Datadog, Inc. (DDOG) Q4 2025 Earnings Call Transcript
Access earnings results, analyst expectations, report, slides, earnings call, and transcript.
Overview
The earnings call summary indicates strong financial performance and optimistic guidance, with expectations of significant revenue growth and operating margins. The Q&A section reveals management's confidence in their strategic focus on AI and cloud, addressing competition effectively, and diversifying the customer base. Despite some avoidance in specifics, the overall sentiment is positive, especially with the emphasis on AI development and strategic partnerships. The absence of negative factors like margin decline or loss widening supports a positive outlook for stock price movement.
Key Financial Performance
Bookings $1.63 billion, up 37% year-over-year. This included some of the largest deals ever made, with 18 deals over $10 million in TCV, 2 over $100 million, and 1 8-figure land with a leading AI model company.
Revenue $953 million, an increase of 29% year-over-year. This growth was driven by broad-based strength across product lines and customer base, as well as acceleration in revenue growth from non-AI-native customers.
Customer Count 32,700 customers, up from about 30,000 a year ago. Additionally, 4,310 customers had an ARR of $100,000 or more, up from 3,610 a year ago, generating about 90% of ARR.
Free Cash Flow $291 million with a free cash flow margin of 31%. This reflects strong operational efficiency and cash generation.
Product Adoption 84% of customers use 2 or more products (up from 83% a year ago), 55% use 4 or more products (up from 50%), 33% use 6 or more products (up from 26%), 18% use 8 or more products (up from 12%), and 9% use 10 or more products (up from 6%). This indicates increasing penetration of the platform.
Infrastructure Monitoring ARR Over $1.6 billion. This includes innovations for visibility across various environments, such as on-prem, virtualized servers, and GPU fleets.
Log Management ARR Over $1 billion, with Flex Logs nearing $100 million in ARR. This reflects continued rapid growth in this area.
APM and DEM Products ARR Over $1 billion, with core APM product accelerating to mid-30% year-over-year growth, making it the fastest-growing core pillar.
Gross Margin 81.4%, compared to 81.2% last quarter and 81.7% a year ago. This indicates stable profitability levels.
Operating Income $230 million, with a 24% operating margin, consistent with the year-ago quarter.
Cash and Marketable Securities $4.47 billion, reflecting a strong liquidity position.
Billings $1.21 billion, up 34% year-over-year, indicating strong customer demand and bookings.
Remaining Performance Obligations (RPO) $3.46 billion, up 52% year-over-year, with current RPO growth at 40% year-over-year. This reflects an increase in multiyear deals.
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Operating Highlights
AI for Datadog: Launched Bits AI SRE Agent for root cause analysis and incident response, with over 2,000 trial and paying customers. Developing Bits AI Dev Agent and Bits AI Security Agent for code-level issue detection and security triage.
Datadog for AI: Introduced LLM Observability with over 1,000 customers and expanded features like LLM Experiments and GPU monitoring. Building security features for AI stack.
Log Management: Achieved over $1 billion in ARR, with Flex Logs nearing $100 million in ARR. Enhanced features like notebooks and log patterns.
Data Observability: Launched for general availability, enabling end-to-end visibility across the data lifecycle.
Digital Experience Monitoring: Introduced Product Analytics and RUM without Limits for better user experience insights.
Security: Launched Code Security and Infrastructure as Code security, enhancing cloud security offerings.
Software Delivery: Launched Feature Flags for canary rollouts and internal developer portal for faster release cadence.
Customer Base Expansion: Increased customer count to 32,700, with 4,310 customers having ARR of $100,000 or more. 48% of Fortune 500 companies are customers.
AI-Native Customers: 650 AI-native customers, with 19 spending $1 million or more annually. 14 of the top 20 AI-native companies are customers.
Large Deals: Signed 18 deals over $10 million in TCV, including 2 over $100 million.
Revenue Growth: Q4 revenue of $953 million, up 29% year-over-year. Free cash flow of $291 million with a 31% margin.
Product Adoption: 84% of customers use 2+ products, 55% use 4+, and 33% use 6+ products.
