud tier. The company has raised a total of roughly $88 million in funding and is based in Palo Alto, positioning itself as the default on-ramp for private, offline, and on-device LLM use even as it expands into hybrid local/cloud usage.
Ollama is a strong fit for individual developers, privacy-conscious teams, and hobbyists who want to run open-source models locally for free and integrate them into scripts or agents via a simple API. It's a weaker fit for teams that need managed, always-available inference at frontier-model quality without hardware constraints, or that want a polished graphical chat interface out of the box rather than a CLI-first tool.
Key features
One-command model management
Pull and run any supported open model with a single CLI command, with no manual weight downloads or environment configuration required.
Local-first, private inference
Models run entirely on the user's own hardware by default, so no prompt or code ever leaves the machine unless the Cloud tier is explicitly used.
REST API and CLI
A local REST API and command-line interface make it straightforward to integrate Ollama into scripts, IDEs, and agent frameworks like LangChain.
Broad model library
Supports 100+ open-source models including Llama, Gemma, Qwen, DeepSeek, and Mistral, with new releases added regularly.
Ollama Cloud
An optional paid tier that runs larger, more powerful models on Ollama's cloud infrastructure when they don't fit on local hardware, using the same CLI and API.
Multimodal and tool calling
Supports vision-capable models and function/tool calling, so local models can be used in agentic workflows, not just chat.
Cross-platform apps
Native desktop applications and CLI tooling for macOS, Windows, and Linux.
Custom Modelfiles
Developers can define custom system prompts, parameters, and model variants through simple Modelfile configuration.
Pricing breakdown
Free
- Run models on your own hardware, unlimited
- Access to cloud models (light usage)
- CLI, API, and desktop apps
- 40,000+ community integrations
- Unlimited public models
Pro
- Everything in Free
- Access larger, more powerful cloud models
- Run 3 cloud models concurrently
- 50x more cloud usage than Free
- Upload and share private models
Max
- Everything in Pro
- Run 10 cloud models concurrently
- 5x more cloud usage than Pro
Team
- Shared billing across seats
- Usage beyond seat allowance drawn from shared team balance
- Centralized management
Pros and cons
Pros
- Local inference is entirely free and unlimited, bounded only by the user's own hardware, which eliminates per-token API costs for experimentation and many production use cases.
- Setup genuinely takes minutes: install Ollama, run one command, and start chatting with or calling a model, with no manual dependency wrangling.
- Ollama states it never logs or trains on prompt or response data, and local mode keeps all data on-device, which appeals strongly to privacy- and compliance-conscious users.
- A large open ecosystem — over 40,000 claimed community integrations including LangChain, Open WebUI, and various agent frameworks — means it slots into existing developer workflows easily.
- Cross-platform native support (macOS, Windows, Linux) and a growing library of 100+ open models keep it broadly useful as new open-weight models are released.
Cons
- Performance and which models are practical to run depend entirely on the user's own GPU/RAM, so larger frontier-scale open models may be slow or infeasible without the paid Cloud tier.
- As of research, new Max plan sign-ups are paused while Ollama adds cloud capacity, meaning power users can't currently subscribe to the highest usage tier.
- The experience is primarily CLI- and API-driven; Ollama itself doesn't ship a fully polished built-in chat GUI, so users often pair it with a separate web UI project.
- Advanced configuration — GPU memory tuning, multimodal setups, custom Modelfiles — has documentation gaps according to user reviews, requiring some trial and error.
- Concurrent request handling and VRAM management can get tricky with larger local models, occasionally causing scaling friction outside of light single-user use.
What reviewers say
Ollama holds a 4.3 out of 5 rating on G2 from a small pool of five reviews, with reviewers consistently praising ease of local setup and privacy, while noting some rough edges in GPU memory handling and reliance on the command line.
Frequently praised
- Extremely simple one-command install and model setup
- Strong privacy from fully local, offline inference
- Clean REST API that integrates easily into existing projects
Frequently criticized
- GPU memory management can be tricky with larger models
- Too much reliance on command-line interface versus a built-in UI
- Documentation for advanced configuration could be more detailed
Alternatives to Ollama
GitHub Copilot
AI pair programmer built into major IDEs
Compare →Cursor
AI-first code editor built for agentic coding
Compare →Claude Code
Anthropic's agentic coding tool for the terminal
Compare →Replit AI
AI app builder inside a browser-based dev environment
Compare →Sofy
AI testing agents that write, run, and fix tests across mobile, web, API, and enterprise apps
Compare →Factory
Agent-native software development — autonomous Droids for the full SDLC
Compare →Zeedle AI
AI agents that generate, test, and optimize your code end-to-end
Compare →Windsurf
The agentic IDE that codes alongside you
Compare →Supermaven
The fastest AI code completion, built for huge codebases
Compare →Cohere
Secure, enterprise-grade LLMs built for the real business world
Compare →Fireworks AI
Fast, affordable inference for open-source AI models
Compare →Langflow
Visual drag-and-drop builder for LangChain AI agents
Compare →Frequently asked questions
Is Ollama free?
Yes, running models locally on your own hardware is completely free and unlimited. Optional paid Cloud tiers (Pro from $20/month, Max from $100/month) unlock larger cloud-hosted models.
What is Ollama Cloud?
A paid tier that runs larger, more powerful models on Ollama's own cloud infrastructure for cases where local hardware can't handle a given model, using the same CLI and API.
Which models can I run?
Ollama supports 100+ open-source models, including Llama, Gemma, Qwen, DeepSeek, Mistral, and others, with new models added regularly.
Does Ollama use my GPU?
Yes, it automatically detects and uses supported NVIDIA, AMD, or Apple Silicon GPUs to accelerate local inference.
Is my data private?
Local inference never leaves your machine, and Ollama states it does not log or train on prompt or response data, including in the Cloud tier.
Can I use Ollama with agent frameworks like LangChain?
Yes, its REST API and broad community integration ecosystem make it straightforward to plug into agent frameworks and existing developer tooling.
Ready to try Ollama?
Head to the official site to explore pricing and start a free trial where available.
Visit Ollama →