View Past PerformanceDatadog バランスシートの健全性財務の健全性 基準チェック /56Datadogの総株主資本は$4.0B 、総負債は$984.5Mで、負債比率は24.7%となります。総資産と総負債はそれぞれ$7.0Bと$3.0Bです。主要情報24.69%負債資本比率US$984.50m負債インタレスト・カバレッジ・レシオn/a現金US$4.76bエクイティUS$3.99b負債合計US$2.96b総資産US$6.95b財務の健全性に関する最新情報更新なしすべての更新を表示Recent updatesお知らせ • May 10Datadog, Inc. Provides Earnings Guidance for the Second Quarter and Full Year 2026Datadog, Inc. provided earnings guidance for the second quarter and full year 2026. For the second quarter. the company expects Revenue between $1.07 billion and $1.08 billion. For the full year, the company expects revenue between $4.30 billion and $4.34 billion.お知らせ • May 02Datadog, Inc., Annual General Meeting, Jun 15, 2026Datadog, Inc., Annual General Meeting, Jun 15, 2026.お知らせ • Apr 24Datadog Announces GPU Monitoring to Help Businesses Optimize Spend and Performance as They Aim to Scale AI ProjectsDatadog, Inc. announced that GPU Monitoring is available to customers everywhere. The new product addresses one of the most prevalent issues facing organizations as they look for a scalable and effective way to manage expanding AI costs. The launch of GPU Monitoring marks one of the first times a single solution provides unified visibility across the AI stack—giving customers a single view linking GPU fleet health, cost, and performance directly to the teams relying on them for faster troubleshooting of slow workloads and cost savings. Most GPU tools provide high-level device health metrics, but they don’t surface cross-functional resource contention issues, explain why training and inference workloads fail, or provide visibility into which devices are idle or ineffectively used. This lack of visibility slows down investigations and means that teams overprovision as the safest default—leading to wasted spend. GPU Monitoring streamlines this work by linking fleet telemetry directly to the workloads consuming those resources, and gives platform engineering and machine learning teams a shared view to investigate together, enabling them to: Scale AI without overspending: With visibility and forecasting based on the usage patterns of fleets and direct guidance on whether to buy new GPUs or free up existing ones, platform teams avoid expensive purchases and long procurement cycles, machine learning teams get capacity faster, and leadership gets better ROI with predictable spend. Accelerate AI delivery: Stalled workloads are correlated directly to the underlying GPUs, pods and processes running them so that teams can troubleshoot performance bottlenecks in minutes instead of hours, allowing engineers to focus on shipping AI projects. Avoid costly disruptions: Unhealthy GPUs are proactively identified before failures cascade across a cluster and cause training and inference delays. Maximize ROI on GPU spend: Teams are empowered and accountable for their GPU utilization and costs, and can easily pinpoint where they are overserving or underutilizing their GPUs. This allows teams to reclaim and reallocate resources in order to reduce wasted spend. GPU Monitoring is now generally available.お知らせ • Apr 17Datadog, Inc. to Report Q1, 2026 Results on May 07, 2026Datadog, Inc. announced that they will report Q1, 2026 results Pre-Market on May 07, 2026お知らせ • Apr 03Datadog Inc. Announces Datadog Experiments AvailabilityDatadog, Inc. announced that Datadog Experiments is available to customers everywhere. The new product enables teams to design, launch, and measure product experiments and A/B tests directly within the Datadog platform—giving teams the data and insights they need to understand how every change affects user behavior, application performance and business outcomes. Datadog solves this problem with the first experimentation platform that combines business metrics from a customer’s data warehouse with product analytics events and application observability. Powered by Datadog’s acquisition of Eppo, Datadog Experiments pairs best-in-class statistical methods with real-time observability