DDOG

T13.5% portfolio

Datadog, Inc.

Next est. report 2026-08-06 · BMO

AI '25: Cloud Platform & Software AI '25: Phase 2 Deployment AI '26: Citizen DevOps AI Short '26: AI-Obsolete Software Agentic Utilities '26: Deterministic Telemetry Agentic Utilities '26: Governance & Zerotrust Business Services '26: AI Governance, Trust & Compliance Data Owners '25: Cybersecurity & Threat Intelligence Data Owners '25: Enterprise Workflow Data

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Overview

Datadog provides a cloud-based platform that helps businesses monitor their technology systems, applications, and security in real time. Revenue comes from infr

Datadog provides a cloud-based platform that helps businesses monitor their technology systems, applications, and security in real time. Revenue comes from infrastructure tracking (43%), application performance (33%), logs (17%), and security (7%). They serve software and artificial intelligence firms, including Microsoft and nearly half of the Fortune 500. Their tools ensure websites and apps run smoothly and securely.

What They Do (Plain English & Analogies): Datadog acts like a digital 'control tower' for businesses that use cloud-based technology. Imagine a massive airline: Datadog is the dashboard that tells the pilots (engineers) if the engines (servers) are running hot, if the cabin pressure (app performance) is dropping, or if there's a security threat in the cargo hold (logs/security). It collects data from every part of a company's cloud infrastructure and turns it into real-time charts and alerts. This helps IT teams fix problems before customers even notice them. In the age of Artificial Intelligence (AI), it also acts as a 'stethoscope' for AI models, monitoring how much they cost to run and whether they are 'hallucinating' or failing, ensuring they operate smoothly and securely.
Very Brief History: Founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, Datadog initially aimed to break down silos between development and operations teams. It began as an infrastructure monitoring tool and quickly expanded its offerings to include Application Performance Monitoring (APM) in 2017 and Log Management in 2018, becoming a 'three-pillar' observability leader. The company went public in 2019 and has since broadened its platform into Cloud Security, Software Delivery, and Generative AI observability, with multiple product lines exceeding $1 billion in Annual Recurring Revenue (ARR) by 2025.
"Street Stereotype": Datadog is widely regarded as the 'Gold Standard' and 'Best-of-Breed' platform in the cloud observability space. It is perceived as an execution machine with a relentless research and development engine that consistently releases numerous features each year. Investors often view it as a high-valuation, high-growth 'compounder' that serves as a primary indicator for the health of the broader cloud and AI ecosystem.
Subsidiaries On Linked In*: Seekret — Acquired by Datadog in 2022
CoScreen — Acquired by Datadog in 2022
Hdiv Security — Acquired by Datadog in 2021
Cloudcraft — Acquired by Datadog in 2021
Customer Sectors & Example Clients: Datadog serves a wide range of sectors including Technology, Financial Services, E-commerce, Healthcare, and Retail. They count nearly half of the Fortune 500 as customers. Specific clients mentioned or implied in the recent earnings include AI research divisions at two of the world's largest technology companies, a leading online recruiting platform, a Fortune 500 bank, a leading global hedge fund, a Fortune 500 insurance company, one of the world's largest travel groups in APAC, and a leading Latin American fintech company.
New Customers / Segments They'Re Targeting: Datadog is actively targeting and seeing rapid growth from 'AI native customers' who are building and training advanced AI models. They are also expanding their reach into the public sector, having recently received federal high certification from the U.S. federal government (FedRAMP High), which allows them to serve federal agency customers with sensitive workloads.
Supply Chain And Sourcing Geographies: Datadog runs most of its workloads on cloud infrastructure, meaning related costs are primarily reflected in operating expenses rather than capital expenditures. The company does not explicitly detail a physical supply chain or specific sourcing geographies for hardware or components in the provided information.
Sales Geographies And Expansion Plans: Datadog sells its products across North America and internationally. The company is expanding its global presence, including the launch of a new data center in the U.K. to serve British customers, particularly as cloud adoption accelerates in regulated industries. They are also expanding go-to-market teams and channel partnerships for public sector customers, both in the U.S. and internationally, following their FedRAMP High certification.
How Key Themes May Help/Hurt: The 'Agentic Utilities '26: Governance & Zerotrust' theme significantly benefits Datadog. As autonomous AI agents proliferate, they introduce immense complexity and expand the attack surface for enterprises. Datadog's platform becomes crucial for 'Agentic Governance' by providing the observability layer needed to precisely meter agent usage, control costs, detect anomalies, and ensure security in real-time. The shift towards Zero Trust architectures, where every agent request is continuously verified, aligns perfectly with Datadog's ability to provide end-to-end visibility and security across the entire technology stack, including AI workloads. This theme drives demand for Datadog's specialized AI security and observability products, helping customers manage agent behavior, prevent data leaks, and maintain compliance.

