Sentry

Unternehmensdaten

Offizieller Name

Functional Software, Inc.

Einheitentyp

COMPANY

Marktrolle

B2B SaaS Provider

Offizielle Website

sentry.io

Was Sentry macht

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.

Strategische Einordnung

KI-gestützte Einordnung aus der bestehenden Unternehmensrecherche; Interpretation und belegte Fakten sind zu unterscheiden.

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.

Geschäftsmodell und Monetarisierung

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

Produkte und Fähigkeiten

Für diese Ansicht liegen keine Produkte mit zugeordneten Quellen vor.

Produkte und Marktkategorien

Zuletzt erfasste Signale

Datumsangaben beziehen sich auf die Quellenveröffentlichung. Ältere Einträge sind historischer Kontext, kein Beleg für ein neues Ereignis.

• Sentry Blog: New Articles on Application Metrics, dataCollection, and Session Replay Evaluation

  2026-09-01blog.sentry.io

  Erfasster 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

  2026-08-30dev.to

  Large Language Models (LLM) & AI · Erfasster 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

  2026-08-26dev.to

  Large Language Models (LLM) & AI · Erfasster 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

  2026-08-22dev.to

  Large Language Models (LLM) & AI · Erfasster 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

  2026-08-21dev.to

  Large Language Models (LLM) & AI · Erfasster 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.

Unternehmensbeziehungen vertiefen

• Wettbewerber und Alternativen
• Partner und Integrationen
• Akquisitionshistorie
• Eigentumsverhältnisse
• Ähnliche Unternehmen

Fragen zu Sentry

Quellen und Datenabdeckung

Mit Sentry weiterarbeiten

Explorer bietet zusätzliche Unternehmensdetails, eine Watchlist für bis zu 25 Unternehmen und deinen persönlichen Strategic Intelligence Agenten. Er analysiert deine Märkte täglich – und liefert dir bei Neuigkeiten ein maßgeschneidertes Briefing mit strategischer Einordnung statt Informationsflut.

Kostenlos und ohne zeitliche Begrenzung.