The use case: how Intelligence Fusion deploys Sentry
Intelligence Fusion uses Sentry for error monitoring and, more recently, performance monitoring, tracing and profiling across its PHP, Go and TypeScript services. The engineering team uses it to find and fix slow endpoints and database queries in its threat intelligence platform and REST APIs.
More reported results
Figures published in Sentry’s case study. They are not independently measured by usedby.
API 1 (threat actors) average latency decreased from 1820ms to 23.8ms
API 2 (static assets) average latency decreased from 60.7ms to 24.1ms
API 3 (authorization) average latency decreased from 105ms to 24.9ms
Ultimately, average time decreased from 17.5 seconds to 1.2 seconds – making their API response time 13x faster.
In the customer’s words
Sentry has always been our go to tool for critical error handling to ensure we continue to deliver reliable and robust software. As Sentry has evolved its offering, it has allowed us to not only scale our microservice architecture horizontally but vertically, as Profiling and performance metrics let us dive deeper into our applications to ensure we provide an optimal level of performance for our customers.Other companies using Sentry
Customers across industries, with public evidence.
Animation Digital NetworkConfirmed
AnthropicConfirmed
ArcadeConfirmed
AtlassianConfirmed
BasetenConfirmed
BitnamiConfirmedGo straight to the source
Based on Sentry’s published case study. We check the public evidence; the results are reported by the vendor.
sentry.io- Last checked
- Nov 13, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 13xChecked
Printed word for word on the page, near the name of Intelligence Fusion.
- The passage quoted aboveChecked
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- Intelligence Fusion uses SentryChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
The slowest endpoint, their country data endpoint, initially took 17.5 seconds to return. Having identified the database query as the biggest bottleneck from the flame graph, they optimized the endpoint in several ways