# Firedrill vs Raindrop AI: Are you checking a release or investigating live responses?

**Choose Firedrill when you need to exercise a refund failure before customers encounter it. Choose Raindrop AI when you need to find which production conversations already contain that failure and replay them against a change.** A support agent can say “I refunded you” after receiving a timeout. The reply alone leaves open whether the payment service committed the refund.

By Firedrill · November 12, 2026. Based on published product documentation.

## What each platform checks

| Decision | Firedrill | Raindrop AI |
| --- | --- | --- |
| Simulation | Start from a synthetic payment record and a saved scenario. The Stripe package declares a fault that commits a refund and loses the reply. [Stripe catalog](https://firedrill.run/tools?tool=stripe) | Instrument the agent to collect production traces. Its public beta Simulations product uses code and trace history to model dependencies and replay relevant conversations. [Simulations](https://www.raindrop.ai/simulate/) |
| Outcome scoring and hard rules | Count refund calls, inspect refund records and grade the response against the available evidence. [Evaluations](https://docs.firedrill.run/build/evaluations) | Define a signal for a behavior such as an unsupported refund claim. Issue Detection groups patterns across events and exposes the traces behind them. [Signals](https://www.raindrop.ai/signals/) · [Issue detection](https://www.raindrop.ai/issue-detection/) |
| Coding-agent setup | Your coding agent prepares the world. With your approval, the CLI starts or connects to the target locally. Optional remote MCP manages cloud resources and reads reports. [Run your agent](https://firedrill.run/run-your-agent.md) | Use supported tracing integrations. Coding agents can investigate through MCP. The beta PR workflow requires agent events, a supported GitHub agent repository and the Raindrop GitHub app. [Simulation setup](https://www.raindrop.ai/simulate/) |
| Synthetic services | Choose operations and faults from 32 synthetic tool families, including Stripe and Slack. [Service catalog](https://firedrill.run/tools) | The simulation harness builds stateful copies from the tools found in code and traces. Check the fidelity of the refund path your traces exercised. [Simulation environments](https://www.raindrop.ai/simulate/) |
| Pricing | Free includes 500 monthly credits. Starter is $29/month, Team $99/month and Scale $299/month. Enterprise is scoped separately. Your target agent’s model provider bills separately. [Pricing and credits](https://firedrill.run/pricing) | Hobby includes 1,000 events/month free. Pro starts at $299/month plus event charges. Enterprise pricing is custom. Confirm beta simulation access for your account. [Raindrop plans](https://www.raindrop.ai/#pricing) |

## When to choose each

**Choose Firedrill** when your prompt is “Can this agent safely recover if a refund succeeds and the acknowledgment gets lost?” You can establish the expected ledger state, introduce that fault and rerun the scenario after changing retry logic.

**Choose Raindrop** when your prompt is “Which customers heard that their refund succeeded, and did this release reduce those incidents?” The trace population matters as much as an individual verdict. A signal is only as informative as the payment evidence you capture.

## Check a refund promise against two ledgers

1. Create two labeled test interactions for a $40 payment. In A the payment system commits one refund and the read-back confirms it. In B the write fails and the read-back shows zero refunds. Have the agent say “I refunded $40” in both.
2. In Firedrill, seed the payment and record the resulting refund state for each drill. For Raindrop, instrument the same test harness and send both interactions, including the payment read-back, to a test project. Define an unsupported-refund signal there.
3. Expect the claim to pass in A and be flagged in B. Save the reply, refund ID or empty refund list, and each evaluator’s reason. If a system sees only a timeout, mark the outcome unassessable.
4. Change the agent to verify the ledger before claiming success. Repeat with the same inputs. Compare the verdicts for those two cases. Measure production prevalence separately against the event volume you actually observe.