
Sentry is an Observability and AI Monitoring company founded in 2012 and based in San Francisco, United States. It has raised $216.5M in total funding, most recently a Series E in 2022 at a $3B valuation.
| Date | Stage | Amount | Valuation | Lead investors |
|---|---|---|---|---|
| May 4, 2022 | Series E | $90M | $3B | BOND, Accel |
| Feb 18, 2021 | Series D | $60M | $1B |
| Accel |
| Sep 24, 2019 | Series C | $40M | — | Accel |
| May 23, 2018 | Series B | $16M | $100M | New Enterprise Associates (NEA), Accel |
| Jun 23, 2016 | Series A | $9M | $40M | New Enterprise Associates (NEA) |
| Jul 22, 2015 | Seed | $1.5M | — | Accel |
Sentry's Error Tracking captures exceptions and crashes in real time across frontend, backend, and mobile applications. It provides full stack traces, breadcrumbs, and release tracking, enabling developers to triage and root-cause issues with 95% accuracy. Supporting 100+ languages and frameworks, it aggregates errors by fingerprint and surfaces the most impactful issues first, so engineering teams can prioritize what matters most.
Performance Monitoring traces distributed transactions across services to measure throughput, latency, and error rates. Developers can pinpoint slow database queries, N+1 selects, external API calls, and cache misses with flame graphs and span details. It integrates seamlessly with Error Tracking to provide a unified view of code health, helping teams optimize response times and prevent regressions before they reach users.
Session Replay records pixel-perfect video reproductions of user sessions, linked directly to errors and performance issues. Developers can watch exactly what a user saw and did before a crash or slow interaction, complete with console logs, network requests, and DOM snapshots. This product bridges the gap between raw error data and real user experience, making it dramatically faster to reproduce and fix frontend bugs.
Sentry Profiling provides continuous, low-overhead code-level profiling that correlates CPU and memory hot spots with errors and transactions. Developers can identify which functions consume the most resources in production without needing dedicated profiling infrastructure. The data surfaces alongside error and performance traces in a single dashboard, enabling engineers to optimize code efficiency while debugging issues.