3 Main Long-Term Bull Details

Dominant Observability Layer for the AI Economy: Datadog is solidifying its position as the essential 'mission control' for the agentic AI era, with AI-native customers rapidly growing and over 6,500 customers already sending data for one or more AI integrations, representing 80% of their ARR. New products like GPU monitoring and LLM Observability are capturing high-margin revenue as AI agents move into production and training workloads.
Accelerating Platform Consolidation: The company continues to win massive consolidation deals, displacing fragmented open-source and legacy tools with its unified platform. This strategy drives strong multi-product adoption, with 56% of customers using four or more products, and 20% using eight or more, highlighting the mission-critical nature of Datadog's platform and leading to robust net retention.
Broad-Based Core Business Re-acceleration: Beyond AI, Datadog's non-AI customer revenue growth accelerated to mid-20% year-over-year, indicating strong continued cloud migration and greater adoption of their products across a diversified customer base. This broad-based strength, combined with low churn, demonstrates the underlying health and expansion potential of Datadog's core business.

3 Main Long-Term Bear Details

Usage-Based Volatility and Cost Optimization: Datadog's revenue model, which charges based on data volume, exposes it to potential volatility if customers engage in aggressive cost optimization or if there's a slowdown in overall cloud usage. While AI currently drives increased data, a shift to more efficient inference models or customer-built sophisticated homegrown observability scripts could impact revenue.
Intense Competitive Pressure: The observability market remains highly competitive, with legacy incumbents and hyperscalers integrating AI-driven features into their own monitoring tools. If open-source alternatives or hyperscaler-native tools improve their agentic capabilities to be 'good enough,' Datadog may face pressure to increase R&D and sales spending, potentially compressing operating margins.
Monetization Uncertainty of New AI Products: While Datadog is aggressively building new AI products like Bits AI SRE Agent and MCP Server, their long-term monetization and ability to drive material per-seat or usage-based revenue are still in early stages. If these innovations fail to translate into significant revenue streams, the stock's AI-driven upside may be capped.

Competitors And Differentiation: Datadog competes with a range of players including legacy incumbents, open-source tooling, and hyperscaler-native monitoring tools. Datadog differentiates itself by offering a unified, end-to-end observability and security platform that consolidates various functions like infrastructure monitoring, APM, log management, and security surveillance. This platform approach helps customers reduce complexity, drive developer velocity, improve efficiency, and save money by replacing fragmented tools. The company also emphasizes its rapid product innovation, particularly in AI-driven capabilities, and its ability to provide comprehensive visibility across heterogeneous environments, including custom silicon and GPU monitoring for AI workloads.
Recent Performance & What The Market'S Focused On: Datadog delivered a very strong first quarter of 2026, with revenue reaching $1.01 billion, an increase of 32% year-over-year, accelerating from 29% last quarter. This marked the first time quarterly revenue exceeded $1 billion. The company ended Q1 with approximately 33,200 customers, including about 4,550 customers with an ARR of $100,000 or more, representing about 90% of total ARR. Free cash flow was $289 million, with a free cash flow margin of 29%. For Q2 2026, Datadog expects revenues between $1.07 billion and $1.08 billion, representing 29% to 31% year-over-year growth. For the full fiscal year 2026, they project revenues between $4.3 billion and $4.34 billion, representing 25% to 27% year-over-year growth. The market is focused on the continued acceleration of revenue growth, particularly from both AI and non-AI customers, the successful displacement of legacy vendors through platform consolidation, and the monetization of Datadog's AI innovations.

