portkey.ai NEW

Portkey is an LLMOps platform that combines an AI Gateway, observability, guardrails, governance, and prompt management into a single interface. It exposes a unified API for accessing multiple LLMs and a model catalog, reducing integration complexity for developers and AI teams.

Portkey centralizes authentication, role-based access control, org-wide audit logs, and MCP gateway management to simplify governance and multi-team collaboration. Built-in observability and activity logs enable real-time monitoring of model behavior, anomaly detection, and usage tracking for cost and performance analysis.

Features like caching, routing, batching, and PII redaction help optimize token spend and protect sensitive data. Integrations with common developer and cloud tools support deployment, orchestration, and production-grade monitoring for MLOps and enterprise AI workflows.

Key Features

• Unified AI Gateway: single API to access and manage multiple LLMs with routing, intelligent caching, and request batching.
• Real-time Observability: dashboard with traces, metrics, logs, anomaly detection, and usage monitoring of LLM behavior.
• Guardrails & Governance: policy enforcement, role-based access control (RBAC), budget limits, and audit logging.
• Prompt Management & Engineering Studio: centralized prompt storage, versioning, and production-ready agent workflow management.
• MCP Gateway (Model Context Protocol): deploy, authenticate, govern, and observe MCP servers through a centralized gateway.

Use Cases

• Build an enterprise-grade multi-model customer support assistant using Portkey's unified LLM API and model catalog, leveraging prompt management, routing and caching to deliver consistent, low-latency responses while enforcing guardrails, RBAC and audit logs for compliance
• Implement secure, compliant AI workflows for finance or healthcare with Portkey by applying PII redaction, fine-grained RBAC, audit logging and observability to track model behavior and policy adherence across teams
• Optimize AI performance and costs by using Portkey to route requests across models, cache frequent prompts, monitor token usage and run A/B comparisons in the model catalog for automated token cost optimization and centralized observability

Who is it for?

• Ai developers and engineers
• Mlops teams
• Enterprises implementing multi-model ai systems
• Organizations requiring ai governance and compliance
• Teams needing llm observability and cost optimization