Portkey

Portkey AI is a production-grade AI gateway and control panel that enables developers and organizations to access, manage, and monitor over 1,600 large language models (LLMs) from various providers through a unified API. It offers features like reliability, observability, guardrails, governance, and prompt management.

Portkey AI's main features include a unified API to access over 1,600 LLMs from different providers such as OpenAI, Anthropic, Bedrock, Groq, and xAI. It provides reliability through automatic retries, fallbacks, load balancing, and smart routing for high uptime. Observability features encompass real-time monitoring, logging, tracing, analytics, and cost tracking. Guardrails offer input/output checks, PII protection, and custom hooks for data security. Governance includes access controls, model restrictions, and usage policies. Prompt management involves centralized prompt templates and versioning. Additionally, it features intelligent caching to optimize performance and reduce costs, and canary testing to test new models and prompts without impacting production.

Setting up Portkey AI Gateway involves three straightforward steps: first, configure the Gateway by setting up your Portkey account and creating configurations for your desired LLM providers; second, send your first request using the Portkey client (such as Python, LangChain, Langroid, or CrewAI) to make requests through the gateway; and third, optimize routing and guardrails by fine-tuning routing rules, load balancing, and security guardrails for optimal performance.

Yes, Portkey AI integrates seamlessly with existing applications built on frameworks like CrewAI, LangChain, Langroid, and more. You only need to update your LLM configuration code to use Portkey’s gateway; the rest of your application code remains unchanged.

Portkey AI ensures reliability through smart routing, which dynamically switches between models and providers. It also uses automatic retries to rescue failed requests with configurable retry logic, load balancing to distribute traffic across multiple LLMs, and fallbacks that automatically route requests to backup models if primary models fail.

Portkey AI offers several security and compliance features, including virtual keys for secure API key management, guardrails to enforce input/output checks, PII protection, and custom hooks, access controls to restrict access to specific models and data, and audit logs for detailed compliance and monitoring.

Portkey AI optimizes costs through caching, which caches responses to reduce redundant API calls; usage tracking, which monitors and manages usage to stay within budget; and cost analytics, which provides real-time cost tracking and reporting.

Yes, Portkey AI supports canary testing, allowing you to test new models and prompts without impacting your production environment.

To get started with Portkey AI, first sign up for a Portkey AI account. Next, configure your gateway and set up your desired LLM providers. Then, use the Portkey client to make requests through the gateway. Finally, optimize your setup with routing, guardrails, and observability features.

More resources and support can be found in the Portkey AI Docs, the Portkey Developer Forum, the Portkey Cookbook GitHub Repository, and by joining the Portkey team on Discord for community support.

Portkey AI offers various pricing plans, including community-negotiated pricing for institutions. Check the official website or contact Portkey for detailed pricing information.

To handle errors and rate limits with Portkey AI, you can configure retry parameters and use virtual keys for better rate limit management, and set up usage policies and monitor rate limits through the Portkey dashboard.