Revenue Segments And Estimated Mix: Infrastructure Monitoring — Mix: ~$1.6B+ ARR; Source: Q4 2025 earnings summary, ~43% of revenue previously
Application Performance Monitoring (APM) & Digital Experience Monitoring (DEM) — Mix: ~$1B+ ARR; Source: Q4 2025 earnings summary, ~33% of revenue previously; Trend: APM growth accelerated to mid-30s% y/y in Q4 2025
Log Management — Mix: ~$1B+ ARR; Source: Q4 2025 earnings summary, ~17% of revenue previously; Trend: Flex Logs nearing $100M ARR as of Feb 2026
Cloud Security (SIEM) — Mix: ~7% of revenue previously; Source: Overview table; Trend: Security ARR grew mid-50s% y/y in Q3 2025
Data Observability — Mix: n/m; Source: Transcript mentions as newer product
Cloud Service Management (On-call) — Mix: n/m; Source: Transcript mentions as newer product
Product Brands: Infrastructure Monitoring
Application Performance Monitoring (APM)
Log Management
Cloud Security (SIEM)
Real User Monitoring (RUM)
Synthetics
Product Analytics
Network Performance Monitoring
Network Device Monitoring
Serverless Monitoring
Cloud Cost Management
CI Visibility
Observability Pipelines
Universal Service Monitoring
Data Streams Monitoring
Database Monitoring
Data Jobs Monitoring
Continuous Profiler
Dynamic Instrumentation
Sensitive Data Scanner
Audit Trail
Cloud Security Posture Management
Workload Protection
Cloud Infrastructure Entitlement Management
Vulnerability Management
Compliance
App and API Protection
Software Composition Analysis
Code Security
Static Code Analysis (SAST)
Runtime Code Analysis (IAST)
IaC Security
Cloud SIEM
Browser Real User Monitoring
Mobile Real User Monitoring
Experiments
Session Replay
Synthetic Monitoring
Mobile App Testing
Continuous Testing
Error Tracking
BYOC Log Management
Internal Developer Portal
CI Visibility Test Optimization
Feature Flags
Code Coverage
AI Impact Service Level Objectives
Incident Response
Event Management
Case Management
Bits AI Agents
Bits Code
Bits Investigation
Bits Security Analyst
Bits Agent Builder
Bits Chat
MCP Server
Pup CLI Agent Directory
Metrics Watchdog
Agent Observability AI Integrations
Workflow Automation App Builder
CoScreen Teams Dashboards
Notebooks Mobile App Fleet Automation
Governance Console Access Control
OpenTelemetry Alerts integrations
IDE Plugins
API Marketplace
Security Labs Research Open Source Projects
Storage Management
GPU Monitoring
DORA Metrics
Secret Scanning
LLM Observability
Watchdog Anomaly detection
Bit Assistant
Flex Logs

Bull / Bear Details

Datadog is solidifying its position as the essential mission control for the agentic AI era, now further bolstered by accelerating revenue growth to 32% and mid

Thesis

Datadog is solidifying its position as the essential mission control for the agentic AI era, now further bolstered by accelerating revenue growth to 32% and mid-20s% non-AI usage. As of 2026-07-23, the platform is successfully displacing legacy vendors through massive consolidation deals, while expanding into AI model training and GPU monitoring. Datadog's dominant AI-native footprint and robust multi-product adoption make the bull case highly compelling.

Bull case

Datadog's core business is re-accelerating, with total revenue growth hitting 32% year-over-year and non-AI customer revenue accelerating to mid-20%. This strong performance is driven by continued cloud migration and successful platform consolidation, evidenced by customers replacing multiple legacy tools and open-source solutions to unify their observability and security needs, proving Datadog's mission-critical status.
Datadog is the dominant observability layer for the expanding AI economy, now capturing the nascent AI model training market, including landing hyperscalers for GPU monitoring. AI integrations represent 80% of ARR from just 20% of customers, with rapid usage growth in LLM Observability and MCP server calls. This positions Datadog to capture high-margin revenue as AI agents and training workloads move into production.
The company continues to demonstrate strong multi-product adoption and expansion, with 20% of customers now using eight or more products, up from 13% a year ago. Total ARR now exceeds $4 billion, and quarterly revenue surpassed $1 billion for the first time. With 5 products over $100M ARR and 18 more with similar potential, Datadog's platform strategy creates a powerful flywheel effect.

Bear case

Despite strong current performance, management's FY2026 revenue guidance of 25-27% (though an improvement from prior guidance) still reflects conservatism, particularly for its largest customer. This continued caution highlights potential volatility in large customer consumption and introduces concentration risk, as significant optimization or shifts by these key accounts could still impact Datadog's growth trajectory and premium valuation.
Competitive pressures persist from both legacy incumbents and hyperscalers, who are continuously enhancing their own AI-driven observability features. While Datadog is currently outperforming and winning consolidation deals, the high cost of its comprehensive platform remains a potential friction point for enterprises. If open-source alternatives or hyperscaler-native tools improve their capabilities, Datadog may face increased pressure to maintain its lead.
The rapid evolution of the agentic AI era introduces inherent unpredictability regarding long-term observability consumption patterns. While Datadog's usage-based model currently benefits from increased data volume and agent activity, customers might eventually leverage AI agents to optimize their observability spend in unforeseen ways, or new, disruptive agent-native observability paradigms could emerge, potentially altering Datadog's competitive landscape.

