Sentry
Developer observability, error tracking and AI debugging software.
Available information varies by company and source.
Profile record updated:
Company facts
- Official name
- Functional Software, Inc.
- Entity type
- COMPANY
- Market role
- B2B SaaS Provider
- Official website
- sentry.io
What Sentry does
Sentry operates a recurring-revenue SaaS model built around developer observability. It provides a hosted platform that ingests application telemetry, surfaces incidents and performance issues, and integrates into engineering workflows such as CI/CD, source control and pull requests. Value is created by reducing time to detect and fix production issues, improving software reliability and expanding account usage as customers monitor more services, environments and development teams.
Category differentiation
This company is a developer observability software vendor, not a cyber security monitoring provider or a generic IT services firm. It sells engineering tooling for software reliability, debugging and performance analysis.
Strategic context
AI-supported assessment from the existing company research; distinguish interpretation from sourced facts.
Functional Software, Inc., trading as Sentry, is a private B2B SaaS company that provides developer observability software. Its core platform combines error monitoring, application performance monitoring, distributed tracing, logs, session replay, metrics and profiling in a single system used by engineering teams to detect, diagnose and resolve software issues across web, mobile and backend environments. The company sells cloud software to software engineering teams, DevOps teams, site reliability engineers and larger product engineering organisations. It generates revenue through tiered SaaS subscriptions and consumption-based pricing tied to processed events, transactions and logs, with larger customers buying custom enterprise contracts. Sentry has expanded beyond core monitoring into AI-assisted debugging and adjacent developer tooling through acquisitions including Codecov and Emerge Tools.
Company news briefing
Briefing updated:
Following recent security concerns regarding 'ghostjacking' vulnerabilities where error reports and logs were exploited as command injections for AI agents, Sentry has continued to refine its core observability platform. The company recently introduced a centralized 'dataCollection' control panel, updated application metrics frameworks, and expanded its integration within the Sentry AI Suite to address modern software debugging and telemetry challenges.
Business model & monetisation
Sentry monetises through tiered SaaS subscriptions combined with usage-based billing. Free, Team, Business and Enterprise plans govern access, while charges scale with event volume, transactions, logs, users and feature entitlements. When customers exceed plan quotas, overage fees apply. Larger customers purchase negotiated enterprise agreements tied to scale, support and governance requirements.
- Platform subscriptions
- Software Subscription
- Usage overages on telemetry volumes
- Pay-per-Use
- Enterprise contracts with custom pricing and support
- Software Subscription
Products & capabilities
No products with linked sources are available in this view.
Products & market categories
Technology
Recent recorded signals
Dates refer to the source publication. Older entries are historical context, not evidence of a new event.
Sentry Blog: New Articles on Application Metrics, dataCollection, and Session Replay Evaluation
Recorded impact score: 3/5
New blog posts include 'Application Metrics caught my broken size estimator', 'From one switch to a control panel: meet dataCollection', and 'How to evaluate session replay software: a developer's guide'.
Live API specs for coding agents
Large Language Models (LLM) & AI · Recorded impact score: 2/5
Jonas Gauffin published a technical post describing docs-mcpserver, a tool that fetches an OpenAPI specification from a running service, caches it, and serves API documentation one operation at a time. The approach lets coding agents request only the single path/operation they need (reducing token usage) and keeps a last-known-good cached spec so frontend development can continue even if the backend is offline. The article includes a minimal configuration example, an installation command, and links to the project's GitHub repository and npm package.
- Jonas Gauffin published the article on August 30, 2026.
- docs-mcpserver fetches an OpenAPI spec from a running service, caches it, and serves it one operation at a time.
Coastline Index Releases llms.txt and JSON APIs
Large Language Models (LLM) & AI · Recorded impact score: 1/5
Coastline Index published a machine-readable GTA VI reference dataset designed for LLMs and developer tools, exposing an XML sitemap, an llms.txt file, and JSON APIs for verified entities (release, platforms, editions, characters, Leonida region, official videos). The dataset includes only Rockstar-confirmed information and provides a public data update log and multilingual front-end to help tools and chatbots consume a small, vetted corpus instead of speculative sources. The project's site is available at coastlineindex.com.
- Coastline Index launched a machine-readable GTA VI reference layer with llms.txt, an XML sitemap, and JSON APIs.
- The dataset includes structured entities such as release, platforms, editions, characters, the Leonida region, and official videos.
How to Spot and Fix Hacked AI Accounts
Large Language Models (LLM) & AI · Recorded impact score: 2/5
This technical how-to explains how to detect, verify, contain, and harden AI accounts after a suspected compromise. It lists red flags (unfamiliar logins, API usage spikes, rogue keys, unexpected projects, altered prompt histories, and billing anomalies), provides a step-by-step audit process (login export, API activity review, permissions and billing checks, integrity verification, and contacting platform support), and recommends immediate containment and long-term controls (revoke tokens, enforce MFA, adopt zero-trust networking, usage caps, backups, team education, and continuous monitoring). The article includes a short promo for Sentry’s MCP and Cursor debugging capabilities.
- The article provides a checklist of red flags indicating a compromised AI account, including unfamiliar logins, API usage spikes, new API keys, unexpected projects, altered prompt histories, and unexplained billing charges.
- A recommended verification audit includes exporting login CSVs, scrutinizing API endpoints (example: /v1/fine-tunes), reviewing permission matrices, checking billing dashboards, running integrity checks, and contacting platform security support.
Making Local AI Tool Calls More Reliable
Large Language Models (LLM) & AI · Recorded impact score: 2/5
A developer describes a simple, programmatic recovery pattern to make local AI agent tool calls more reliable. The author observed that allowing models to auto-select tools sometimes caused them to skip required tool calls. The fix: attempt automatic selection first, and if a local-data request returns no tool call, retry once with tool_choice="required" (only before any tool runs) and then fall back to auto. The flow was tested with automated regression tests and a running local Gemma model; streaming output is buffered to avoid showing incorrect answers during recovery. The post was published on DEV Community on 2026-08-21.
- Alain Chan published the article on DEV Community on 2026-08-21.
- The author implemented a recovery flow for tool selection: auto -> required -> auto.
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Questions about Sentry
What is Sentry?
Sentry is a B2B SaaS platform for developer observability, combining error tracking, APM, tracing, logs, session replay and AI-assisted debugging.
Who uses Sentry?
Software engineering teams, DevOps teams, site reliability engineers and enterprise product engineering organisations use Sentry to monitor and debug applications.
How does Sentry make money?
Sentry makes money through tiered SaaS subscriptions, usage-based charges tied to telemetry volume, and custom enterprise contracts.
Sources & coverage
This profile uses public, official and technically observable information. Missing information does not prove that a product or relationship does not exist. The list below does not imply that every profile statement has been verified.
14 publicly documented primary sources and citations linked across the market graph.
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