Why it matters
- Single endpoint for all LLM providers eliminates provider lock-in and enables A/B testing across GPT-4, Claude, Gemini, and others.
- Automatic fallbacks prevent production outages when a single provider experiences downtime or rate limits.
- Semantic caching can reduce LLM API costs by 20-40% for repeated or similar queries without any application changes.
- OpenAI SDK compatibility means zero code changes beyond the base URL — drop-in adoption for existing apps.
Key capabilities
- Multi-provider routing: Route to OpenAI, Anthropic, Cohere, Mistral, Azure OpenAI, AWS Bedrock, and 20+ providers.
- Automatic fallbacks: Define fallback chains; switch providers on rate limits or errors without downtime.
- Load balancing: Distribute requests across multiple API keys or providers for higher throughput.
- Semantic caching: Cache LLM responses by embedding similarity; serve cached answers for semantically equivalent queries.
- Request logging: Log every LLM call with input, output, latency, cost, and model metadata.
- Prompt versioning: Version and deploy prompts with rollback capabilities.
- Guardrails: Detect and filter PII, harmful content, and off-topic requests before they reach the LLM.
- Analytics dashboard: Track spend, latency, token usage, and error rates across providers.
- SDK compatibility: Works with OpenAI Python/JS SDK, LangChain, LlamaIndex, and raw HTTP.
Technical notes
- Integration: Change base URL to
api.portkey.ai+ addx-portkey-api-keyheader - SDK support: OpenAI Python, OpenAI JS, LangChain, LlamaIndex, raw REST
- Providers: OpenAI, Anthropic, Cohere, Mistral, Azure, Bedrock, Vertex AI, Groq, 20+
- Caching: Semantic cache (embedding-based) + exact match cache
- Observability: Logs, traces, cost tracking, latency percentiles
- Hosting: Cloud (Portkey-managed); self-hosted (Enterprise)
- Pricing: Free (10K req/mo); Pro ~$49/mo; Enterprise custom
- Founded: 2022; San Francisco; YC W23
Ideal for
- Teams running LLM apps in production who need reliability (fallbacks), cost control (caching), and visibility (logging).
- Organizations using multiple LLM providers who want a unified routing layer without custom middleware.
- Developers migrating between providers or A/B testing GPT-4 vs. Claude for quality and cost comparisons.
Not ideal for
- Local LLM deployments (Ollama, LocalAI) where the gateway adds unnecessary network hops.
- Simple single-model prototypes where the overhead of a gateway isn't justified.
- Teams who need full evaluation pipelines — Braintrust or LangSmith have stronger eval workflows.
See also
- Helicone — Simpler LLM observability proxy; logging-focused, less routing logic.
- LangSmith — LangChain-native observability with stronger trace visualization for complex chains.
- Braintrust — Enterprise eval platform with experiment management; less gateway, more eval.