guardrails so companies can test what matters, move quickly and ship with confidence. The product empowers every product manager, designer and engineer at a company to take a measured approach to change. Datadog Experiments enables teams to accelerate decisions without the overhead: Experimentation is self-serve and standardized, so teams can move from insight to decision without coordination overhead. Run safer, higher-quality experiments: Built-in guardrails, real-time feedback and shared standards help teams catch issues early, protect users and keep experiments valid. Make decisions leaders trust: Results are credible, reproducible and comparable by measuring impact directly against source-of-truth business metrics in native data warehouses, using consistent methodologies teams can audit and trust. By tying experiments to Real User Monitoring (RUM), Product Analytics, APM and logs, organizations can measure both business impact and performance implications to reduce risk without slowing innovation. Datadog Experiments is now generally available.お知らせ • Mar 25Datadog, Inc. Announces Availability of Bits AI Security AnalystDatadog, Inc. announced that Bits AI Security Analyst is available to customers everywhere. As part of Datadog’s Cloud SIEM, the AI agent reduces investigations that can take analysts hours down to as little as 30 seconds. Bits AI Security Analyst solves these issues by pairing the expertise of a senior SOC analyst with machine scale and speed, enabling investigation analysis across a breadth and volume of data sources that would be unachievable by a human, while still delivering high-accuracy verdicts backed by real-world context. This allows analysts to scale their investigation expertise so they can focus more time on high-impact defense priorities. When using other SIEMs, it can take teams hours to acknowledge alerts, run investigations, gather evidence, analyze results and escalate if needed. With Bits AI Security Analyst, teams using Datadog Cloud SIEM can autonomously complete all those steps in minutes, reducing the mean-time-to-resolution by more than 90%. Bits AI Security Analyst helps security teams: Detect and resolve issues faster: Autonomous investigations reduce alert fatigue, mean-time-to-detection and mean-time-to-resolution, all of which are critical to responding to attacks happening at machine speed. Gain comprehensive coverage: With a unified view of the entire attack surface across clouds, identities, EDRs and more—along with built-in observability telemetry—teams can identify and resolve critical threats and attacks. Scale at enterprise-grade speed: Native to Cloud SIEM, SOC teams can scale their use of AI by deploying faster with thousands of integrations, a unified user experience, and security controls like RBAC, giving teams enterprise-grade visibility, security and control. Bits AI Security Analyst is now generally available.お知らせ • Mar 10Datadog, Inc. Launches MCP Server For AI Agents With Secure, Real-Time Access To Unified Observability DataDatadog, Inc. announced that its MCP Server is generally available. For developers embedding AI agents into development and operational workflows, the Datadog MCP Server provides access to live observability data—so teams can debug in their preferred choice of AI coding agents or Integrated Development Environment with real-time telemetry and take action within established security and governance controls. As embedding AI agents into workflows becomes standard practice at companies across all industries, engineering teams are being tasked with operationalizing AI agents and navigating the intense complexity of this process. To do this, they need secure, governed access to production data, reduced integration overhead and compatibility with compliance requirements. Datadog MCP Server is a purpose-built interface designed for agentic systems, extending Datadog’s unified observability platform directly into AI workflows so that engineering