Datadog unveils AI-powered security tools for cloud & code
New AI agent
The company has launched Bits AI Security Analyst, an AI agent that autonomously investigates potential threats and supports teams in managing risks with greater efficiency and accuracy. Integrated into Datadog Cloud SIEM, this agent triages security signals—starting with those generated by AWS CloudTrail—and performs detailed investigations into possible threats. Actionable, context-driven recommendations are then provided to help security teams respond more swiftly.
"AI has exponentially increased the ever-expanding backlog of security risks and vulnerabilities organizations deal with. This is because AI-native apps are not deterministic; they're more of a black box and have an increased surface area that leaves them open to vulnerabilities like prompt or code injection," said Prashant Prahlad, Vice President of Products, Security at Datadog.
"The latest additions to Datadog's Security Platform provide preventative and responsive measures—powered by continuous runtime visibility—to strengthen the security posture of AI workloads, from development to production."
Enhancing code security
Datadog Code Security, now generally available, aims to help developers and security teams detect and prioritise vulnerabilities not just in proprietary code but also within open-source libraries. The platform is specifically designed to uncover issues that may be present in large language model (LLM) integrations and AI-powered code, as these can be difficult to identify using traditional static analysis tools. The solution also uses artificial intelligence to facilitate the remediation of complex problems and ranks risks based on runtime activity and business impact.
Deep integrations with widely-used developer environments, including integrated development environments (IDEs) and GitHub, are intended to allow faster remediation workflows without interrupting established development processes.
Strengthening AI application security
With AI-native applications operating autonomously and often in unpredictable ways, new types of attacks such as prompt injection have become more prevalent. Datadog's updated security offerings include features to help organisations implement stronger security controls through measures such as separation of privileges, finely-tuned authorisation, and data classification throughout their AI application landscape and infrastructure.
Datadog LLM Observability, now also generally available, monitors the integrity of AI models, with tools to identify harmful or toxic behaviours across prompts and responses in enterprise AI applications. Other updates to Datadog Cloud Security support compliance with standards such as the NIST AI framework. This suite can uncover and remediate misconfigurations, unpatched vulnerabilities, and instances of unauthorised data or infrastructure access. The Sensitive Data Scanner, now supporting AWS S3 and RDS instances in preview, helps prevent personal or sensitive information from inadvertently being incorporated in LLM training data or inference processes.
Monitoring runtime risks
The complexity of AI-based applications increases the challenge for security analysts to manage alerts, distinguish credible threats from benign signals, and respond in a timely manner. According to Datadog, AI applications are at particular risk of attacks that could lead to resource exhaustion or financial damage if not detected early.
Bits AI Security Analyst is designed to reduce the workload on Security Operations Centres by providing initial investigations and filtering for more relevant threats. The new solution aims to enable teams to act on rich context and prioritised guidance so they can focus resources where they matter most.
Additional updates include Datadog Workload Protection, which now features LLM Isolation capabilities in preview. This enables continuous monitoring of interactions between LLMs and their host environments, helping to detect and prevent exploitation of vulnerabilities while enforcing controls to protect production AI models.
Datadog's new security features encompass Code Security, updated Cloud Security tools, Sensitive Data Scanner, Cloud SIEM, Workload and Application Protection, and expanded abilities within LLM Observability. These updates are designed to give organisations multiple layers of risk mitigation as they increasingly deploy AI in critical workflows.
