Respan

Freemium

Respan is an AI infrastructure platform in the AI Agents space. It routes LLM requests and watches how AI agents behave in production.

You send all model calls through one API endpoint. It reaches over 1,000 models across providers and falls back automatically if a model errors or hits a rate limit.

It captures every LLM call, tool run, retrieval step, and agent turn as a trace. You see input, output, latency, and cost. Built-in evaluators score outputs using LLM judges, code checks, or human review.

Open it if you run production AI agents and need to see what is breaking. Skip it if you want simple logging or already have a working observability stack you trust.

It helps when you are past the prototype stage and hitting real problems.

• Teams building AI agents who need observability beyond traditional APM: Standard tools do not capture agent loops and tool calls. Respan tracks the full execution flow so you can debug what actually happened.
• Organizations running multiple AI frameworks in production: You have agents in LangChain, CrewAI, or custom code. One SDK auto-instruments 25+ frameworks so you do not wire up separate pipelines.
• Teams building AI agents facing reliability issues in production: Models fail at 2am. Automatic fallbacks and routing keep you from waking up to fix the same outage twice.
• Startups and enterprise teams needing to scale agent complexity: You started with one model. Now you have many. Respan handles the gateway and cost controls as you add providers.
• Developers spending significant time on manual bug reproduction: You get a full execution trace with memory and routing decisions. You see the prompt that caused the failure without asking the user what they did.
• Teams running LLM apps at scale needing high-throughput observability: The platform handles 30 million requests per day. Traces ingest async without blocking your app.
• Engineers designing log pipelines who want specific schema patterns: You get compact logs with typed columns and materialized views. Query tens of millions of rows in sub-second time.
• Teams running production agents at scale who have hit fragmentation pain: You wired together a gateway, an eval tool, and a dashboard that do not talk. Respan puts them in one place.
• AI teams running production LLM systems needing automated observability: An evaluation agent triggers when you change prompts or models. You catch regressions without running manual test suites.
• Teams wanting LLM-as-judge automated evaluation pipelines: You define quality rules once. The system scores production traffic against them and flags outliers.

These are the parts that matter after you have it running.

• End-to-end tracing of LLM calls, tool invocations, and agent loops: You see the full picture in one view. No more jumping between logs to reconstruct what the agent did.
• Automatic fallbacks when models fail: Your app keeps working even when a provider is down. You configure the backup chain once and stop babysitting uptime.
• Cost management to identify expensive prompts: You spot which calls burn the most money. This lets you optimize or cache before the bill surprises you.
• Automated evaluation triggers on meaningful changes: You change a prompt. The system runs evals against production samples and tells you if quality dropped. You ship faster with less fear.
• Multi-provider gateway with one API: You call one endpoint and reach 1,000+ models. Adding a new provider takes minutes, not days of integration work.
• Debugging with actual prompt and output review: You read the exact prompt that failed and the raw response. No guessing what the model saw.

Skip it if these limits matter to you.

• No verified user reviews on G2: You cannot read how other teams rank it. All claims come from the company itself.
• No pricing information available: You cannot estimate cost before talking to sales. This makes budgeting hard.
• No evidence the automated loop actually closes: The pitch is to go from evaluation to automatic fix. There is no public proof this works in practice.
• Integration with proprietary gateway: You route all traffic through Respan. Moving away later means rebuilding your pipeline.
• Company-provided metrics without independent verification: The scale and customer claims are self-reported. You take their word for traction and reliability.