Retention: Gross revenue retention stable in mid-to-high 90s, with net retention at 120%.
AI Integration: Focused on integrating AI into Datadog platform and building products for AI observability and security.
Cloud Migration: Continued emphasis on cloud migration as a long-term growth driver.
Tool Consolidation: Actively replacing legacy vendors, with nearly 100 deals for tens of millions in new revenue.
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Risk or Challenges
Regulatory Risks: Potential risks and uncertainties mentioned in the forward-looking statements, which could cause actual results to differ materially. These include regulatory hurdles and compliance issues as referenced in the Form 10-Q and Form 10-K filings.
Competitive Pressures: The company faces competition from legacy vendors and other observability tools, as highlighted by the need to displace existing solutions in large enterprises.
Economic Uncertainties: Guidance reflects conservatism due to potential economic uncertainties, which could impact growth trends.
Supply Chain and Operational Risks: No explicit mention of supply chain disruptions, but operational risks are implied in the context of scaling issues and fragmented observability stacks faced by customers.
Strategic Execution Risks: Challenges in consolidating multiple tools and ensuring seamless integration across the platform, as evidenced by customer feedback and expansion efforts.
AI and Technology Risks: Risks associated with securing the AI stack against threats like prompt injection attacks, model hijacking, and data poisoning. Additionally, the rapid pace of AI innovation could pose challenges in maintaining competitive advantage.
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Guidance & Outlook
Revenue Expectations for Q1 2026: Revenues are expected to be in the range of $951 million to $961 million, representing a 25% to 26% year-over-year growth.
Revenue Expectations for FY 2026: Revenues are expected to be in the range of $4.06 billion to $4.10 billion, representing 18% to 20% year-over-year growth.
Operating Income for Q1 2026: Non-GAAP operating income is expected to be in the range of $195 million to $205 million, implying an operating margin of 21%.
Operating Income for FY 2026: Non-GAAP operating income is expected to be in the range of $840 million to $880 million, implying an operating margin of 21%.
Net Income Per Share for Q1 2026: Non-GAAP net income per share is expected to be in the range of $0.49 to $0.51 per share based on approximately 367 million weighted average diluted shares outstanding.
Net Income Per Share for FY 2026: Non-GAAP net income per share is expected to be in the range of $2.08 to $2.16 per share based on approximately 372 million weighted average diluted shares.
Capital Expenditures for FY 2026: Capital expenditures and capitalized software together are expected to be in the 4% to 5% of revenue range.
Growth Excluding Largest Customer: The business, excluding the largest customer, is modeled to grow at least 20% during FY 2026.
Market Trends and Long-Term Outlook: Digital transformation and cloud migration are expected to remain long-term secular growth drivers. AI adoption is anticipated to inflect further into customer applications, with Datadog integrating AI into its platform to enhance customer value and outcomes.
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Shareholder Return Plan
The selected topic was not discussed during the call.
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Key Q&A
Q:What are Olivier's views on the future of observability in the context of advancements in agentic frameworks and AI models?
A:Olivier believes that the advancements in agentic frameworks and AI models will lead to more applications being built faster, creating more complexity. Observability will play a crucial role in validating, testing, and ensuring the safety and functionality of these applications. He sees observability expanding into new domains and becoming a critical part of the AI development lifecycle.
Q:How does Datadog plan to evolve in a world with a mix of human and agentic SREs?
A:Datadog plans to incorporate more automation while maintaining user interfaces (UIs) for human interaction. They are building products to cater to both automated agents and human users. For example, their MCP server, currently in preview, is seeing explosive growth in usage.
Q:What drove an AI model company to switch from open-source tools to Datadog, resulting in an 8-figure deal?
A:The AI model company realized that building observability solutions in-house or using open-source tools was not cost-effective. Datadog demonstrated value by improving the velocity of engineers and providing a more efficient solution, which led to the switch.
Q:How does Datadog view the potential competition from LLMs in anomaly detection and observability?
A:Datadog acknowledges that LLMs are improving in analyzing broad data sets. However, Datadog's moat lies in its ability to aggregate and contextualize data, run real-time analysis, and proactively resolve issues before outages occur. They believe their embedded data plane and specialized models provide a significant advantage.
