Respan

Overview

Respan is an LLM engineering platform that unifies observability, evaluations, prompt optimization, and a unified LLM gateway in a single system. It enables engineering teams to route, observe, and evaluate every LLM call through one endpoint, providing end-to-end visibility and control over AI applications in production. By combining model routing, tracing, monitoring, and automated evals, Respan serves as a control plane for building reliable, cost-efficient LLM-based products.

The gateway gives developers a single API to reach 1,000+ models across every major provider, with the ability to switch between models by changing a single parameter. Automatic fallbacks ensure that if one model errors or rate-limits, the next model in the list takes over, preventing outages. Response caching serves repeated requests instantly, cutting cost and latency to near zero. Budgets and rate limits can be set per key, per customer, or org-wide, with warnings and blocking as limits approach. For quality, Respan's evals framework lets teams compose LLM judges, deterministic code checks, and human reviewers into one evaluator that scores any output. Production traffic can be sampled by filter and rate, or datasets can be uploaded as CSV, so test cases reflect real user requests. Teams can run model or prompt versions against a dataset and compare score distributions side-by-side before shipping.

Respan provides real-time monitoring of requests, errors, cost, latency, and tokens across all traffic, sliced by model, key, or user, with alerts delivered to Slack, email, or webhooks when thresholds are breached. Its tracing feature captures every LLM call, tool run, retrieval, and agent turn as a span in a single trace, displaying the exact input, output, latency, cost, and token usage of each step. This makes debugging complex agent workflows fast and precise. The platform is trusted by teams who have scaled to trillions of tokens, and it integrates with popular frameworks such as OpenAI SDK, Vercel AI SDK, LangChain, LlamaIndex, and Python/JS/TS SDKs. It is also built to meet enterprise and healthcare security standards, including ISO 27001, SOC 2, GDPR, and HIPAA with BAA available.

Respan is designed for AI agents and production LLM applications. Whether you are building support agents, copilots, or complex multi-step systems, Respan helps you observe behavior, evaluate quality, optimize prompts, and control spend. From early prototyping to hundreds of millions of API calls per month, it provides the debugging layer and infrastructure needed to ship AI products with confidence.

Core capabilities

Unified LLM Gateway

One API endpoint for 1,000+ models with automatic fallbacks, response caching, and spend limits across all major providers.

Tracing

Every LLM call, tool run, retrieval, and agent turn captured as spans with input, output, latency, cost, and token usage.

Evals

Compose LLM judges, deterministic code checks, and human reviews into a single evaluator that turns any output into a score.

Production Sampling

Pull real requests from logs by filter and sampling rate, or upload CSV files, to build realistic test sets.

Model and Prompt Comparison

Run models or prompts against datasets and compare score distributions side-by-side before shipping.

Monitoring and Alerts

Real-time dashboard for requests, errors, cost, latency, and tokens, with threshold-based alerts to Slack, email, or webhooks.

Budgets and Limits

Set budgets and rate limits per API key, customer, or organization, with warnings and blocking as limits approach.

Caching

Serve repeat requests from cache to reduce cost and latency to near zero, with configurable expiry and hit tracking.

Security and Compliance

SOC 2, ISO 27001, GDPR, and HIPAA compliant, with BAA available for healthcare teams.

Framework Integrations

Native support for OpenAI SDK, Vercel AI SDK, LangChain, LlamaIndex, Python/JS/TS SDKs, and more.

Use cases

Unifying access to multiple LLM providers behind one gateway with automatic failover

Debugging multi-step agent workflows with end-to-end trace spans

Evaluating prompt and model versions against production-quality datasets

Monitoring cost, latency, and error rates across large-scale LLM traffic

Building and optimizing AI support agents, copilots, and voice assistants

Reducing LLM spend and latency with response caching and budgets

Maintaining compliance in regulated industries with SOC 2, HIPAA, and GDPR support