Bull / Bear Case

Bear Case: Despite strong Q1 2026 performance, Datadog's FY2026 revenue guidance of 25-27% year-over-year growth implies a deceleration from current levels, with management applying a higher degree of conservatism to its largest customer, indicating potential volatility in consumption. The company's usage-based revenue model exposes it to risks from customer cost optimization, especially as "wasted cloud spend" has increased due to AI workloads. The observability market remains highly competitive, with legacy incumbents and hyperscalers integrating AI-driven features, which could pressure Datadog's margins if it needs to increase R&D and sales spending to maintain its lead. Furthermore, the long-term monetization of new AI products like Bits AI and MCP is still in early stages, and there's a risk that sophisticated customers could eventually build homegrown observability solutions using AI agents, bypassing commercial platforms.
Bull Case: Datadog is solidifying its position as the essential mission control for the agentic AI era, with Q1 2026 revenue growth accelerating to 32% year-over-year, driven by broad-based strength across both AI-native and non-AI customers. The company's platform strategy continues to resonate, leading to increased multi-product adoption (56% of customers use four or more products) and robust net revenue retention in the low 120s%. New AI products like GPU monitoring, Bits AI SRE Agent, and MCP Server are capturing high-margin revenue as AI agents move into production and training workloads, even attracting hyperscalers. Datadog is successfully displacing legacy vendors through consolidation deals and expanding its market opportunity with new data centers (U.K.) and certifications (FedRAMP High), positioning it for continued long-term growth in cloud migration, digital transformation, and AI adoption.
More Compelling & Why: Given the current valuation, the Bear Case is more compelling. Datadog's P/S ratio of ~25x and EV/FCF of 91.93x are significantly above historical averages and industry medians, with GuruFocus deeming it "Significantly Overvalued". The strongest argument for the bear case is the implied deceleration in the FY2026 revenue guidance (25-27%) compared to the Q1 actual (32%), coupled with management's explicit conservatism regarding its largest customer. My view would flip to bullish if Datadog consistently raises its full-year revenue guidance to sustain 30%+ year-over-year growth, demonstrating that AI-driven demand can offset any optimization headwinds and justify its premium multiples.

Key Factors5 rows

Key Factor	Why It Matters	What To Watch	What It Signals	Where/How To Track	Free Alt Data	Paid Alt Data
Model Context Protocol (MCP) Tool Call Velocity	MCP is a key innovation for the agentic AI era, and its rapid adoption signals Datadog's ability to monetize AI agent interactions and provide essential tools for automated troubleshooting and development.	Management commentary on the quarter-over-quarter growth rate of 'tool calls' to the Datadog MCP server.	Bullish if tool calls continue to grow >300% q/q, indicating strong adoption and integration of MCP into developer workflows and AI agent operations.	Company earnings calls and investor presentations (next expected Q2 2026 earnings in August 2026).	GitHub: Stars/forks for open-source projects integrating with Datadog's MCP (if publicly available).	Apptopia: Datadog developer tool usage trends (if available).
AI-Native Customer Tiering ($1M+ ARR Count)	This metric directly reflects Datadog's success in capturing and expanding high-value AI workloads, validating its position as the dominant observability layer for the AI economy and driving significant revenue growth.	The number of AI-native customers with an ARR of $1 million or more, and specifically those with $10 million or more.	Bullish if the count of AI-native customers spending >$1M ARR continues to grow beyond 22, and >$10M ARR customers beyond 5, indicating rapid scaling of AI production workloads and increasing wallet share.	Company earnings calls and press releases (next expected Q2 2026 earnings in August 2026).	N/A	Thinknum: AI-related job postings by Datadog's AI-native customers (growth rate)
Adoption of GPU Monitoring for AI Training Workloads by Hyperscalers	This indicates Datadog's successful expansion into the high-growth AI training market, particularly with demanding hyperscaler customers, showcasing its ability to monitor complex, specialized AI infrastructure and drive new revenue streams.	Management commentary on the adoption rate and usage growth of GPU monitoring, especially by large AI research teams or hyperscalers for their training workloads.	Bullish if Datadog reports increasing customer wins and usage of GPU monitoring, particularly with hyperscalers, validating the product's market fit and the company's ability to capture the AI training observability market.	Company earnings calls and product announcements (next expected Q2 2026 earnings in August 2026).	N/A	Sensor Tower: App usage data for AI development tools that might integrate with Datadog's GPU monitoring (indirect).
Attainment of FedRAMP High Certification	This certification opens up the lucrative U.S. federal government market for sensitive workloads, significantly expanding Datadog's addressable market and validating its robust security posture, which is crucial for the 'Governance & Zerotrust' theme.	Announcements of new contracts or partnerships with U.S. federal agencies requiring FedRAMP High, and management commentary on pipeline build-out and initial bookings from this sector.	Bullish if Datadog announces initial significant federal agency contracts or provides positive updates on the pipeline and go-to-market execution in the federal sector.	Company press releases, SEC filings, and earnings calls (next expected Q2 2026 earnings in August 2026). USASpending.gov for federal contract awards.	USASpending.gov: Government contract awards to Datadog (DDOG) or its partners, specifically for cloud monitoring or security services.	GovWin IQ: Federal IT spending data and contract awards for observability and security solutions.
Legacy SIEM and Logging Displacement Deals	Successful displacement of legacy vendors, particularly in SIEM and logging, demonstrates Datadog's platform consolidation strength, its ability to deliver cost savings, and its critical role in enterprise security and compliance, aligning with the Zero Trust theme.	Specific mentions of large-scale customer wins involving the full replacement of legacy log vendors (e.g., with Flex Logs) or consolidation of multiple SIEM/APM tools.	Bullish if Datadog continues to announce significant deals where customers fully replace legacy SIEM or logging vendors, or consolidate multiple observability tools, indicating strong competitive wins and platform stickiness.	Company earnings calls and press releases (next expected Q2 2026 earnings in August 2026).	N/A	Gartner Peer Insights: Customer reviews mentioning 'replacement' or 'consolidation' of SIEM/logging tools.

