Respawn Review: Pricing, Features & Alternatives

Ship through one gateway, not a mess of moving parts.

Promote prompts, models, and workflows straight from the UI into production, with version control, rollout logic, and access to 500+ models through one gateway. This streamlined approach simplifies the deployment process, allowing users to manage everything from a single interface.

One API for every model

Route OpenAI-style calls through Respan to 500+ models, or keep each provider’s native SDK on a passthrough endpoint—every request is logged. This flexibility allows users to choose their preferred integration method while maintaining comprehensive tracking.

Stay up when models fail

If a model errors or rate-limits, try the next model in your fallback list, balance load across keys, and retry with backoff from one place. This feature ensures continuous operation by automatically managing model failures, enhancing reliability.

Control spend and reuse answers

Set soft warnings or hard caps per API key, get Slack or email alerts when a threshold crosses, and cache repeat prompts to cut cost and latency. This feature helps users manage their API usage effectively, reducing unnecessary expenses.

Know when production shifts - and act before it spreads.

Dashboards for LLM usage, slice metrics by model or user, and get notified when cost, latency, errors, or tokens cross the line you set. This proactive monitoring allows users to address issues before they escalate.

Track usage on one dashboard

See requests, tokens, errors, latency, and cost in one place—broken down by model, API key, and the traffic your product sends through Respan. This consolidated view simplifies tracking and analysis of usage patterns.

Slice metrics by model or user

Switch the same dashboard by model or user to spot spikes, compare traffic, and see which features or keys are driving volume and spend. This feature enhances the granularity of monitoring, allowing for targeted insights.

Alert when thresholds breach

Monitor error rate, cost, latency, or tokens over a window—and notify Slack, email, or a webhook when production crosses the limit you set. This feature ensures that users are promptly informed of any critical issues.

Turn judgment into a system.

Build evaluation workflows that combine human review, code checks, and LLM judges in one flow - all measured against the metrics that actually matter. This structured approach to evaluation enhances the reliability of assessments.

Compose one evaluation flow

Run fast rule checks, LLM judges, and human review in the same workflow—so code, rubric, and ground-truth grading live in one evaluation system. This integration streamlines the evaluation process, making it more efficient.

Score live traffic automatically

Run the same evaluators on sampled production requests so quality issues show up on real spans, not only in offline tests. This feature ensures that evaluations reflect actual performance, enhancing accuracy.

Test before you ship

Build a dataset from production traces or a CSV, run experiments across prompt and model variants, and compare scores before merge. This capability allows users to validate changes before deployment, reducing risks.

Alert when production scores drop

Watch evaluator scores such as faithfulness over a rolling window and trigger alerts when they fall below your threshold—before users report the issue. This proactive alerting helps maintain quality standards.

Know exactly what your agents did.

Every prompt, tool call, and response - captured with rich context from real production traffic. This comprehensive tracing allows users to understand agent behavior in detail, facilitating debugging and optimization.

Online evals on production traffic

Run the same evaluators on sampled production logs so scores like faithfulness and json_schema show up on real spans—not only in offline tests. This feature enhances the relevance of evaluations by using live data.

Reproduce and inspect real sessions

Group related messages in a thread view and see how each turn ties back to spans in the trace—so you keep session context when agents branch or retry. This capability aids in understanding user interactions and agent responses.

Iterate on prompts, tools, and routing without losing control.

Track every change, compare what actually improved, and keep optimization tied to real production signals. This feature allows users to refine their systems based on actual performance data, enhancing effectiveness.

Version every moving part

Track prompt, tool, model, and workflow changes so you always know what changed, when, and why. This versioning capability provides clarity and accountability in the optimization process.

Compare changes against real baselines

Test new prompt versions, tool behavior, and routing logic against prior versions using the same product data and evaluation criteria. This comparative analysis helps identify effective changes and improvements.

Improve the system, not just the prompt

Optimize across prompts, tools, and orchestration together instead of treating each change like an isolated experiment. This holistic approach to optimization enhances overall system performance.