The use case: how Causal deploys Sentry
Causal's engineering team uses Sentry for error monitoring and performance monitoring across its React/TypeScript frontend, Express and Go backend, and evaluation service. They combine automatic and custom instrumentation, dashboards, and distributed tracing to find and fix performance bottlenecks before users report them.
In the customer’s words
Sentry is particularly useful for us to understand problems that are hard to replicate locally.Other AI tools used by Causal
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Animation Digital NetworkConfirmed
AnthropicConfirmed
ArcadeConfirmed
AtlassianConfirmed
BasetenConfirmed
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Based on Sentry’s published case study. We check the public evidence; the results are reported by the vendor.
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- Nov 13, 2026
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On the backend, Andrew’s team recently hit a problem where a user’s model was taking longer than expected to evaluate and they couldn’t easily replicate the state of the data locally without considerable effort. With Sentry they were able to identify the slow transaction and see which phase of the dependency graph generation was causing the lag: