View Future GrowthDatadog 過去の業績過去 基準チェック /26Datadogは、平均年間57.2%の収益成長を遂げていますが、 Software業界の収益は、年間 成長しています。収益は、平均年間7.6% 28%収益成長率で 成長しています。 Datadogの自己資本利益率は3.4%であり、純利益率は3.7%です。主要情報57.22%収益成長率56.75%EPS成長率Software 業界の成長19.85%収益成長率28.00%株主資本利益率3.40%ネット・マージン3.69%前回の決算情報31 Mar 2026最近の業績更新更新なしすべての更新を表示Recent updatesRecent Insider Transactions • Jun 04Independent Director recently sold €8.6m worth of stockOn the 1st of June, Matthew Jacobson sold around 39k shares on-market at roughly €223 per share. This transaction amounted to 6.2% of their direct individual holding at the time of the trade. This was the largest sale by an insider in the last 3 months. Insiders have been net sellers, collectively disposing of €72m more than they bought in the last 12 months.Buy Or Sell Opportunity • Jun 01Now 22% overvaluedThe stock has been flat over the last 90 days, currently trading at €235. The fair value is estimated to be €192, however this is not to be taken as a sell recommendation but rather should be used as a guide only. Revenue has grown by 23% over the last 3 years. Meanwhile, the company has become profitable. For the next 3 years, revenue is forecast to grow by 17% per annum. Earnings are also forecast to grow by 32% per annum over the same time period.お知らせ • May 28Datadog Achieves FedRAMP High Certification For Its Observability And Security PlatformDatadog for Government has achieved Federal Risk and Authorization Management Program (FedRAMP) High certification for its observability and security platform. Datadog’s AI-powered end-to-end observability and security platform delivers real-time visibility and actionable insights across agencies’ entire IT environments while complying with Federal Government’s most stringent security requirements. The platform enables agencies to strengthen their security posture by identifying and managing risk within a single, unified solution designed for high-impact systems. With comprehensive dashboards and intelligent alerts, Government teams can proactively detect and resolve issues before they disrupt mission-critical operations or impact citizen services. Carahsoft serves as Datadog’s Master Government Aggregator, providing ease of procurement for the company’s platform and solutions and access to services and training for the Public Sector through hundreds of contract vehicles. Datadog’s solutions are available through Carahsoft’s GSA Schedule No. 47QSWA18D008F, NASPO ValuePoint Master Agreement #AR2472, TIPS Contract #220105, OMNIA Partners Contract #R240303 and E&I Contract #EI00063~2021MA.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.収支内訳Datadog の稼ぎ方とお金の使い方。LTMベースの直近の報告された収益に基づく。収益と収入の歴史BATS-CHIXE:3QDD 収益、費用、利益 ( )USD Millions日付収益収益G+A経費研究開発費31 Mar 263,6721361,2981,60131 Dec 253,4271081,2221,50830 Sep 253,2121071,1511,41030 Jun 253,0161251,0791,30231 Mar 252,8351661,0071,19431 Dec 242,6841849501,12230 Sep 242,5361928861,06030 Jun 242,3941638531,00931 Mar 242,25811580897531 Dec 232,1284977994130 Sep 232,008-3476591030 Jun 231,897-8372987931 Mar 231,794-8469082031 Dec 221,675-5063174230 Sep 221,532-1455965630 Jun 221,366748956431 Mar 221,193242848131 Dec 211,029-2138641230 Sep 21880-4435035130 Jun 21764-5432429631 Mar 21671-4429924731 Dec 20603-2527521130 Sep 20540-725217830 Jun 20481322815131 Mar 20424-120713131 Dec 19363-1718511330 Sep 19311-241639330 Jun 19266-241447831 Mar 19228-201266731 Dec 18198-111075531 Dec 17101-35625質の高い収益: 3QDDは 高品質の収益 を持っています。利益率の向上: 3QDDの現在の純利益率 (3.7%)は、昨年(5.8%)よりも低くなっています。フリー・キャッシュフローと収益の比較過去の収益成長分析収益動向: 3QDD過去 5 年間で収益を上げており、収益は年間57.2%増加しています。成長の加速: 3QDDは過去 1 年間の収益成長がマイナスであったため、5 年間の平均と比較することはできません。収益対業界: 3QDDは過去 1 年間で収益成長率がマイナス ( -18.1% ) となったため、 Software業界平均 ( -6.5% ) と比較することが困難です。株主資本利益率高いROE: 3QDDの 自己資本利益率 ( 3.4% ) は 低い とみなされます。総資産利益率使用総資本利益率過去の好業績企業の発掘7D1Y7D1Y7D1YSoftware 、過去の業績が好調な企業。View Financial Health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2026/06/14 23:28終値2026/06/12 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
Recent Insider Transactions • Jun 04Independent Director recently sold €8.6m worth of stockOn the 1st of June, Matthew Jacobson sold around 39k shares on-market at roughly €223 per share. This transaction amounted to 6.2% of their direct individual holding at the time of the trade. This was the largest sale by an insider in the last 3 months. Insiders have been net sellers, collectively disposing of €72m more than they bought in the last 12 months.
Buy Or Sell Opportunity • Jun 01Now 22% overvaluedThe stock has been flat over the last 90 days, currently trading at €235. The fair value is estimated to be €192, however this is not to be taken as a sell recommendation but rather should be used as a guide only. Revenue has grown by 23% over the last 3 years. Meanwhile, the company has become profitable. For the next 3 years, revenue is forecast to grow by 17% per annum. Earnings are also forecast to grow by 32% per annum over the same time period.
お知らせ • May 28Datadog Achieves FedRAMP High Certification For Its Observability And Security PlatformDatadog for Government has achieved Federal Risk and Authorization Management Program (FedRAMP) High certification for its observability and security platform. Datadog’s AI-powered end-to-end observability and security platform delivers real-time visibility and actionable insights across agencies’ entire IT environments while complying with Federal Government’s most stringent security requirements. The platform enables agencies to strengthen their security posture by identifying and managing risk within a single, unified solution designed for high-impact systems. With comprehensive dashboards and intelligent alerts, Government teams can proactively detect and resolve issues before they disrupt mission-critical operations or impact citizen services. Carahsoft serves as Datadog’s Master Government Aggregator, providing ease of procurement for the company’s platform and solutions and access to services and training for the Public Sector through hundreds of contract vehicles. Datadog’s solutions are available through Carahsoft’s GSA Schedule No. 47QSWA18D008F, NASPO ValuePoint Master Agreement #AR2472, TIPS Contract #220105, OMNIA Partners Contract #R240303 and E&I Contract #EI00063~2021MA.
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.