Portkey-AI/gateway

Features

• AI Request Routing - Distributes requests across multiple AI models using load balancing, fallbacks, and retries to ensure high availability.
• LLM Middleware - Functions as a middleware layer that standardizes requests, manages provider credentials, and enforces security guardrails for AI applications.
• AI Model Load Balancers - Distributes requests across multiple AI model providers using load balancing and automated failover to ensure high availability.
• AI Governance Tools - Manages organizational access, security policies, and data privacy requirements for large-scale AI deployments.
• AI Guardrails - Enforces real-time safety checks and sensitive data redaction to protect AI applications from malicious or non-compliant interactions.
• AI Observability Tracing - Provides a centralized system for tracing request lifecycles, logging model interactions, and analyzing usage costs across teams.
• Prompt Management Systems - Offers a centralized repository for versioning, testing, and organizing prompt templates to ensure consistent engineering workflows.
• Model Request Proxies - Intercepts and routes API requests to interchangeable language model providers with centralized authentication and logging.
• Model Safety Filters - Validates model inputs and outputs against security checks and prompt filters to prevent harmful content.
• Virtual Key Managers - Manages virtualized API credentials with usage tracking and rotation for multi-tenant AI environments.
• Agent Prompt Templates - Provides centralized management for prompt versioning and template libraries to streamline agent development workflows.
• AI Application Monitoring - Tracks request-level cost, latency, and error rates to provide visibility into how AI applications behave in production.
• Semantic Caching Systems - Stores and retrieves model outputs based on semantic request content to reduce latency and operational costs.
• AI Usage Analytics - Tracks, attributes, and optimizes financial expenditure and performance metrics across various language models.
• Response Caching - Stores and retrieves previous model outputs for identical or semantically similar queries to reduce latency and operational costs.
• Safety-based Routing - Directs traffic based on guardrail results by denying risky requests or switching to alternative models.
• API Gateway Security - Provides centralized proxy-based security and authentication management for AI model interactions.
• Data Redaction Tools - Automatically strips sensitive information from outgoing AI model requests to ensure data privacy.
• Model Access Controls - Controls access to specific AI models and providers to manage costs and compliance across teams.
• Model Access Governance - Manages user permissions, team budgets, and role-based access across organizational levels to ensure secure and governed model usage.
• Security Guardrails - Applies real-time security filters and validation logic to request and response payloads to ensure compliance.
• Resilient Deliberation Strategies - Maintains service continuity through automated retries and fallback mechanisms between different AI models.
• Request Interception Middleware - Intercepts incoming API traffic to perform authentication, logging, and policy enforcement before forwarding to model providers.
• AI Cost Monitoring - Tracks and categorizes standardized cost metrics across AI models by application or team to assist with budget planning.
• Usage Limiters - Caps financial expenditure and request frequency per API key to prevent resource exhaustion.
• Model Context Protocol - Provides a standardized protocol interface for connecting AI models to local data sources and external tools.
• MCP Server Management - Provides a centralized control plane for authenticating and monitoring access to protocol servers.
• AI Gateways - High-performance gateway for routing to numerous LLMs with guardrails.
• API And Routing - Fast gateway for routing and managing AI guardrails.
• Application Development - Unified API gateway for managing requests to multiple models.
• Infrastructure and Gateways - Fast AI gateway for routing to multiple LLMs.
• Privacy and Safety - AI gateway with integrated guardrails for model safety.
• Private Infrastructure Management - Supports deployment within private networks or cloud environments to ensure full data ownership and operational control.
• Traffic Routing Policies - Distributes requests across multiple model endpoints using conditional logic and load balancing to ensure high availability.
• Agent Execution Tracing - Records the full lifecycle of agent interactions and request journeys to simplify debugging and identify bottlenecks.
• Observability Pipelines - Aggregates logs, traces, and performance metrics from diverse AI workflows into a standardized format for auditing.
• System Audit Trails - Maintains comprehensive logs of all model interactions and feedback data to ensure compliance and operational transparency.
• Agent Input and Output Validators - Inspects and validates agent responses in real time to ensure content standards and prevent data leaks.
• Agent Framework Integrations - Connects prompt management workflows with external agent orchestration tools to streamline complex AI application development.
• Human Feedback Collection - Gathers structured qualitative and quantitative user feedback at the request level to evaluate response quality.
• Prompt Registries - Centralizes prompt storage to facilitate versioning, A/B testing, and rapid iteration across multiple models.
• Prompt Templates - Groups prompts into shared templates to enable team-wide access and consistent prompt engineering workflows.
• Data Encryption - Secures data at rest and in transit using cryptographic standards and customer-managed keys.
• Network Access Control - Filters incoming requests based on IP addresses to enforce infrastructure access security.
• Activity Monitors - Records logs of configuration changes and user actions across workspaces to ensure accountability and compliance.
• Logging and Telemetry - Records interactions with language models and tools, including parameters and responses, to provide visibility into usage patterns.
• Model Fine-Tuning - Improves response quality by applying provider-specific fine-tuning configurations through a consistent interface.
• Model Comparison Tools - Runs prompts across multiple language models simultaneously to analyze output quality and performance in a unified environment.
• Data Governance - Enforces data privacy and compliance through isolated storage and custom retention policies.
• Integration Layers - Connects disparate agent frameworks and tools through a standardized interface for consistent interaction across environments.
• Traffic Isolation - Segregates operational metrics from sensitive request content to ensure anonymized data handling.
• Audit Log Filters - Isolates specific events by resource, user, or network origin to enable rapid incident investigation and security auditing.
• Automatic Tracing Instrumentation - Captures logs, traces, and metrics from language model frameworks automatically without requiring manual integration code.
• Standardized Interfaces - Exposes a standardized interface mirroring industry SDKs to facilitate integration with existing development tools.

Historique des stars