View Future GrowthDatadog 과거 순이익 실적과거 기준 점검 2/6Datadog은 연평균 57.2%의 비율로 수입이 증가해 온 반면, Software 산업은 수입이 8.8% 증가했습니다. 매출은 연평균 28%의 비율로 증가했습니다. Datadog의 자기자본이익률은 3.4%이고 순이익률은 3.7%입니다.핵심 정보57.22%순이익 성장률56.75%주당순이익(EPS) 성장률Software 산업 성장률19.85%매출 성장률28.00%자기자본이익률3.40%순이익률3.69%다음 순이익 업데이트06 Aug 2026최근 과거 실적 업데이트공고 • Jul 17Datadog, Inc. to Report Q2, 2026 Results on Aug 06, 2026Datadog, Inc. announced that they will report Q2, 2026 results Pre-Market on Aug 06, 2026모든 업데이트 보기Recent updates공고 • Jul 17Datadog, Inc. to Report Q2, 2026 Results on Aug 06, 2026Datadog, Inc. announced that they will report Q2, 2026 results Pre-Market on Aug 06, 2026공고 • Jul 01Datadog, Inc. (NasdaqGS:DDOG) acquired Adaptive ML, Inc.Datadog, Inc. (NasdaqGS:DDOG) acquired Adaptive ML, Inc. on June 30, 2026. Datadog, Inc. (NasdaqGS:DDOG) completed the acquisition of Adaptive ML, Inc. on June 30, 2026.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.공고 • 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.공고 • May 09Datadog, 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.공고 • May 01Datadog, Inc., Annual General Meeting, Jun 15, 2026Datadog, Inc., Annual General Meeting, Jun 15, 2026.공고 • 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.매출 및 비용 세부 내역Datadog가 돈을 벌고 사용하는 방법. 최근 발표된 LTM 실적 기준.순이익 및 매출 추이BATS-CHIXE:3QDD 매출, 비용 및 순이익 (USD Millions)날짜매출순이익일반관리비연구개발비31 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 17101-35625양질의 수익: 3QDD는 고품질 수익을 보유하고 있습니다.이익 마진 증가: 3QDD의 현재 순 이익률 (3.7%)은 지난해 (5.8%)보다 낮습니다.잉여현금흐름 대비 순이익 분석과거 순이익 성장 분석수익추이: 3QDD는 지난 5년 동안 흑자전환하며 연평균 57.2%의 수익 성장을 기록했습니다.성장 가속화: 3QDD은 지난 1년 동안 수익이 감소하여 5년 평균과 비교할 수 없습니다.수익 대 산업: 3QDD은 지난 1년 동안 수익이 감소(-18.1%)하여 Software 업계 평균(-6.5%)과 비교하기 어렵습니다.자기자본이익률높은 ROE: 3QDD의 자본 수익률(3.4%)은 낮음으로 평가됩니다.총자산이익률투하자본수익률우수한 과거 실적 기업을 찾아보세요7D1Y7D1Y7D1YSoftware 산업에서 과거 실적이 우수한 기업.View Financial Health기업 분석 및 재무 데이터 상태데이터최종 업데이트 (UTC 시간)기업 분석2026/07/23 23:17종가2026/07/23 00:00수익2026/03/31연간 수익2025/12/31데이터 소스당사의 기업 분석에 사용되는 데이터는 S&P Global Market Intelligence LLC에서 제공됩니다. 아래 데이터는 이 보고서를 생성하기 위해 분석 모델에서 사용됩니다. 데이터는 정규화되므로 소스가 제공된 후 지연이 발생할 수 있습니다.패키지데이터기간미국 소스 예시 *기업 재무제표10년손익계산서현금흐름표대차대조표SEC 양식 10-KSEC 양식 10-Q분석가 컨센서스 추정치+3년재무 예측분석가 목표주가분석가 리서치 보고서Blue Matrix시장 가격30년주가배당, 분할 및 기타 조치ICE 시장 데이터SEC 양식 S-1지분 구조10년주요 주주내부자 거래SEC 양식 4SEC 양식 13D경영진10년리더십 팀이사회SEC 양식 10-KSEC 양식 DEF 14A주요 개발10년회사 공시SEC 양식 8-K* 미국 증권에 대한 예시이며, 비(非)미국 증권에는 해당 국가의 규제 서식 및 자료원을 사용합니다.별도로 명시되지 않는 한 모든 재무 데이터는 연간 기간을 기준으로 하지만 분기별로 업데이트됩니다. 이를 TTM(최근 12개월) 또는 LTM(지난 12개월) 데이터라고 합니다. 자세히 알아보기.분석 모델 및 스노우플레이크이 보고서를 생성하는 데 사용된 분석 모델의 세부 정보는 당사의 GitHub 페이지에서 확인하실 수 있습니다. 또한 보고서 사용 방법에 대한 가이드와 YouTube 튜토리얼도 제공하고 있습니다.Simply Wall St 분석 모델을 설계하고 구축한 세계적 수준의 팀에 대해 알아보세요.산업 및 섹터 지표산업 및 섹터 지표는 Simply Wall St가 6시간마다 계산하며, 프로세스에 대한 자세한 내용은 Github에서 확인할 수 있습니다.분석가 소스Datadog, Inc.는 59명의 분석가가 다루고 있습니다. 이 중 45명의 분석가가 우리 보고서에 입력 데이터로 사용되는 매출 또는 수익 추정치를 제출했습니다. 분석가의 제출 자료는 하루 종일 업데이트됩니다.분석가기관Adam ShepherdArete Research Services LLPWilliam PowerBairdRaimo LenschowBarclays56명의 분석가 더 보기
공고 • Jul 17Datadog, Inc. to Report Q2, 2026 Results on Aug 06, 2026Datadog, Inc. announced that they will report Q2, 2026 results Pre-Market on Aug 06, 2026
공고 • Jul 17Datadog, Inc. to Report Q2, 2026 Results on Aug 06, 2026Datadog, Inc. announced that they will report Q2, 2026 results Pre-Market on Aug 06, 2026
공고 • Jul 01Datadog, Inc. (NasdaqGS:DDOG) acquired Adaptive ML, Inc.Datadog, Inc. (NasdaqGS:DDOG) acquired Adaptive ML, Inc. on June 30, 2026. Datadog, Inc. (NasdaqGS:DDOG) completed the acquisition of Adaptive ML, Inc. on June 30, 2026.
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.
공고 • 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.
공고 • May 09Datadog, 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.
공고 • May 01Datadog, Inc., Annual General Meeting, Jun 15, 2026Datadog, Inc., Annual General Meeting, Jun 15, 2026.
공고 • 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.