Scaling LLM Workflows for Enterprise
Architectural patterns for deploying LLM applications to thousands of concurrent enterprise users.
Enterprises have unique challenges: data privacy, massive scale, high reliability, and strict compliance. Scaling an LLM workflow isn't just about spinning up more servers; it's about creating a robust, multi-layered architecture.
Multi-Layered Caching
Implement caching at three levels: (1) Local semantic cache for common user queries, (2) Redis-based result cache for application state, and (3) Document-index cache for fast RAG retrieval. This reduces load on your LLM API and provides a much faster experience.
Gateway and Rate Limiting
Protect your LLM backend with a robust gateway that enforces rate limiting, authentication, and usage quotas. This prevents a single user or runaway script from overwhelming your system or, worse, exhausting your entire monthly API budget in an hour.