Respan

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About Respan

Respan focuses on LLM engineering, giving teams a central gateway and tracing layer for AI applications. It routes traffic to providers such as OpenAI, Anthropic, Google Gemini, AI21 Labs, and AssemblyAI, then tracks tokens, costs, latency, and error rates in a single view. By pairing gateway-based logging with an OpenTelemetry tracing SDK, it lets engineers inspect entire agent workflows, from high-level tasks down to individual model calls. Key Features: Unified LLM gateway: Route requests through one base URL while still choosing models across multiple AI providers and tools. Token, cost, and latency analytics: Dashboard views show token usage, per-request cost, latency distributions, and error rates across all calls. Tracing SDK with decorators: OpenTelemetry-based SDK for Python and JavaScript uses decorators such as @workflow and @task to capture end-to-end traces, auto-attaching LLM calls. Rich attribution metadata: Attributes like customer_identifier, trace_group_identifier, and custom metadata help teams slice metrics by user, project, experiment, or environment. Flexible logging modes: Teams can either proxy traffic through the gateway by switching the base URL or log requests asynchronously via a dedicated logging endpoint.

Key features

Unified LLM gateway
Token, cost, and latency analytics
Tracing SDK with decorators
Rich attribution metadata
Flexible logging modes
Support for multiple AI providers

Use cases

AI product teams monitoring production features that rely on GPT-style models and speech-to-text services across apps and services.
Data and platform engineers owning shared AI infrastructure, centralizing provider access, and wiring traces into existing observability stacks.
Academic labs experimenting with multi-model research agents

Pros

Unified gateway for routing LLM traffic across multiple providers (e.g., OpenAI, Anthropic, Google Gemini) via a single endpoint
OpenTelemetry-based tracing SDK for Python and JavaScript to inspect end-to-end agent workflows
Real-time monitoring of tokens, costs, latency, and error rates with customizable dashboards and alerts
Built-in response caching to reduce costs and latency for repeated requests
Flexible evaluation tools supporting LLM judges, deterministic checks, and human reviews for output scoring

Cons

Requires integration with existing SDKs or codebase modifications to leverage full tracing capabilities
Advanced features like caching and fallbacks may introduce complexity for smaller teams
Dependency on third-party LLM providers for model availability and performance

Frequently asked questions about Respan

What is Respan and what does it do?

Respan is an LLM engineering platform that provides a unified gateway for routing and observing AI model calls across multiple providers. It enables teams to trace, evaluate, and optimize AI workflows using OpenTelemetry-based tracing and analytics.

Who should use Respan?

Respan is designed for engineering teams building AI applications who need centralized routing, observability, and evaluation of LLM calls across providers like OpenAI, Anthropic, Google Gemini, and others.

How does Respan handle model failures or rate limits?

Respan supports automatic fallbacks, allowing teams to list alternative models to switch to if the primary model errors or hits rate limits, ensuring continuous availability.

Can Respan cache responses to reduce costs and latency?

Yes, Respan allows caching of requests and responses, serving repeat calls instantly to cut costs and latency without reprocessing the request.

Does Respan support budget and rate limit controls?

Respan enables setting budgets and rate limits at various scopes, such as per API key, customer, or organization-wide, with alerts to prevent overspending.

How can I get started with Respan?

Teams can start by integrating Respan’s SDK (Python or JavaScript) or routing traffic through its gateway endpoint, then use its dashboard to monitor, evaluate, and optimize their AI workflows.