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Non-GAAP operating income between $684 million and $694 million. Non-GAAP net income per share between $1.80 and $1.83, assuming approximately 364 million weighted average diluted shares outstanding. Datadog has not reconciled its expectations as to non-GAAP operating income, or as to non-GAAP net income per share, to their most directly comparable GAAP measure as a result of uncertainty regarding, and the potential variability of, reconciling items such as stock-based compensation and employer payroll taxes on equity incentive plans. Accordingly, reconciliation is not available without unreasonable effort, although it is important to note that these factors could be material to Datadog's results computed in accordance with GAAP. About Datadog Datadog is the observability and security platform for cloud applications. Our SaaS platform integrates and automates infrastructure monitoring, application performance monitoring, log management, user experience monitoring, cloud security and many other capabilities to provide unified, real-time observability and security for our customers' entire technology stack. Datadog is used by organisations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior, and track key business metrics. Forward-Looking Statements This press release and the earnings call referencing this press release contain 'forward-looking' statements, as that term is defined under the federal securities laws, including but not limited to statements regarding Datadog's strategy, product and platform capabilities, the growth in and ability to capitalise on long-term market opportunities including the pace and scope of cloud migration and digital transformation, gross margins and operating margins including with respect to third-party cloud infrastructure hosting costs, sales and marketing, research and development expenses, net interest and other income, cash taxes, investments and capital expenditures, and Datadog's future financial performance, including its outlook for the third quarter and the full year 2025 and related notes and assumptions. These forward-looking statements are based on Datadog's current assumptions, expectations and beliefs and are subject to substantial risks, uncertainties, assumptions and changes in circumstances that may cause Datadog's actual results, performance or achievements to differ materially from those expressed or implied in any forward-looking statement. The risks and uncertainties referred to above include, but are not limited to (1) our recent rapid growth may not be indicative of our future growth; (2) our history of operating losses; (3) our limited operating history; (4) our dependence on existing customers purchasing additional subscriptions and products from us and renewing their subscriptions; (5) our ability to attract new customers; (6) our ability to effectively develop and expand our sales and marketing capabilities; (7) risk of a security breach; (8) risk of interruptions or performance problems associated with our products and platform capabilities; (9) our ability to adapt and respond to rapidly changing technology or customer needs; (10) the competitive markets in which we participate; (11) risks associated with successfully managing our growth; and (12) general market, political, economic, and business conditions including concerns about trade policies, tariffs, reduced economic growth and associated decreases in information technology spending. These risks and uncertainties are more fully described in our filings with the Securities and Exchange Commission (SEC), including in the section entitled 'Risk Factors' in our Quarterly Report on Form 10-Q for the quarter ended March 31, 2025, filed with the SEC on May 7, 2025. Additional information will be made available in our Quarterly Report on Form 10-Q for the quarter ended June 30, 2025 and other filings and reports that we may file from time to time with the SEC. Moreover, we operate in a very competitive and rapidly changing environment. New risks emerge from time to time. It is not possible for our management to predict all risks, nor can we assess the impact of all factors on our business or the extent to which any factor, or combination of factors, may cause actual results to differ materially from those contained in any forward-looking statements we may make. In light of these risks, uncertainties and assumptions, we cannot guarantee future results, levels of activity, performance, achievements, or events and circumstances reflected in the forward-looking statements will occur. Forward-looking statements represent our beliefs and assumptions only as of the date of this press release. We disclaim any obligation to update forward-looking statements. About Non-GAAP Financial Measures Datadog discloses the following non-GAAP financial measures in this release and the earnings call referencing this press release: non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses (research and development, sales and marketing and general and administrative), non-GAAP operating income (loss), non-GAAP operating margin, non-GAAP net income (loss), non-GAAP net income (loss) per diluted share, non-GAAP net income (loss) per basic share, free cash flow and free cash flow margin. Datadog uses each of these non-GAAP financial measures internally to understand and compare operating results across accounting periods, for internal budgeting and forecasting purposes, for short- and long-term operating plans, and to evaluate Datadog's financial performance. Datadog believes they are useful to investors, as a supplement to GAAP measures, in evaluating its operational performance, as further discussed below. Datadog's non-GAAP financial measures may not provide information that is directly comparable to that provided by other companies in its industry, as other companies in its industry may calculate non-GAAP financial results differently, particularly related to non-recurring and unusual items. In addition, there are limitations in using non-GAAP financial measures because the non-GAAP financial measures are not prepared in accordance with GAAP and may be different from non-GAAP financial measures used by other companies and exclude expenses that may have a material impact on Datadog's reported financial results. Non-GAAP