Q:How does Datadog ensure customers perceive value when their bills increase due to more AI usage?
A:Datadog ensures customers see value by demonstrating cost savings or increased revenue opportunities. They also show that using Datadog's platform is more cost-effective than adding another vendor or product.
Q:What assumptions are built into Datadog's 2026 guidance, and how does the AI cohort impact it?
A:The 2026 guidance assumes diversified growth across industries and geographies, with the core business growing at 20%+. The AI cohort is highly diversified, and while the largest customer has a lower growth rate, the overall AI cohort is contributing significantly to growth.
Q:How is Datadog addressing competition and the rise of LLMs?
A:Datadog sees no significant changes in competition and continues to take market share. They are confident in their approach to observability and are building new products, such as LLM Observability, to address emerging needs. They believe observability for LLMs should be integrated with the rest of the system.
Q:What is Datadog's perspective on the hyperscalers' CapEx growth and its impact on their business?
A:Datadog views the hyperscalers' CapEx growth as indicative of increasing complexity and the proliferation of systems, which will benefit their business. However, they find it challenging to directly map this CapEx to future value creation.
Q:How does Datadog plan to diversify its AI customer base and reduce concentration risk?
A:Datadog is actively engaging with more AI-native companies and hyperscalers, aiming to diversify its customer base. They are seeing strong inbound interest and expect to drive more business from the AI cohort.
Q:What factors contributed to the growth of Datadog's APM product?
A:The growth of Datadog's APM product is attributed to investments in simplifying deployment, enhancing digital experience monitoring, and expanding go-to-market efforts. These efforts have led to increased adoption and reaccelerated growth.
Q:How does Datadog view the trend of companies taking observability in-house?
A:Datadog believes that while some companies may choose to in-source observability for cultural reasons, it is not economically or strategically viable for most. Even hyperscalers with significant resources often choose Datadog for its efficiency and effectiveness.
Q:What is Datadog's approach to scaling its go-to-market team?
A:Datadog is focused on scaling its go-to-market team while maintaining productivity. They aim to cover more market segments and geographies, ensuring they can address the growing demand for their products.
Q:How does Datadog plan to capture value from its Bits AI initiative?
A:Datadog plans to demonstrate the value of Bits AI by showing how it reduces incident resolution time and costs. They aim to move from post-hoc analysis to proactive issue detection and resolution, leveraging real-time data analysis.
Q:What is Datadog's long-term margin framework?
A:Datadog prioritizes revenue growth and invests heavily in R&D and go-to-market efforts. They aim to balance these investments with margin improvements, allowing for incremental gains as revenues exceed targets.
Q:How does Datadog view the impact of large AI-native customers on gross margins?
A:Datadog does not see large AI-native customers as significantly dilutive to gross margins. The impact is more related to the size of the customer rather than their AI-native status.
Q:Review of Unclear Management Responses
A:Management avoided providing specific details on the percentage of revenue from the AI cohort this quarter, citing that they had not disclosed this information. Additionally, they did not provide a direct answer regarding the level of conservatism in their 2026 guidance assumptions for the largest customer.
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Earnings Word Cloud
The most frequently occurring keywords in this quarter's earning call
AI SRE
AI Security
AI agent
AI development
AI stack
APM
Fortune
GPU
MCP
SRE Agent
Security Agent
Today
acceleration
application AI
consolidation
core pillar
customer log
deployment
design
detail
developer
end visibility
experience security
incident response
inflection AI
infrastructure monitoring
innovation
insight
investigation
legacy
million dollar
observability tool
partner
platform product
product Observability
production context
recommendation
server
software delivery
solution
user experience
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The earnings call summary and Q&A indicate a positive outlook for Datadog. Strong revenue growth, AI integration, and new market opportunities, such as training workloads, are highlighted. Management's confidence in Q2 guidance and expansion into public sectors further supports a positive sentiment. Despite some lack of detailed metrics, the overall tone, coupled with strategic investments and customer additions, suggests a positive stock price movement.
DDOG Report
Frequently Asked Questions
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