> ## Documentation Index > Fetch the complete documentation index at: https://portkey-docs-feat-rerank-documentation.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Arize Phoenix > Extend Portkey’s powerful AI Gateway with Arize Phoenix for unified LLM observability, tracing, and analytics across your ML stack. Portkey is a **production-grade AI Gateway and Observability platform** for AI applications. It offers built-in observability, reliability features and over 40+ key LLM metrics. For teams standardizing observability in Arize Phoenix, Portkey also supports seamless integration. Portkey provides comprehensive observability out-of-the-box. This integration is for teams who want to consolidate their ML observability in Arize Phoenix alongside Portkey's AI Gateway capabilities. ## Why Portkey + Arize Phoenix? [Arize Phoenix](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-portkey) brings observability to LLM workflows with tracing, prompt debugging, and performance monitoring. Thanks to Phoenix's OpenInference instrumentation, Portkey can emit structured traces automatically — no extra setup needed. This gives you clear visibility into every LLM call, making it easier to debug and improve your app. * **1600+ LLM Providers**: Single API for OpenAI, Anthropic, AWS Bedrock, and more * **Advanced Routing**: Fallbacks, load balancing, conditional routing * **Cost Optimization**: Semantic caching, request * **Security**: PII detection, content filtering, compliance controls * **40+ Key Metrics**: Cost, latency, tokens, error rates * **Detailed Logs & Traces**: Request/response bodies and custom tracing * **Custom Metadata**: Attach custom metadata to your requests * **Custom Alerts**: Real-time monitoring and notifications With this integration, you can route LLM traffic through Portkey and gain deep observability in Arize Phoenix—bringing together the best of gateway orchestration and ML observability. ## Getting Started ### Installation Install the required packages to enable Arize Phoenix integration with your Portkey deployment: ```bash arize theme={"system"} pip install portkey-ai openinference-instrumentation-portkey arize-otel ``` ```bash phoenix theme={"system"} pip install portkey-ai openinference-instrumentation-portkey arize-phoenix-otel ``` ### Setting up the Integration First, set up the Arize OpenTelemetry configuration: ```python arize theme={"system"} from arize.otel import register # Configure Arize as your telemetry backend tracer_provider = register( space_id="your-space-id", # Found in Arize app settings api_key="your-api-key", # Your Arize API key project_name="portkey-gateway" # Name your project ) ``` ```python phoenix theme={"system"} from phoenix.otel import register # Configure Arize as your telemetry backend tracer_provider = register( space_id="your-space-id", # Found in Arize app settings api_key="your-api-key", # Your Arize API key project_name="portkey-gateway" # Name your project ) ``` Initialize the Portkey instrumentor to format traces for Arize: ```python theme={"system"} from openinference.instrumentation.portkey import PortkeyInstrumentor # Enable instrumentation PortkeyInstrumentor().instrument(tracer_provider=tracer_provider) ``` Set up Portkey with all its powerful features: ```python theme={"system"} from portkey_ai import Portkey # Initialize Portkey client portkey = Portkey( api_key="your-portkey-api-key", provider="@your-openai-portkey-provider" ) response = portkey.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": "Explain machine learning"}] ) print(response.choices[0].message.content) ``` ## Complete Integration Example Here's a complete working example that connects Portkey's AI Gateway with Arize Phoenix for centralized monitoring: ```python [expandable] theme={"system"} import os from openai import OpenAI from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders from arize.otel import register # OR from phoenix.otel import register # configure the Phoenix tracer from openinference.instrumentation.portkey import PortkeyInstrumentor # Step 1: Configure Arize Phoenix tracer_provider = register( space_id="your-space-id", api_key="your-arize-api-key", project_name="portkey-production" ) # Step 2: Enable Portkey instrumentation PortkeyInstrumentor().instrument(tracer_provider=tracer_provider) # Step 3: Configure Portkey's Advanced AI Gateway