Beginner-Intermediate

Use Cases

When to Use Ollama

🛠 Local Development

Iterate on LLM-powered apps without API costs or rate limits. The OpenAI-compatible API means your production code switches between Ollama (dev) and cloud APIs (prod) by changing one URL.

🔒 Privacy-Sensitive Apps

Healthcare records, legal documents, proprietary code -- when data cannot leave the premises. No data sent to external servers. Essential for GDPR, HIPAA compliance.

💻 AI Coding Tools

Power local code completion with Continue, Cline, or VS Code extensions. Low-latency local inference (no network round-trip) makes real-time coding assistance practical.

🔍 RAG Pipelines

Generate embeddings locally with nomic-embed-text for retrieval-augmented generation. The entire vector search + generation pipeline runs locally.

🤖 Edge Deployments

Run on NVIDIA Jetson and edge devices for on-device content moderation, smart assistants, or text analysis where cloud connectivity is unreliable.

Should You Use Ollama?

Use this interactive decision tree to determine if Ollama is the right fit for your use case.

Is Ollama Right for You?

Do you need to run LLMs locally (not via cloud APIs)?
Yes No
✅ Ollama is a great fit! Simple setup, automatic GPU management, OpenAI-compatible API.
☁ Consider cloud APIs (OpenAI, Anthropic) for your use case. No hardware management needed.
🚀 Consider vLLM or TensorRT-LLM for production serving. Use Ollama for your development environment.

When NOT to Use Ollama

⚠
Not for production serving at scale. No built-in load balancing, authentication, rate limiting, or metrics. Use vLLM, TensorRT-LLM, or managed APIs for serving hundreds of concurrent users.
⚠
Not for training or fine-tuning. Ollama is inference-only. For fine-tuning, use Unsloth, Axolotl, or Hugging Face, then import the result into Ollama.
⚠
Not for maximum performance. Ollama adds ~5-15% overhead over raw llama.cpp. If you need every token/second, use llama.cpp directly.