teams can: Debug and act quickly without context switching: Feeds live logs, metrics and traces directly into AI coding agents like Claude Code, Cursor, Codex, Github Copilot, Cognition and Visual Studio Code when investigating production issues. Give custom AI agents direct access to real-time observability and intelligence: Empowers agents to leverage Datadog’s proactive detection and remediation signals so they can investigate and respond to issues automatically. Simplify data access for AI workflows: Reduces the risk of breaking changes by providing a dynamic, purpose-built protocol for agent communication. Datadog MCP Server is now generally available.お知らせ • Mar 03Datadog, Inc. Appoints Dominic Phillips to its Board of DirectorsDatadog, Inc. announced the appointment of Dominic Phillips to its Board of Directors. Dominic brings more than two decades of financial leadership in the technology space to Datadog. As EVP and Chief Financial Officer at Samsara, he leads the company's global financial operations, including strategic finance, accounting, procurement, tax, treasury, corporate development, investor relations, IT, and security. Prior to Samsara, Dominic served as Vice President of Finance and Head of Corporate Development at ServiceNow, where he led FP&A, investor relations, treasury, and corporate development, supporting the company’s significant growth. Earlier in his career, Dominic was a Vice President in Morgan Stanley’s technology investment banking group, advising technology companies on complex financings and strategic transactions. Dominic holds a BS in Business from Cal Poly, San Luis Obispo, and an MBA from UC Berkeley.お知らせ • Mar 02Datadog, Inc., Annual General Meeting, Apr 21, 2026Datadog, Inc., Annual General Meeting, Apr 21, 2026.お知らせ • Feb 10Datadog, Inc. Provides Earnings Guidance for the First Quarter and Fiscal Year 2026Datadog, Inc. provided earnings guidance for the First quarter and Fiscal year 2026. For the quarter, the company expects revenue between $951 million and $961 million. For the year, the company expects revenue between $4.06 billion and $4.10 billion.財務状況分析短期負債: DDOG19の 短期資産 ( $5.6B ) が 短期負債 ( $1.7B ) を超えています。長期負債: DDOG19の短期資産 ( $5.6B ) が 長期負債 ( $1.3B ) を上回っています。デット・ツー・エクイティの歴史と分析負債レベル: DDOG19総負債よりも多くの現金を保有しています。負債の削減: DDOG19の負債対資本比率は、過去 5 年間で87.1%から24.7%に減少しました。債務返済能力: DDOG19の負債は 営業キャッシュフロー によって 十分にカバー されています ( 113.1% )。インタレストカバレッジ: DDOG19の負債に対する 利息支払い が EBIT によって 十分にカバーされている かどうかを判断するにはデータが不十分です。貸借対照表健全な企業の発掘7D1Y7D1Y7D1YSoftware 業界の健全な企業。View Dividend企業分析と財務データの現状データ最終更新日（UTC時間）企業分析2026/05/25 21:39終値2026/05/25 00:00収益2026/03/31年間収益2025/12/31データソース企業分析に使用したデータはS&P Global Market Intelligence LLC のものです。本レポートを作成するための分析モデルでは、以下のデータを使用しています。データは正規化されているため、ソースが利用可能になるまでに時間がかかる場合があります。パッケージデータタイムフレーム米国ソース例会社財務10年損益計算書キャッシュ・フロー計算書貸借対照表SECフォーム10-KSECフォーム10-Qアナリストのコンセンサス予想+プラス3年予想財務アナリストの目標株価アナリストリサーチレポートBlue Matrix市場価格30年株価配当、分割、措置ICEマーケットデータSECフォームS-1所有権10年トップ株主インサイダー取引SECフォーム4SECフォーム13Dマネジメント10年リーダーシップ・チーム取締役会SECフォーム10-KSECフォームDEF 14A主な進展10年会社からのお知らせSECフォーム8-K* 米国証券を対象とした例であり、非米国証券については、同等の規制書式および情報源を使用。特に断りのない限り、すべての財務データは1年ごとの期間に基づいていますが、四半期ごとに更新されます。これは、TTM（Trailing Twelve Month）またはLTM（Last Twelve Month）データとして知られています。詳細はこちら。分析モデルとスノーフレーク本レポートを生成するために使用した分析モデルの詳細は当社のGithubページでご覧いただけます。また、レポートの使用方法に関するガイドやYoutubeのチュートリアルも掲載しています。シンプリー・ウォールストリート分析モデルを設計・構築した世界トップクラスのチームについてご紹介します。業界およびセクターの指標私たちの業界とセクションの指標は、Simply Wall Stによって6時間ごとに計算されます。アナリスト筋Datadog, Inc. 45 これらのアナリストのうち、弊社レポートのインプットとして使用した売上高または利益の予想を提出したのは、 。アナリストの投稿は一日中更新されます。58 アナリスト機関Adam ShepherdArete Research Services LLPWilliam PowerBairdRaimo LenschowBarclays55 その他のアナリストを表示
お知らせ • May 10Datadog, Inc. Provides Earnings Guidance for the Second Quarter and Full Year 2026Datadog, Inc. provided earnings guidance for the second quarter and full year 2026. For the second quarter. the company expects Revenue between $1.07 billion and $1.08 billion. For the full year, the company expects revenue between $4.30 billion and $4.34 billion.
