Respan
Route, track, and debug all LLM traffic.
About Respan
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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.
Pros
- Strong LLM observability: Fine-grained analytics make it much easier to understand where tokens, time, and errors are going.
- Quick integration paths: Many stacks only need a base URL change or a few decorators to start emitting traces.
- Provider flexibility: Support for several model vendors and speech-to-text APIs suits teams that like to experiment.
- Agent-friendly tracing model: Concepts such as workflows, tasks, agents, and tools line up well with modern agent architectures.
Cons
- Requires routing changes: Applications must adopt the gateway or SDK, which may feel heavy for very simple prototypes.
- Data governance questions: Security teams will want to review how prompts and outputs are stored and who can access logs.
- Pricing transparency: Public materials do not clearly show per-plan prices, which complicates early budgeting.