Portkey is a production-grade AI Gateway and LLMOps platform that provides reliability, cost optimization, and full observability for LLM applications — acting as a smart reverse proxy between your application and AI providers, with automatic fallbacks, semantic caching, detailed tracing, and budget controls that transform LLM API calls from fragile one-off requests into managed, monitored infrastructure.

What Is Portkey?

• Definition: A managed AI Gateway (cloud-hosted or self-hosted) and observability platform that intercepts LLM API calls through an OpenAI-compatible endpoint — adding reliability features (fallbacks, retries, load balancing), cost optimization (semantic caching, budget limits), and full observability (tracing, cost tracking, user analytics) transparently.
• Gateway Model: Applications send requests to Portkey's OpenAI-compatible endpoint instead of directly to providers — a single line change enables all Portkey features without modifying application logic.
• Provider Coverage: Routes to 200+ AI providers and models — OpenAI, Anthropic, Azure, Google Vertex, AWS Bedrock, Together AI, Groq, Ollama, and any OpenAI-compatible endpoint.
• Config-Based Routing: Routing logic (fallbacks, load balancing, caching) is defined in JSON configs stored in Portkey — decoupled from application code and changeable without redeployment.
• Enterprise Focus: Designed for teams managing LLM spend at scale — per-user budgets, team-level access controls, audit logs, and SSO integration.

Why Portkey Matters

• Reliability at Scale: Single provider outages don't bring down your application — Portkey automatically routes to fallback providers with sub-second switchover, maintaining user experience during OpenAI or Anthropic incidents.
• Cost Reduction: Semantic caching (not just exact match) can reduce API costs by 20-40% for applications with similar repeated queries — a user asking "What's the weather?" and another asking "Tell me the weather" can share a cached response.
• Unified Observability: Every request — across all providers, all models, all users — appears in a single dashboard with latency, cost, token usage, and error rate — replacing scattered per-provider monitoring.
• Prompt Management: Store, version, and A/B test prompts in Portkey's prompt library — deploy prompt changes without code releases.
• Multi-Tenant Control: Route different users or teams to different models, apply different rate limits, and track costs per customer — essential for SaaS products billing customers for AI usage.

Core Portkey Features

Automatic Fallbacks:

import portkey_ai

portkey = portkey_ai.Portkey(api_key="pk-...", config={
    "strategy": {"mode": "fallback"},
    "targets": [
        {"provider": "openai", "api_key": "sk-..."},
        {"provider": "anthropic", "api_key": "sk-ant-..."}
    ]
})
# If OpenAI fails, automatically retries on Anthropic — transparent to caller
response = portkey.chat.completions.create(model="gpt-4o", messages=[...])

Load Balancing:

config = {
    "strategy": {"mode": "loadbalance"},
    "targets": [
        {"provider": "openai", "weight": 0.7},   # 70% of traffic
        {"provider": "azure-openai", "weight": 0.3}  # 30% of traffic
    ]
}

Semantic Caching:

portkey = portkey_ai.Portkey(api_key="pk-...", cache={"mode": "semantic", "max_age": 3600})
# Requests semantically similar to cached queries return cached results — no LLM call

Observability Features

• Request Tracing: Every LLM call recorded with input, output, latency, tokens, cost, model, provider, and user ID.
• Cost Analytics: Daily/weekly/monthly spend by model, provider, user, or custom metadata tag — budget forecasting and anomaly detection.
• Error Analysis: Automatic categorization of errors (rate limits, context length, content policy) with retry rates and failure patterns.
• Feedback Integration: Attach user feedback (thumbs up/down, CSAT scores) to traces for quality monitoring.
• Custom Metadata: Tag requests with user_id, session_id, feature_name — filter any metric by any dimension.

Portkey vs Competitors

Deployment Modes

• Cloud Gateway: Use Portkey's managed endpoint — zero infrastructure, instant setup, 99.99% uptime SLA.
• Self-Hosted: Deploy Portkey Gateway on your own infrastructure — data never leaves your environment, required for regulated industries (healthcare, finance).
• SDK Integration: Python and TypeScript SDKs for programmatic config management and metadata attachment.

Portkey is the production LLM infrastructure layer that transforms unreliable AI API calls into managed, observable, cost-optimized services — for teams moving from prototype to production with LLM applications, Portkey provides the reliability and visibility that enterprise applications require without the months of custom infrastructure development.