Key Reported Metrics, Reratings Triggers & Results3 rows

Growth in this customer segment is a key indicator of Datadog's ability to land and expand with larger, higher-value customers, which are crucial for long-term

Key reported metrics			Rerating thresholds			Earnings results
Metric	Last period	Why it matters	What's needed for rerating	Rerating context	Earnings date	Actual reported	Hit target?	Notes
Customers with ARR of $100,000 or more (Count Growth)	21%	Growth in this customer segment is a key indicator of Datadog's ability to land and expand with larger, higher-value customers, which are crucial for long-term ARR growth and platform adoption, especially in the AI era.	An acceleration to 25% or more year-over-year growth.	This metric is crucial as it directly reflects Datadog's ability to land and expand with high-value customers, which drive 90% of its ARR. An acceleration would validate its 'land and expand' strategy and AI adoption, justifying its premium valuation and reinforcing its competitive position.	2026-08-06
Total Revenue Growth	32%	As a high-growth category leader, sustaining strong revenue growth is critical for Datadog's premium valuation and signals continued market leadership and demand for its platform.	Datadog needs to report Q2 2026 Total Revenue Growth of at least 32% year-over-year, significantly exceeding its own guidance of 29-31% and analyst consensus of approximately 30-31%. Additionally, the company must raise its full-year 2026 revenue guidance to at least 29-30% year-over-year, demonstrating sustained acceleration and robust AI monetization.	Datadog's premium valuation depends on its status as a high-growth category leader. Exceeding revenue growth expectations and raising guidance would validate strong AI-driven demand and platform consolidation, justifying a higher multiple and reinforcing market leadership.	2026-08-06
Broad-based (Non-AI Native) Revenue Growth	mid-20%	This metric indicates the health and acceleration of Datadog's core enterprise and SMB business, demonstrating successful platform consolidation and reduced reliance on the volatile AI-native cohort.	The Broad-based (Non-AI Native) Revenue Growth needs to accelerate to at least 28%+ year-over-year, representing a clear re-acceleration beyond the mid-20% reported in Q1 2026 and the 23% in Q4 2025.	This metric indicates the health and acceleration of Datadog's core enterprise business, demonstrating successful platform consolidation and reduced reliance on the volatile AI-native cohort. Sustained acceleration in non-AI revenue would de-risk the investment case, justify premium valuation multiples, and contribute to the overall 30%+ total revenue growth needed for a positive rerating.	2026-08-06

Key Questions

Will Datadog's accelerating AI-native customer growth, including new wins in AI training and GPU monitoring, translate into sustained higher revenue growth and

Will Datadog's accelerating AI-native customer growth, including new wins in AI training and GPU monitoring, translate into sustained higher revenue growth and further solidify its 'data plane' moat against potential in-house solutions?

Question 2: Can Datadog continue to accelerate its platform consolidation strategy, evidenced by increasing multi-product adoption and large-scale displacement of legacy observability and security tools, to drive significant ARR expansion across its diverse customer base?
Question 3: Will Datadog's conservative FY2026 revenue guidance, particularly the higher degree of conservatism applied to its largest customer, prove to be overly cautious, leading to significant upside beats driven by broad-based strength and AI adoption, or does it signal a potential slowdown in key customer segments?