お知らせ • May 02Datadog, Inc., Annual General Meeting, Jun 15, 2026Datadog, Inc., Annual General Meeting, Jun 15, 2026.
お知らせ • Apr 24Datadog Announces GPU Monitoring to Help Businesses Optimize Spend and Performance as They Aim to Scale AI ProjectsDatadog, Inc. announced that GPU Monitoring is available to customers everywhere. The new product addresses one of the most prevalent issues facing organizations as they look for a scalable and effective way to manage expanding AI costs. The launch of GPU Monitoring marks one of the first times a single solution provides unified visibility across the AI stack—giving customers a single view linking GPU fleet health, cost, and performance directly to the teams relying on them for faster troubleshooting of slow workloads and cost savings. Most GPU tools provide high-level device health metrics, but they don’t surface cross-functional resource contention issues, explain why training and inference workloads fail, or provide visibility into which devices are idle or ineffectively used. This lack of visibility slows down investigations and means that teams overprovision as the safest default—leading to wasted spend. GPU Monitoring streamlines this work by linking fleet telemetry directly to the workloads consuming those resources, and gives platform engineering and machine learning teams a shared view to investigate together, enabling them to: Scale AI without overspending: With visibility and forecasting based on the usage patterns of fleets and direct guidance on whether to buy new GPUs or free up existing ones, platform teams avoid expensive purchases and long procurement cycles, machine learning teams get capacity faster, and leadership gets better ROI with predictable spend. Accelerate AI delivery: Stalled workloads are correlated directly to the underlying GPUs, pods and processes running them so that teams can troubleshoot performance bottlenecks in minutes instead of hours, allowing engineers to focus on shipping AI projects. Avoid costly disruptions: Unhealthy GPUs are proactively identified before failures cascade across a cluster and cause training and inference delays. Maximize ROI on GPU spend: Teams are empowered and accountable for their GPU utilization and costs, and can easily pinpoint where they are overserving or underutilizing their GPUs. This allows teams to reclaim and reallocate resources in order to reduce wasted spend. GPU Monitoring is now generally available.
お知らせ • Apr 17Datadog, Inc. to Report Q1, 2026 Results on May 07, 2026Datadog, Inc. announced that they will report Q1, 2026 results Pre-Market on May 07, 2026
お知らせ • Apr 03Datadog Inc. Announces Datadog Experiments AvailabilityDatadog, Inc. announced that Datadog Experiments is available to customers everywhere. The new product enables teams to design, launch, and measure product experiments and A/B tests directly within the Datadog platform—giving teams the data and insights they need to understand how every change affects user behavior, application performance and business outcomes. Datadog solves this problem with the first experimentation platform that combines business metrics from a customer’s data warehouse with product analytics events and application observability. Powered by Datadog’s acquisition of Eppo, Datadog Experiments pairs best-in-class statistical methods with real-time observability guardrails so companies can test what matters, move quickly and ship with confidence. The product empowers every product manager, designer and engineer at a company to take a measured approach to change. Datadog Experiments enables teams to accelerate decisions without the overhead: Experimentation is self-serve and standardized, so teams can move from insight to decision without coordination overhead. Run safer, higher-quality experiments: Built-in guardrails, real-time feedback and shared standards help teams catch issues early, protect users and keep experiments valid. Make decisions leaders trust: Results are credible, reproducible and comparable by measuring impact directly against source-of-truth business metrics in native data warehouses, using consistent methodologies teams can audit and trust. By tying experiments to Real User Monitoring (RUM), Product Analytics, APM and logs, organizations can measure both business impact and performance implications to reduce risk without slowing innovation. Datadog Experiments is now generally available.
