Portkey is a production control plane for GenAI applications. It sits between your app and 1,600+ LLM providers, offering a single unified API for routing, observability, guardrails, and governance. Teams use it to switch between models without code changes, track costs and latency per request, cache repeat queries, and enforce security policies. It suits AI engineering teams, platform engineers, and enterprises running multi-provider LLM workloads that need reliability, compliance, and cost control in production.

• Unified AI Gateway: Connect to 1,600+ LLMs across providers like OpenAI, Anthropic, and Vertex through one OpenAI-compatible API, switching models without changing application code.
• Reliability Controls: Automatic retries with exponential backoff, fallbacks to alternate providers on failure, load balancing across API keys, and request timeouts keep your app running when models go down.
• Observability Suite: Detailed logs, request tracing, latency metrics, and token/cost analytics broken down by app, team, model, or custom metadata tags.
• Semantic Caching: Reduce AI costs and response times by caching similar requests. Portkey’s semantic caching helps avoid repeated model calls, especially for RAG-based applications.
• Guardrails: Built-in request/response filters, jailbreak detection, PII redaction, and 50+ guardrail integrations for safe AI outputs.
• Prompt Management: Create, version, test, and deploy prompts across providers with canary testing for new models or prompt changes.
• Governance & Security: Virtual keys, role-based access control, budgets, rate limits, SSO/SCIM, audit trails, and SOC2/ISO/HIPAA/GDPR compliance.
• MCP Gateway: Centralized control plane for Model Context Protocol servers with authentication, access control, and full tool-call logging.

• Fast Multi-Provider Setup: Integrate any of 1,600+ LLMs in under two minutes without rewriting application code.
• Strong Observability: Users praise the dashboard for making debugging, latency analysis, and cost tracking straightforward.
• Better Cost Control: Reduce unnecessary AI spending with caching, usage tracking, routing controls, and budget management tools.
• High Reliability: Automatic failover between providers keeps applications running during outages, with reported 99.99% uptime at scale.
• Secure Key Management: Store provider API keys in a vault so developers never see raw credentials.

• Steep Learning Curve: The dashboard is feature-dense and can feel complex, especially for teams new to LLMOps.
• Cloud-First Features: Semantic caching, guardrails, and prompt versioning work best in the hosted SaaS; on-prem deployments require the Enterprise tier.
• Log Limits on Lower Tiers: Retention caps on Dev and Pro plans can be restrictive for compliance-heavy use cases.
• Requires Technical Knowledge: Portkey is built for developers and AI teams, so non-technical users may find the setup and configuration process challenging.

• Multi-Provider LLM Applications: Route traffic across OpenAI, Anthropic, and Google based on cost, latency, or specialization, and swap providers without redeploying code.
• RAG Cost Optimization: Enable semantic caching on retrieval-heavy apps to serve 18-60% of queries from cache while keeping accuracy near 99%.
• Enterprise AI Platforms: Give hundreds of internal engineers a governed gateway with per-team budgets, virtual keys, and cost attribution across many GenAI use cases.
• Multi-Tenant SaaS Billing: Attach metadata like user ID, tenant, or feature to every request, then slice analytics for accurate per-customer cost tracking.
• Tiered User Experiences: Use conditional routing to send premium users to GPT-4 while free users get cheaper open models, all through one endpoint.
• AI Agent Workflow: Manage MCP servers centrally with authentication, access control, and full logging of every tool call across agents.