Earnings Transcript Summary2 rows

2026-05-07 · 2026Q1 Earnings Call

3 Things Management Is Most Focused On	Call Takeaway & Tone	Prior Quarter'S Y/Y Growth By Segment	3 Things Analysts Most Pressed On (And Mgmt Responses)	Revenue Segments
1. Accelerating revenue growth and strong Q1 performance: Management highlighted the strong start to 2026 with 32% year-over-year revenue growth, accelerating from 29% last quarter, driven by broad-based acceleration across both AI and non-AI customers. 2. Platform strategy and product adoption: They emphasized the continued resonance of their platform strategy, with increasing multi-product adoption (56% of customers use 4+ products, up from 51% a year ago) and significant new product launches, particularly in AI (AI for Datadog, Datadog for AI, GPU monitoring, Bits AI security agent, Bits Assistant, MCP server). 3. Capitalizing on secular growth drivers: Management reiterated that digital transformation and cloud migration remain long-term secular growth drivers, with AI now identified as an additional, significant secular growth driver, positioning Datadog to help customers with AI adoption journeys.	The overall tone of the call was highly positive and confident. Datadog reported a very strong start to 2026, with revenue growth accelerating to 32% year-over-year, driven by broad-based strength across both AI-native and non-AI customers. Management emphasized the success of their platform strategy, leading to increased multi-product adoption and significant new product innovations, particularly in AI observability and security. The company is successfully displacing legacy vendors and expanding its market opportunity by addressing the complexities of cloud migration, digital transformation, and the emerging AI landscape, including new opportunities in AI model training. Management expressed strong confidence in their Q2 outlook, supported by record ARR additions and diversified customer growth.	In the prior quarter (2025Q4), total revenue grew 29% year-over-year. Broad-based (non-AI native) usage grew 23% year-over-year.	1. Growth in code production due to AI code generators and its impact on Datadog: Analysts questioned how the exponential growth in code production from AI generators like GitHub Copilot affects Datadog's activity. Olivier Pomel responded that they "definitely think and see that there's many more applications being created" and "way more complexity in production," driving an "inflection point" in customer consumption for Datadog. 2. Increasing heterogeneity of silicon (custom chips like Trinium, Graviton, TPUs) and its tailwinds for Datadog: Analysts asked if the proliferation of custom silicon makes monitoring more difficult for traditional tools but provides tailwinds for Datadog. Olivier Pomel affirmed this, stating that "the more heterogeneous, the more you need someone else to make sense of everything for you and title together." He also noted that AI model training, previously a niche, is now becoming a viable market for Datadog, attracting hyperscalers. 3. The future of observability when agents perform triaging/investigating versus human engineers, and potential new pricing modalities: Analysts inquired about Datadog's vision for how the category evolves with AI agents and if pricing models might change. Olivier Pomel responded that their usage-based business model is well-suited regardless of whether usage comes from humans or agents. He observed both a "stratospheric increase of agent usage" and continued "increase of usage of the web interface by humans," indicating both modalities are growing hand-in-hand.	Total revenue grew 32% year-over-year. Non-AI customer revenue growth accelerated to mid-20% year-over-year. AI native customer growth continued to significantly outpace the rest of the business. Total ARR now exceeds $4 billion, and quarterly revenue exceeded $1 billion for the first time. 5 products are over $100 million in ARR, and another 3 are between $50 million and $100 million ARR.

2026-02-10 · 2025Q4 Earnings Call

3 Things Management Is Most Focused On	Call Takeaway & Tone	Prior Quarter'S Y/Y Growth By Segment	3 Things Analysts Most Pressed On (And Mgmt Responses)	Revenue Segments
1. AI Product Innovation: Executing a dual strategy of 'AI for Datadog' (Bits AI SRE agents to automate root cause analysis) and 'Datadog for AI' (observability for LLM stacks and GPU fleets). 2. Platform Consolidation: Displacing legacy vendors and open-source tools through unified observability, evidenced by 18 deals over $10M TCV this quarter. 3. Scaling Go-to-Market: Expanding sales capacity and geographic reach to maintain productivity while capturing the long-term secular trend of cloud migration.	Tone: Highly Positive and Confident. Takeaway: Datadog delivered a standout quarter characterized by the re-acceleration of its core non-AI business and massive deal wins within the AI-native cohort. The company is successfully positioning itself as the critical 'mission control' for the agentic AI era, with strong multi-product adoption (9% of customers using 10+ products) and robust free cash flow margins.	Total Revenue: 26% y/y; Broad-based (non-AI native) usage: 20% y/y; Core APM: ~31% y/y (estimated based on management's commentary regarding Q4 acceleration).	1. Defensibility against Agentic AI: Analysts asked if AI agents could eventually build their own observability. Mgmt responded that AI increases system complexity and data volume, making Datadog's real-time data plane and specialized models more essential for proactive resolution. 2. 2026 Guidance Conservatism: Analysts questioned the 18-20% revenue growth guide for FY26. Mgmt explained they apply conservatism to the usage of their largest customers and maintain a consistent guidance philosophy despite strong current trends. 3. Competition and Build-vs-Buy: Analysts asked about the threat of open-source or in-house solutions. Mgmt argued that DIY is economically irrational for most companies due to high engineering costs and that Datadog provides faster velocity and better ROI.	Total Revenue: 29% y/y; Broad-based (non-AI native) usage: 23% y/y; Core APM: Mid-30s% y/y; Infrastructure Monitoring: ARR >$1.6B; Log Management: ARR >$1B; APM & DEM: ARR >$1B.

