Sriti Core: Local-First LLM Router with Cascading Fallbacks

Sriti Core addresses a practical challenge in production LLM deployment: how to balance the privacy and cost benefits of local inference against the capabilities of frontier cloud models. Rather than choosing one or the other, it implements intelligent cascading that routes requests through multiple inference layers based on complexity and latency requirements.

The architecture prioritizes local Ollama models first, falling back to cloud APIs only when necessary. This approach minimizes inference costs by keeping simple queries on-device, while preserving the ability to route complex tasks to more capable models. The system can learn which tasks succeed locally versus which require external APIs, optimizing routing decisions over time.

For practitioners building production systems, this represents a mature approach to hybrid inference: maintaining full local capability while gracefully degrading to cloud alternatives. This architecture pattern will likely become standard in enterprise deployments where privacy, cost control, and capability must all be balanced simultaneously.

Read the full article on Hacker News.

Source: Hacker News · Relevance: 8/10