Respan

AI gateway with built-in observability, evaluations, and security testing for agent routing and monitoring.

Overview

Respan is an AI gateway that routes requests through 1,000+ models while providing built-in observability, automated evaluations, and security testing. It enables developers to trace every LLM call, tool run, and agent step, monitor costs and performance metrics in real-time, and evaluate outputs against custom scoring criteria before and after deployment.

The platform combines a unified API endpoint for model access with comprehensive monitoring dashboards, alert systems, and tracing capabilities. It supports automatic fallbacks between models, request caching, spending limits, and PII masking. Respan is designed specifically for AI agents and agentic workflows, helping teams scale from millions to billions of monthly API calls while maintaining visibility and control.

Key features

1,000+ model routing through unified API
Distributed tracing for LLM calls and agent steps
Automated evaluations with LLM judges and code checks
Real-time monitoring dashboards and alerts
Request caching and automatic fallbacks
Spending limits and rate limiting
PII masking and security testing
Multi-framework integration (LangChain, Claude, OpenAI, etc.)

Pros

Easy integration with existing SDKs and frameworks
Comprehensive observability across entire agent workflows
Flexible evaluation system combining AI judges, code checks, and human review
Automatic model fallbacks ensure high availability
Cost optimization through caching and spending controls
Enterprise-grade security certifications (SOC 2, HIPAA, GDPR)

Cons

Free tier has limited logs (100k) and scores (1k)
Pricing increases significantly for high-volume usage
Requires integration setup for each framework
Enterprise features require custom pricing

Use this if

You need to route requests across multiple AI models, require comprehensive observability for production AI agents, want automated evaluation of LLM outputs, or need cost monitoring and optimization for high-volume API usage.

Skip this if

You only use a single LLM provider, don't need detailed tracing and monitoring, or are building simple chatbot applications without complex agent workflows.

Best for

AI agent development and deploymentMulti-model routing and fallbackProduction observability for LLM applicationsAutomated evaluation of agent outputsCost and usage monitoringReal-time error detection and alerting

Alternatives

LangSmithLangFuseHeliconeBraintrustPortkey

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