Kamya Shah

Kamya Shah

How AI Gateways Tackle Rate Limiting for LLM Apps

How AI Gateways Tackle Rate Limiting for LLM Apps

Every LLM application runs into the same production wall: provider rate limits. A spike in traffic, a long context window, or a runaway agent loop trips a requests-per-minute or tokens-per-minute ceiling, and the API returns a 429 Too Many Requests error. Exponential backoff delays the problem

Smart LLM Routing: Picking the Optimal Model per Request

Smart LLM Routing: Picking the Optimal Model per Request

Smart LLM routing in production cuts cost and latency by matching each request to the right model. Learn how Bifrost routes at the gateway layer. Every production AI application eventually hits the same wall: the team picked one default model, costs keep climbing, and latency on simple requests is indistinguishable

How to Govern Claude Code Usage Across Engineering Teams

How to Govern Claude Code Usage Across Engineering Teams

Govern Claude Code usage across engineering teams with virtual keys, hierarchical budgets, and tool filtering. Bifrost is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. Claude Code has become the default terminal coding agent in most engineering organizations, and

What Production AI Systems Need from an MCP Gateway in 2026

What Production AI Systems Need from an MCP Gateway in 2026

Production AI systems need an MCP gateway with access control, auth, token efficiency, and audit trails. Here's the 2026 checklist and how Bifrost meets it. Model Context Protocol moved out of the pilot phase in 2026. Enterprises that started experimenting with MCP in 2025 are now running it

Reducing Your OpenAI and Anthropic Bill with Semantic Caching

Reducing Your OpenAI and Anthropic Bill with Semantic Caching

Cut OpenAI and Anthropic API bills 40 to 70 percent with semantic caching. Learn how Bifrost's gateway-level cache captures redundant traffic at scale. OpenAI and Anthropic bills are growing faster than traffic for most teams shipping LLM features. Claude Opus 4.7 costs $5 per million input

The Complete AI Guardrails Implementation Guide for 2026

The Complete AI Guardrails Implementation Guide for 2026

A practical guide to AI guardrails implementation covering prompt injection, PII, content safety, and how Bifrost by Maxim AI enforces policies at the gateway layer. AI guardrails implementation is no longer optional for teams running LLMs in production. With the EU AI Act's high-risk obligations taking effect

Semantic Caching for LLMs: Cut Cost and Latency at Scale

Semantic Caching for LLMs: Cut Cost and Latency at Scale

Semantic caching for LLMs reduces API cost and latency by serving cached responses to similar queries. Learn how Bifrost makes it production-ready. LLM API bills grow faster than traffic for almost every team that ships a chatbot, a RAG application, or an agent. Users rarely ask the exact same