financial measures should not be considered in isolation from, or as a substitute for, financial information prepared in accordance with GAAP. A reconciliation of the historical non-GAAP financial measures to their most directly comparable GAAP measures has been provided in the financial statement tables included below in this press release. Datadog defines non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses (research and development, sales and marketing and general and administrative), non-GAAP operating income (loss), non-GAAP operating margin and non-GAAP net income (loss) as the respective GAAP balances, adjusted for, as applicable: (1) stock-based compensation expense; (2) the amortisation of acquired intangibles; (3) employer payroll taxes on employee stock transactions; (4) M&A transaction costs; (5) amortisation of issuance costs; and (6) an assumed provision for income taxes based on our long-term projected tax rate. Non-GAAP financial measures prior to April 1, 2025 have not been adjusted for M&A transaction costs, as such costs were not material to our results of operations in such prior periods. Our estimated long-term projected tax rate is subject to change for a variety of reasons, including the rapidly evolving global tax environment, significant changes in Datadog's geographic earnings mix, or other changes to our strategy or business operations. We will re-evaluate our long-term projected tax rate as appropriate. Datadog defines free cash flow as net cash provided by operating activities, minus capital expenditures and minus capitalised software development costs, if any. Investors are encouraged to review the reconciliation of these historical non-GAAP financial measures to their most directly comparable GAAP financial measures. Management believes these non-GAAP financial measures are useful to investors and others in assessing Datadog's operating performance due to the following factors: Stock-based compensation. Datadog utilises stock-based compensation to attract and retain employees. It is principally aimed at aligning their interests with those of its stockholders and at long-term retention, rather than to address operational performance for any particular period. As a result, stock-based compensation expenses vary for reasons that are generally unrelated to financial and operational performance in any particular period. Amortization of acquired intangibles. Datadog views amortisation of acquired intangible assets as items arising from pre-acquisition activities determined at the time of an acquisition. While these intangible assets are evaluated for impairment regularly, amortisation of the cost of acquired intangibles is an expense that is not typically affected by operations during any particular period. Employer payroll taxes on employee stock transactions. Datadog excludes employer payroll tax expense on equity incentive plans as these expenses are tied to the exercise or vesting of underlying equity awards and the price of Datadog's common stock at the time of vesting or exercise. As a result, these taxes may vary in any particular period independent of the financial and operating performance of Datadog's business. M&A transaction costs. Datadog views acquisition-related expenses, such as transaction costs, as costs that are not necessarily reflective of operational performance during a period. In particular, Datadog believes the consideration of measures that exclude such expenses can assist in the comparison of operational performance in different periods which may or may not include such expenses. Amortisation of issuance costs. In June 2020 and December 2024, Datadog issued $747.5 million of 0.125% convertible senior notes due 2025 and $1.0 billion of 0% convertible senior notes due 2029, respectively. Debt issuance costs, which reduce the carrying value of the convertible debt instrument, are amortized as interest expense over the term. The expense for the amortization of debt issuance costs is a non-cash item, and we believe the exclusion of this interest expense will provide for a more useful comparison of our operational performance in different periods. Additionally, Datadog's management believes that the non-GAAP financial measure free cash flow is meaningful to investors because it is a measure of liquidity that provides useful information in understanding and evaluating the strength of our liquidity and future ability to generate cash that can be used for strategic opportunities or investing in our business. Free cash flow represents net cash provided by operating activities, reduced by capital expenditures and capitalized software development costs, if any. The reduction of capital expenditures and amounts capitalized for software development facilitates comparisons of Datadog's liquidity on a period-to-period basis and excludes items that management does not consider to be indicative of our liquidity. Operating Metrics Datadog's number of customers with ARR of $100,000 or more is based on the ARR of each customer, as of the last month of the quarter. We define the number of customers as the number of accounts with a unique account identifier for which we have an active subscription in the period indicated. Users of our free trials or tier are not included in our customer count. A single organisation with multiple divisions, segments or subsidiaries is generally counted as a single customer. However, in some cases where they have separate billing terms, we may count separate divisions, segments or subsidiaries as multiple customers. We define ARR as the annualised revenue run-rate of subscription agreements from all customers at a point in time. We calculate ARR by taking the monthly recurring revenue, or MRR, and multiplying it by 12. MRR for each month is calculated by aggregating, for all customers during that month, monthly revenue from committed contractual amounts, additional usage, usage from subscriptions for a committed contractual amount of usage that is delivered as used, and monthly subscriptions. ARR and MRR should be viewed independently of revenue, and do not represent our revenue under GAAP on a monthly or annualised basis, as they are operating metrics that can be impacted by contract start and end dates and renewal rates. ARR and MRR are not intended to be replacements or forecasts of revenue.