Transcript Tidbits2 rows

2026-05-07

About Expanding Eligible Market	About Competition	About The Broader Industry	Where Things Are Headed	Updates On Theme	Broader Themes Emerging	Bullish-Leaning Quotes (Short)	Bearish-Leaning Quotes (Short)	Hiring
Datadog's total ARR now exceeds $4 billion, and quarterly revenue exceeded $1 billion for the first time. The company has 26 products, with 5 over $100 million in ARR and 18 earlier-stage products believed to have the potential to grow to over $100 million. New product launches include GPU monitoring and Experiments for general availability. Datadog is expanding geographically with a planned data center in the U.K. and has achieved FedRAMP High certification, allowing it to serve U.S. federal agency customers. The company is also expanding product offerings, go-to-market teams, and channel partnerships for public sector customers globally. Datadog is seeing training become a viable market, landing deals with hyperscalers for monitoring training workloads and GPUs. The company is investing in deploying into more geographies and certifications for the public sector, as well as 'bring your own cloud' products to support customers with data residency and sovereignty needs, potentially enabling entry into extremely large-scale workloads that previously would not have considered SaaS offerings.	Datadog continues to see customers consolidating fragmented observability stacks, replacing multiple legacy APM tools, open-source solutions, and cloud monitoring tools to unify data, automate workflows, and save money. Even hyperscalers, known for building in-house, are turning to Datadog to accelerate innovation on hyperscale AI training workloads and replace existing solutions. Olivier Pomel stated that Datadog is outperforming competitors at scale and taking market share due to its platform, product expansion, and successful growth of sales capacity. The increasing heterogeneity of silicon environments (e.g., Amazon Trinium/Graviton, Google TPUs, Microsoft Myosilicon) favors Datadog, as it can make sense of diverse environments and tie them together with the broader infrastructure and applications, which traditional monitoring tools often fail to do. The urgency of AI development efforts is forcing even large companies to prioritize core activities and rely on Datadog for non-core observability, a shift from their historical 'build it themselves' approach.	The industry is experiencing strong continued cloud migration and greater adoption of Datadog's products, with customers of all kinds accelerating their use of AI. Digital transformation and cloud migration remain long-term secular growth drivers, now augmented by AI as an additional secular growth driver. There is an observed increase in the sheer volume of code being produced due to code generators, leading to more applications and greater complexity in production, which drives activity for Datadog. The increasing heterogeneity of the silicon environment, with custom chips from major cloud providers, is a trend that plays in Datadog's favor as it makes monitoring more complex. Training of AI models, once limited to a few companies, is democratizing and becoming a more viable market category. The industry is entering an 'agentic era' where the focus is shifting from writing code to validating and monitoring it, with massive hyperscaler CapEx leading to 'very, very, very large increases in complexity,' serving as a long-term tailwind for observability. It's becoming harder to predict future trends, as evidenced by the unexpected return to coding in the console for many engineers.	Datadog believes it is 'still just getting started,' with 18 of its 26 products in earlier life cycles, each having the potential to grow to over $100 million in ARR. The company is pleased with its start to 2026, supporting customers' inflection in AI usage and application development, and leveraging its AI innovations like Bits AI SRE Agent, Bits AI Security Analyst, Bits Assistant, Datadog MCP server, and GPU monitoring. Datadog sees digital transformation, cloud migration, and AI adoption as long-term secular growth drivers. The company aims to help customers of every size and industry transform, innovate, and drive value through AI and cloud adoption. For Q2 2026, Datadog expects revenues between $1.07 billion and $1.08 billion, representing 29% to 31% year-over-year growth, and for fiscal 2026, revenues between $4.3 billion and $4.34 billion, representing 25% to 27% year-over-year growth. The company is ramping up investments in R&D, particularly in the scale of models it trains. Datadog is investing in deploying into more geographies and securing more government certifications, as well as developing 'bring your own cloud' products to support customers with data residency and sovereignty needs. The company's security strategy emphasizes integrated solutions over point solutions to cover the entire security posture.	Governance	The emergence of an 'agentic era' where the focus shifts from writing code to validating and monitoring it, and where AI agents are increasingly performing triaging and investigations. This also implies a shift from human-centric UIs to agent-centric API/MCP interactions for troubleshooting.	Our teams executed very well and delivered revenue growth of 32% year-over-year, accelerating from 29% last quarter and 25% in the year ago quarter. Revenue was $9.1 billion, an increase of 32% year-over-year and above the high end of our guidance range. Our total ARR now exceeds $4 billion, and our quarterly revenue exceeded $1 billion for the first time. 56% of our customers now use four or more products, up from 51% a year ago. New logo annualized bookings set a new all-time record by a significant margin and more than doubled versus a year ago quarter. Our trailing 12-month net revenue retention percentage was in the low 120%, up from about 120 last quarter. We are outperforming all of our competitors at scale, and we're taking share.	For the second quarter, we expect revenues to be in the range of $1.07 billion to $1.08 billion, which represents a 29% to 31% year-over-year growth. For fiscal 2026, we expect revenues to be in the range of $4.3 billion to $4.34 billion, which represents 25% to 27% year-over-year growth. As with last quarter, we are applying a higher degree of conservatism to our largest customer. In Q2, we will be holding our DASH user conference which we estimate to cost about $15 million in which we have reflected in our operating income guidance.	Datadog's Q1 OpEx grew 31% year-over-year, which is an indication of the company's execution of its hiring plans to pursue long-term growth opportunities. The company has been investing in its go-to-market function, including reps and channel partners, for a number of years, investing ahead of certifications like FedRAMP to build pipeline, and plans for more investment in this area.

