Portkey
AI gateway that routes requests across 200+ LLMs with automatic fallbacks, load balancing, caching, and observability - YC W23, free tier available.
Portkey is a production AI gateway that sits in front of any LLM provider and adds routing intelligence, reliability features, and observability without changing application code. Teams configure routing rules - primary model, fallback chain, load balancing weights - and Portkey handles automatic failover when a provider goes down, request caching for repeated prompts, and semantic caching for near-duplicate queries. Founded in 2023 by Rahul Sonwalkar and Ayush Garg through YC W23 and backed by $3M, Portkey supports 200+ models across OpenAI, Anthropic, Mistral, Google, AWS Bedrock, Azure, and local models. The platform adds virtual API key management so developers never expose raw provider credentials in application code.
Key Features
- Automatic fallback routing - if primary LLM fails, Portkey switches to backup model without request failure
- Load balancing across multiple providers or API keys with configurable weight distribution
- Prompt and semantic caching that returns cached responses for identical or near-identical queries
- 200+ model support including all major commercial providers and self-hosted models via OpenAI-compatible format
- Virtual API keys that isolate application code from raw provider credentials and enable per-app key rotation
- Request logging, cost attribution, and latency analytics without requiring a separate observability tool
- Guardrails for input/output filtering to block harmful content and enforce response format constraints
Use Cases
- Teams building multi-model applications that need seamless failover when a provider has an outage
- Engineering teams centralizing LLM credentials and access control across many internal services
- AI products reducing costs by caching frequent repeated queries that would otherwise hit the API each time
- Enterprises needing a single control plane to monitor usage, costs, and reliability across all LLM integrations
Pros
- Automatic failover routing makes AI applications resilient to provider outages without engineering custom retry logic
- 200+ model support through one integration point eliminates provider-specific SDK switching costs
- Semantic caching reduces costs on applications with repetitive prompts - high ROI for Q&A and support bots
Cons
- Another network hop in the request path adds latency - typically 10-30ms per request for US to US routing
- Configuration complexity grows quickly for sophisticated routing rules with many providers and conditions
- Free tier caps at 10,000 requests per month which limits sustained evaluation before committing to paid
Portkey Alternatives
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