Techday NZ
08-08-2025
- Techday NZ
Datadog Q2 revenue jumps 28 per cent to USD $827 million on AI, cloud demand
Datadog has reported its financial results for the second quarter of 2025, posting a 28 per cent year-over-year increase in revenue to USD $827 million. The company's growth in the quarter was attributed to the expansion of its customer base, particularly among larger organisations. Datadog disclosed that it now has approximately 3,850 customers with annual recurring revenue (ARR) of USD $100,000 or more, up 14 per cent from roughly 3,390 such customers a year ago. Customer growth The second quarter saw continued traction among enterprise clients and organisations scaling their use of Datadog's cloud monitoring and security platform. This expansion was reflected not only in revenue growth, but also in key operational metrics. Highlighting the quarter, Datadog introduced more than 125 new products, capabilities, and features. These were showcased during the company's user conference, DASH. "Datadog had a strong second quarter, with 28 per cent year-over-year revenue growth, USD $200 million in operating cash flow, and USD $165 million in free cash flow," said Olivier Pomel, Co-Founder and Chief Executive Officer of Datadog. Pomel also noted, "At our DASH 2025 user conference, we showcased our rapid pace of innovation, announcing over 125 new innovations to help our customers observe, secure, and act on their complex cloud environments and AI tech stacks." Financial performance For the three months to June 30, 2025, Datadog's GAAP operating loss was USD $(36) million, with a GAAP operating margin of (4)%. The company reported non-GAAP operating income of USD $164 million and a non-GAAP operating margin of 20 per cent for the quarter. GAAP net income per diluted share stood at USD $0.01, while non-GAAP net income per diluted share reached USD $0.46. Datadog's operating cash flow for the quarter was USD $200 million, with free cash flow of USD $165 million. The company ended the period with USD $3.9 billion in cash, cash equivalents, and marketable securities. Product and business highlights Datadog advanced its offerings with key launches including the roll-out of its full range of products and services in the Amazon Web Services' Asia-Pacific (Sydney) Region, building on its presence in North America, Asia, and Europe. The company introduced three new AI agents - Bits AI SRE, Bits AI Dev Agent, and Bits AI Security Analyst - to support interactive investigations and asynchronous code fixes across operations, development, and security functions. Additional product releases included Archive Search, FlexFrozen, and CloudPrem in the log management suite. These are aimed at optimising logging costs and meeting stringent data requirements for regulated industries. The Internal Developer Portal was launched as the first developer portal built on live observability data, and new security products - Code Security, Bits AI Security Analyst, and Workload Protection - were introduced to address security across cloud and AI environments. Datadog also unveiled new capabilities for AI operations, such as AI Agent Monitoring, LLM Experiments, and AI Agents Console, which provide end-to-end visibility and governance of AI agents. From its AI Research division, the company announced Toto, an open-weights model trained with internal observability data, and BOOM, a time series benchmark for observability metrics. Recognition and compliance Among other developments during the quarter, Datadog was named a Leader in the Gartner Magic Quadrant for Observability Platforms for the fifth consecutive year. The company also joined the S&P 500 Index and was added to both the Forbes Global 2000 and the Forbes Global 2000 United States Lists for 2025. In regulatory and compliance moves, Datadog announced progress towards attaining Federal Risk and Authorization Management Program (FedRAMP) High authorisation, which would enable federal agencies to use its monitoring and security products in line with strict compliance standards. Guidance for 2025 Datadog provided its outlook for the third quarter and the full fiscal year 2025. For the third quarter, the company expects revenue between USD $847 million and USD $851 million, and non-GAAP operating income between USD $176 million and USD $180 million. Full year 2025 revenue is projected to be between USD $3.312 billion and USD $3.322 billion, with non-GAAP operating income in the range of USD $684 million to USD $694 million. Non-GAAP net income per share for the full year is expected to be between USD $1.80 and USD $1.83, based on approximately 364 million weighted average diluted shares outstanding.