お知らせ • Mar 25Datadog, Inc. Announces Availability of Bits AI Security AnalystDatadog, Inc. announced that Bits AI Security Analyst is available to customers everywhere. As part of Datadog’s Cloud SIEM, the AI agent reduces investigations that can take analysts hours down to as little as 30 seconds. Bits AI Security Analyst solves these issues by pairing the expertise of a senior SOC analyst with machine scale and speed, enabling investigation analysis across a breadth and volume of data sources that would be unachievable by a human, while still delivering high-accuracy verdicts backed by real-world context. This allows analysts to scale their investigation expertise so they can focus more time on high-impact defense priorities. When using other SIEMs, it can take teams hours to acknowledge alerts, run investigations, gather evidence, analyze results and escalate if needed. With Bits AI Security Analyst, teams using Datadog Cloud SIEM can autonomously complete all those steps in minutes, reducing the mean-time-to-resolution by more than 90%. Bits AI Security Analyst helps security teams: Detect and resolve issues faster: Autonomous investigations reduce alert fatigue, mean-time-to-detection and mean-time-to-resolution, all of which are critical to responding to attacks happening at machine speed. Gain comprehensive coverage: With a unified view of the entire attack surface across clouds, identities, EDRs and more—along with built-in observability telemetry—teams can identify and resolve critical threats and attacks. Scale at enterprise-grade speed: Native to Cloud SIEM, SOC teams can scale their use of AI by deploying faster with thousands of integrations, a unified user experience, and security controls like RBAC, giving teams enterprise-grade visibility, security and control. Bits AI Security Analyst is now generally available.
お知らせ • Mar 10Datadog, Inc. Launches MCP Server For AI Agents With Secure, Real-Time Access To Unified Observability DataDatadog, Inc. announced that its MCP Server is generally available. For developers embedding AI agents into development and operational workflows, the Datadog MCP Server provides access to live observability data—so teams can debug in their preferred choice of AI coding agents or Integrated Development Environment with real-time telemetry and take action within established security and governance controls. As embedding AI agents into workflows becomes standard practice at companies across all industries, engineering teams are being tasked with operationalizing AI agents and navigating the intense complexity of this process. To do this, they need secure, governed access to production data, reduced integration overhead and compatibility with compliance requirements. Datadog MCP Server is a purpose-built interface designed for agentic systems, extending Datadog’s unified observability platform directly into AI workflows so that engineering teams can: Debug and act quickly without context switching: Feeds live logs, metrics and traces directly into AI coding agents like Claude Code, Cursor, Codex, Github Copilot, Cognition and Visual Studio Code when investigating production issues. Give custom AI agents direct access to real-time observability and intelligence: Empowers agents to leverage Datadog’s proactive detection and remediation signals so they can investigate and respond to issues automatically. Simplify data access for AI workflows: Reduces the risk of breaking changes by providing a dynamic, purpose-built protocol for agent communication. Datadog MCP Server is now generally available.
お知らせ • Mar 03Datadog, Inc. Appoints Dominic Phillips to its Board of DirectorsDatadog, Inc. announced the appointment of Dominic Phillips to its Board of Directors. Dominic brings more than two decades of financial leadership in the technology space to Datadog. As EVP and Chief Financial Officer at Samsara, he leads the company's global financial operations, including strategic finance, accounting, procurement, tax, treasury, corporate development, investor relations, IT, and security. Prior to Samsara, Dominic served as Vice President of Finance and Head of Corporate Development at ServiceNow, where he led FP&A, investor relations, treasury, and corporate development, supporting the company’s significant growth. Earlier in his career, Dominic was a Vice President in Morgan Stanley’s technology investment banking group, advising technology companies on complex financings and strategic transactions. Dominic holds a BS in Business from Cal Poly, San Luis Obispo, and an MBA from UC Berkeley.
お知らせ • Mar 02Datadog, Inc., Annual General Meeting, Apr 21, 2026Datadog, Inc., Annual General Meeting, Apr 21, 2026.
お知らせ • Feb 10Datadog, Inc. Provides Earnings Guidance for the First Quarter and Fiscal Year 2026Datadog, Inc. provided earnings guidance for the First quarter and Fiscal year 2026. For the quarter, the company expects revenue between $951 million and $961 million. For the year, the company expects revenue between $4.06 billion and $4.10 billion.