advanced_config = { "strategy": { "mode": "loadbalance" # Distribute load across providers }, "targets": [ { "provider":"@openai-prod", "weight": 0.7, "override_params": {"model": "gpt-4o"} }, { "provider":"@anthropic-prod", "weight": 0.3, "override_params": {"model": "claude-3-5-sonnet-latest"} } ], "cache": { "mode": "semantic", # Intelligent caching "max_age": 3600 }, "retry": { "attempts": 3, "on_status_codes": [429, 500, 502, 503, 504] }, "request_timeout": 30000 } # Initialize Portkey-powered client client = OpenAI( api_key="PORTKEY_API_KEY", base_url=PORTKEY_GATEWAY_URL, default_headers=createHeaders( config=advanced_config, metadata={ "user_id": "user-123", "session_id": "session-456", "feature": "chat-assistant" } ) ) # Make requests through Portkey's AI Gateway response = client.chat.completions.create( model="gpt-4o", # Portkey handles provider routing messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Explain quantum computing in simple terms."} ], temperature=0.7 ) print(response.choices[0].message.content) ``` Learn how to use Portkey's Universal API to orchestrate multiple LLMs in a structured debate while tracking performance and evaluating outputs with Arize. ## Portkey AI Gateway Features While Arize Phoenix provides observability, Portkey delivers a complete AI infrastructure platform. Here's everything you get with Portkey: ### 🚀 Core Gateway Capabilities Access OpenAI, Anthropic, Google, Cohere, Mistral, Llama, and 1600+ models through a single unified API. No more managing different SDKs or endpoints. Use the same code to call any LLM provider. Switch between models and providers without changing your application code. Secure vault for API keys with budget limits, rate limiting, and access controls. Never expose raw API keys in your code. Define routing strategies, model parameters, and reliability settings in reusable configurations. Version control your AI infrastructure. ### 🛡️ Reliability & Performance Automatically switch to backup providers when primary fails. Define fallback chains across multiple providers. Distribute requests across multiple API keys or providers based on custom weights and strategies. Configurable retry logic with exponential backoff for transient failures and rate limits. Set custom timeouts to prevent hanging requests and improve application responsiveness. Route requests to different models based on content, metadata, or custom conditions. Gradually roll out new models or providers with percentage-based traffic splitting. ### 💰 Cost Optimization Intelligent caching that understands semantic similarity. Reduce costs by up to 90% on repeated queries. Set spending limits per provider, team, or project. Get alerts before hitting limits. Set rate limits per provider, team, or project. Real-time cost tracking across all providers with detailed breakdowns by model, user, and feature. ### 📊 Built-in Observability Track 40+ metrics including latency, tokens, costs, cache hits, error rates, and more in real-time. Full request/response logging with advanced filtering, search, and export capabilities. Trace requests across your entire AI pipeline with correlation IDs and custom metadata. Set up alerts on any metric with webhook, email, or Slack notifications. ### 🔒 Security & Compliance Automatically detect and redact sensitive information like SSN, credit cards, and personal data. Block harmful, toxic, or inappropriate content in real-time based on custom policies. Fine-grained RBAC with team management, user permissions, and audit logs. Enterprise-grade security with SOC2 Type II certification and GDPR compliance. Complete audit trail of all API usage, configuration changes, and user actions. Zero data retention options and deployment in your own VPC for maximum privacy. ### 🏢 Enterprise Features SAML 2.0 support for Okta, Azure AD, Google Workspace, and custom IdPs. Multi-workspace support with hierarchical teams and department-level controls. 99.9% uptime SLA with dedicated support and custom deployment options. Deploy Portkey in your own AWS, Azure, or GCP environment with full control. ## Next Steps Discover all AI Gateway capabilities beyond observability Secure your API keys and set budgets Configure fallbacks, load balancing, and more Use Portkey's native observability features **Need help?** Join our [Discord community](https://portkey.sh/discord)