2026-02-10

About Expanding Eligible Market	About Competition	About The Broader Industry	Where Things Are Headed	Updates On Theme	Broader Themes Emerging	Bullish-Leaning Quotes (Short)	Bearish-Leaning Quotes (Short)	Hiring
Datadog is aggressively expanding into the security and service management sectors, with Cloud SIEM and 'On-call' (3,000+ customers) gaining traction. The company highlighted a massive untapped opportunity within the Fortune 500, where 48% are customers but the median ARR is still under $500,000. New product launches like Data Observability, Storage Management, and Feature Flags (foundation for AI agentic development) are broadening the platform's reach. The 'Datadog for AI' suite now has over 1,000 customers, with spans sent increasing 10x over the last six months.	Management stated they are 'pulling away' and taking share from any competitor with scale, specifically noting a consolidation motion replacing legacy vendors in nearly 100 deals worth tens of millions. Olivier Pomel dismissed recent industry M&A as involving 'not particularly winning companies.' The company is successfully displacing legacy SIEM and logging providers, with one Fortune 500 retailer expected to save millions by replacing a legacy logging product with Flex Logs. Even AI-native companies are moving away from homegrown/open-source tools to Datadog to prioritize developer velocity.	The industry is entering an 'agentic era' where the focus is shifting from writing code to validating and monitoring it. Pomel noted that massive hyperscaler CapEx (projected at $500B+ for the big three) will lead to 'very, very, very large increases in complexity,' which serves as a long-term tailwind for observability. There is a structural shift toward 'in-stream' analysis, as the volume of data from AI agents makes post-hoc analysis insufficient for maintaining system uptime.	Datadog is moving toward 'preemptive resolution,' where systems auto-diagnose and remediate issues in real-time before outages materialize. The company is heavily betting on the Model Context Protocol (MCP), with their MCP server seeing 11-fold growth in tool calls in Q4. For FY2026, Datadog expects an inflection in AI usage within applications and is guiding for 18-20% revenue growth, which includes a conservative outlook for its largest customer but 20%+ growth for the core business.	Cloud	The emergence of 'Agentic SREs' and the Model Context Protocol (MCP) as a standard for AI agents to interact with production data; a shift from human-centric UIs to agent-centric API/MCP interactions for troubleshooting.	"We signed 18 deals over $10 million in TCV this quarter, of which two were over $100 million."; "Revenue growth accelerated with our broad base of customers, excluding the AI natives, to 23%."; "14 of the top 20 AI-native companies are Datadog customers."; "Log management is now over $1 billion in ARR."	"For the full fiscal year 2026, we expect revenues... which represents 18% to 20% year-over-year growth."; "The median Datadog ARR for our Fortune 500 customers is still less than half a million dollars."; "RPO duration increased year over year as the mix of multiyear deals increased."

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Date	Comment	Comment Type	Comment Sentiment	Link	Price Reaction
2025-11-06	Datadog delivered a strong Q3 with accelerating security ARR (+mid‑50s%), broader AI-native growth (12% of revenue, more $1M+ customers), and the strongest non‑AI usage expansion in 12 quarters. Core business strength, improving sales productivity, and early Bits AI traction drove a positive stock reaction and increased confidence in sustained growth.	Earnings Transcript	Bullish		+23.40% (vs SPY: +22.83%)
2025-08-12	Datadog CFO David Obstler highlighted strong Q2 growth driven by AI customer adoption, expanding enterprise deals, and security momentum. Q3 focus: continued AI use case expansion, Bits AI monetization, security go-to-market ramp, and managing log spend with Flex/Frozen solutions to drive upsell and retention.	Conference Presentation	Neutral
2025-08-07	Strong Q2 beat with 28% y/y growth and AI-native momentum, but management flagged possible AI cohort volatility; focus on AI products, security expansion, and margin gains kept outlook solid.	Earnings Transcript	Mixed		-4.42% (vs SPY: -4.92%)
2026-02-10	Datadog's Q4 results sparked an 11.7% stock surge as revenue growth re-accelerated to 29% alongside record bookings (+37% y/y). Key takeaways included massive consolidation deals—including two $100 million+ contracts—and rapid adoption of Bits AI and MCP servers. The market's bullish reaction confirms high confidence in Datadog's AI leadership, effectively dismissing conservative FY26 guidance in favor of strong underlying usage and platform expansion momentum.	Earnings Transcript	Bullish	https://investors.datadoghq.com/	+11.68% (vs SPY: +11.97%)