Ollama is the platform for running open-source language models — Llama, Mistral, Qwen, Phi, and dozens more — directly on your company’s servers, without sending any data to outside providers. For companies handling confidential information, health data, trade secrets, or any data with special protection under the GDPR, Ollama removes the risk of transferring data to third parties by design.
Why local deployment matters
When a company uses ChatGPT or Claude through their APIs, the data in every query travels to OpenAI’s or Anthropic’s servers, gets processed there, and comes back. Enterprise versions carry solid contractual guarantees, but the architecture still means data leaves the company’s infrastructure.
With Ollama, the model runs on your own hardware — an on-premises server, a private cloud instance, or even high-performance local hardware. Data never leaves your organization’s network.
Available models and what they do
Ollama gives you access to the most powerful open-source models on the market:
- Llama (Meta): a family of general-purpose models that come close to proprietary models on many tasks, available in sizes from 8B to 405B parameters.
- Mistral and Mixtral: high-performance, compute-efficient models, especially strong at reasoning and code.
- Qwen (Alibaba): excellent multilingual performance, including Spanish, French, Italian, German, and other major European languages.
- Phi (Microsoft): compact models with surprising performance for their size, ideal for more limited hardware.
- DeepSeek: very high-performing models for reasoning and code.
When Ollama is the right answer
Sectors with especially sensitive data. Healthcare (medical records, clinical reports), legal (client files, confidential documents), finance (account data, wealth analysis), and any sector where GDPR compliance for special-category data rules out sending data to third parties.
Trade secrets. Proprietary formulas, manufacturing processes, R&D data — the information that defines your competitive advantage shouldn’t be processed on third-party infrastructure.
Offline environments. Industrial plants, military or government settings, or infrastructure with restricted external connectivity, where cloud models aren’t an option.
Cost control at scale. For applications with very high query volumes (millions of requests a month), the per-token cost of external APIs can make local deployment cheaper beyond a certain threshold.
Hardware requirements
The hardware you need depends on model size. A 7–8B parameter model runs well on a consumer GPU (16 GB VRAM). 70B models require professional GPUs (A100, H100) or multiple high-end GPUs. For enterprise production, we recommend assessing latency and throughput requirements before sizing the hardware.
Frequently asked questions
Are Ollama models as good as ChatGPT or Claude? The best open-source models (Llama, Qwen, DeepSeek, or Mistral) come close to proprietary models on most tasks. For use cases where quality is critical, testing with real data is essential before deciding.
Do we need an in-house technical team to maintain Ollama? Yes, or an outside provider to manage it. Deploying and maintaining local models takes technical capabilities that cloud APIs don’t. Factor that cost into the analysis.
Is Ollama suitable for RAG systems? Yes. Ollama can serve as the reasoning engine in a fully local RAG architecture, combined with an on-premises vector database such as Weaviate or Qdrant.