Respan

Respan Key Insights

What is Respan?

Respan is a unified AI observability and LLM engineering platform built for teams shipping AI agents and LLM-powered products in production. It captures full execution traces across every prompt, tool call, routing decision, and memory state, giving engineering teams complete visibility into how their agents actually behave at scale. 

The platform runs automated workflow-level evaluations, surfaces root causes, recommends fixes, and lets teams push prompt and model changes directly from the UI without touching code. Backed by Y Combinator and Gradient Ventures with $5 million in seed funding, Respan processes over 80 trillion tokens and serves hundreds of startups and enterprise teams globally. For any AI engineering team tired of guessing why their agent broke in production, Respan is the answer.

Key Features of Respan

Production Agent Tracing and Session Replay

Respan captures every LLM call, tool invocation, and memory state in a single trace view. Engineers can group related messages into thread views and map each turn back to its corresponding span, which makes reproducing bugs from live traffic a matter of seconds rather than hours. For teams running complex multi-step agents, this eliminates the black-box problem entirely.

Self-Driving Evaluation Workflows

Respan combines code-based rule checks, LLM judge graders, and human-in-the-loop review into one unified evaluation pipeline. The platform scores live production traffic automatically using the same evaluators you build offline, so quality regressions surface on real spans before users ever notice. This is the feature that separates Respan from basic logging tools.

Unified LLM Gateway with 500+ Models

The Respan gateway routes OpenAI-compatible API calls to over 500 LLM providers through a single endpoint. It handles model fallback, retry with backoff, load balancing across API keys, and response caching to cut both latency and cost. Teams get full spend control with per-key caps and Slack or email alerts when thresholds are crossed.

Prompt and Model Version Control

Every change to a prompt, tool config, model selection, or workflow logic is versioned inside the platform. Teams can run A/B experiments against production baselines, compare eval scores across versions, and promote winning changes through the gateway without a code deploy. This closes the loop between evaluation findings and actual production improvements.

Real-Time Dashboards and Anomaly Alerting

Respan's monitoring layer tracks request volume, token usage, latency, error rates, and cost in one dashboard, sliceable by model, API key, or user segment. Alerts fire to Slack, email, or a webhook when any metric crosses a defined threshold. For teams processing millions of calls per hour, this level of visibility is not optional.

Respan Pricing Plans

Who Uses Respan in Production?

Respan has earned strong adoption among AI-native companies at scale. Retell AI used it to scale from 5 million to 500 million monthly API calls while resolving production issues 10 times faster. Mem0's CTO credits Respan for enabling reliable scaling to trillions of tokens with real-time observability.

Teams at AlphaSense, Gumloop, Lovable, and Finta have all publicly praised the developer experience and the metrics dashboard as standout strengths.

Respan vs the Competition: The Core Edge

Respan's biggest structural advantage over tools like LangSmith or Datadog is the closed loop between evaluation and production action.

Most observability tools stop at showing you what went wrong. Respan goes further by converting evaluation results into concrete changes like prompt updates and regression checks that teams can deploy straight from the platform. That self-driving loop is what makes it genuinely different from every other tool in this category.

Pros and Cons

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