ollama
Ollama is an open-source application that lets you run large language models locally on your machine without relying on cloud APIs. It supports models like Llama, Gemma, Mistral, and DeepSeek, and provides both a command-line interface and REST API for integration.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | ollama/ollama |
| Owner | ollama |
| Primary language | Go |
| License | MIT — OSI-approved |
| Stars | 175.7k |
| Forks | 16.9k |
| Open issues | 3.4k |
| Latest release | v0.31.1 (2026-06-30) |
| Last updated | 2026-07-08 |
| Source | https://github.com/ollama/ollama |
What ollama is
Written in Go, Ollama wraps the llama.cpp backend to enable efficient local LLM inference across macOS, Windows, Linux, and Docker. It exposes a REST API and provides official Python and JavaScript SDKs for programmatic access, with support for model management, streaming, and integration into existing applications.
Get the ollama source
Clone the repository and explore it locally.
git clone https://github.com/ollama/ollama.gitcd ollama# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Model compatibility: verify your target model is available on ollama.com/library or can be imported as a Modelfile; quantization format (Q4, Q5, Q8) affects performance and memory trade-offs.
- Hardware planning: confirm available GPU (NVIDIA, Apple Metal, Vulkan) or CPU resources; expect 4–20 seconds per token on mid-range GPUs depending on model size and quantization.
- API integration: use the REST API (localhost:11434) or official SDKs (Python, JavaScript); consider stateless request design for containerized deployments.
- Model lifecycle: manage model downloads and caching; pull updates explicitly (`ollama pull <model>`) to stay current.
- Latency expectations: set realistic user-facing timeouts (5–60 seconds) for chat/completion endpoints; streaming responses reduce perceived latency.
When to avoid it — and what to weigh
- Requiring State-of-the-Art Model Performance — Ollama primarily targets quantized, open-source models. If you need the latest proprietary or instruction-tuned commercial models, cloud APIs (OpenAI, Anthropic) are more direct.
- Minimal Hardware Available — Running LLMs locally requires adequate GPU/CPU and RAM. Devices with <8GB RAM or older hardware will face significant latency and may not support larger models.
- Enterprise-Grade SLA & Support Required — Ollama is community-driven open-source software. If you need guaranteed uptime, dedicated support, or formal SLAs, managed cloud platforms are more appropriate.
- High-Throughput Concurrent Inference — Ollama is designed for single-machine deployment. Large-scale multi-user or distributed inference workloads are better served by platforms like vLLM, Ray, or managed cloud services.
License & commercial use
Licensed under the MIT License, a permissive OSI-approved license allowing broad use, modification, and redistribution with minimal restrictions.
MIT License permits commercial use without restriction. However, verify compliance with individual model licenses (Llama, Gemma, etc.), which may have their own terms; some models restrict commercial use. Review each model's license from ollama.com/library before production deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Local inference eliminates cloud data transmission risk. No authentication built into the REST API by default—if Ollama runs on a network-exposed port, restrict access via firewall or proxy. Model downloads are fetched from ollama.com; validate checksums if deploying to air-gapped or high-security environments. Evaluate the security posture of individual models before processing sensitive data.
Alternatives to consider
vLLM
Designed for high-throughput, multi-GPU inference; better for production serving and batched requests; more complex setup than Ollama.
LocalAI
Similar local inference model, supports OpenAI-compatible API; slightly less polished UI/CLI but comparable feature set.
Cloud APIs (OpenAI, Anthropic, Google)
No hardware investment or latency concerns; latest models always available; higher per-token cost; data leaves your control.
Build on ollama with DEV.co software developers
Start with a proof-of-concept: download Ollama, run a model locally, and integrate via the REST API. Confirm hardware requirements and model license compatibility for your use case.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
ollama FAQ
Can I use Ollama for production?
Which models are best supported?
Do I need a GPU?
How do model licenses affect commercial use?
Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If ollama is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to evaluate Ollama for your team?
Start with a proof-of-concept: download Ollama, run a model locally, and integrate via the REST API. Confirm hardware requirements and model license compatibility for your use case.