Local LLMs and Embeddings

Local models can improve privacy, cost control, and offline resilience. Hosted models can still be better for frontier reasoning, managed scaling, and rapid iteration.

Local Inference

Local runtimes such as Ollama or llama.cpp-style deployments are useful for experimentation, private data workflows, and edge cases where external API calls are not acceptable.

Embedding Models

Embeddings are often a strong candidate for local execution. They can be run close to the data, reduce repeated API costs, and keep source material out of hosted providers.

Tradeoffs

Privacy: local models reduce third-party exposure.
Cost: local inference can be cheaper at steady usage but requires hardware and operations.
Quality: hosted frontier models may outperform local models for complex reasoning.
Latency: local models can be fast on the right hardware and slow on underpowered machines.
Maintenance: local stacks need updates, monitoring, and capacity planning.

Practical Pattern

Use local embeddings and retrieval for sensitive knowledge. Choose local or hosted generation based on task risk, latency needs, model quality, and budget.

Related pages: RAG and Knowledge Pipelines and Deployment Patterns.