Kuldeep Paul

An LLM gateway routes every model request through one API and applies failover, load balancing, and budgets on the request path. This guide compares Bifrost, LiteLLM, Kong AI Gateway, Cloudflare AI Gateway, and OpenRouter, and shows how to benchmark each at your target RPS.

Configure multi-provider routing and custom providers in Bifrost with governance rules, weighted distribution, and request-type controls for production AI. Production AI applications routinely encounter provider-side issues: regional outages, rate-limit rejections at peak load, model deprecations, and unpredictable tail latency. Multi-provider routing addresses these problems by

Implement LLM guardrails at the gateway layer with Bifrost: content safety, PII detection, prompt injection blocking, and MCP tool governance in one platform. LLM guardrails are the runtime controls that validate every prompt and response flowing through an AI application, blocking harmful content, redacting sensitive data, and enforcing policy before

Adaptive model routing and automatic fallback logic keep LLM apps running through provider outages. See how Bifrost handles both at the gateway layer. Adaptive model routing is the practice of selecting which LLM provider, model, and API key should handle each request at runtime, based on live signals like provider

Learn how Bifrost delivers LLM observability with built-in logging, native OpenTelemetry (OTEL) tracing, Prometheus metrics, and multi-backend support. LLM observability has become a non-negotiable requirement for any team running AI workloads in production. Once requests start fanning out across multiple providers, models, and teams, the inputs, outputs,

Route AWS Bedrock, Google Vertex AI, Gemini, and Anthropic through one OpenAI-compatible API with Bifrost. Unified auth, failover, and governance. Enterprise AI teams rarely run a single model from a single provider. A typical production stack pulls Claude from AWS Bedrock for one set of workloads, Gemini from Google