Datadog launches domain-specific AI agents & LLM tools
New AI agents
The company has introduced Bits AI SRE, Bits AI Dev Agent, and Bits AI Security Analyst, each configured to serve specific engineering, operations, and security functions. These agents are designed to support real-time incident response, DevOps tasks, and security workflows for development, security, and operations teams.
The AI agents operate on a shared system of core tasks, including data querying, anomaly analysis, and infrastructure scaling. This architecture allows Datadog to roll out new agents efficiently while maintaining consistency in the user experience. The system integrates a broad set of observability data, enabling precise insights and actions for managing risks within cloud-based applications.
Yanbing Li, Chief Product Officer at Datadog, commented on the company's approach: Datadog is uniquely positioned to deliver value with AI as a platform that has a wealth of clean, rich data—we process trillions of data points and are embedded in our customers' critical engineering, developer and security workflows. With these advancements in AI reasoning and multi-modality, we've gone beyond helping organizations understand their availability, security, performance and reliability. We now enable human-in-the-middle workflows by guiding customers on what to look for and where to start looking, and augment their ability to take action.
Bits AI SRE, which is now in limited availability, acts as an on-call responder for incidents by performing early triage and providing investigation findings before human responders intervene. It allocates incidents, produces real-time summaries, and generates initial post-mortem drafts to save teams time.
Bits AI Dev Agent, currently in preview, identifies code issues, suggests fixes, and can open pull requests directly within the source control management systems organisations use. Bits AI Security Analyst, also in preview, automatically investigates cloud security signals, conducts in-depth threat investigations, and produces actionable resolution recommendations, aiming to reduce response times for security incidents.
Darren Trzynka, Senior Cloud Architect at Thomson Reuters, commented on Bits AI's impact: At Thomson Reuters, we're focused on maximizing operational efficiency and accelerating innovation at scale through generative AI solutions. Bits AI allows operations and downstream platform teams to receive the full context of the investigation—from the initial monitor trigger to conclusion—driving down resolution time significantly freeing them up to do more.
Additional Applied AI features
The updates include two new features in preview. Proactive App Recommendations analyses telemetry collected by Datadog to suggest performance improvements or actions, such as optimising slow queries and addressing code issues, before users are impacted. The APM Investigator helps engineers troubleshoot latency spikes by automating bottleneck identification and recommending fixes.
LLM Observability suite announced
Datadog has also released a suite of tools designed to provide observability for agentic AI—software agents built with LLMs and similar technologies—in production environments. The new products include AI Agent Monitoring, LLM Experiments, and AI Agents Console.
Yrieix Garnier, Vice President of Product at Datadog, addressed the motivations behind these offerings: A recent study found only 25 percent of AI initiatives are currently delivering on their promised ROI—a troubling stat given the sheer volume of AI projects companies are pursuing globally. Today's launches aim to help improve that number by providing accountability for companies pushing huge budgets toward AI projects. The addition of AI Agent Monitoring, LLM Experiments and AI Agents Console to our LLM Observability suite gives our customers the tools to understand, optimize and scale their AI investments.
AI Agent Monitoring, now generally available, provides a mapped overview of each agent's decision-making route, including inputs, tool calls, and outputs, displayed in an interactive graph. This enables engineers to diagnose latency spikes or unexpected behaviours and connect them to quality, security, and cost measures across distributed systems.
Mistral AI's Co-founder and CTO, Timothée Lacroix, provided further industry perspective: Agents represent the evolution beyond chat assistants, unlocking the potential of generative AI. As we equip these agents with more tools, comprehensive observability is essential to confidently transition use cases into production. Our partnership with Datadog ensures teams have the visibility and insights needed to deploy agentic solutions at scale.
LLM Experiments, in preview, enables users to compare the effects of changes to prompts or models using datasets from live or uploaded sources. This aims to support quantifiable improvements in cost, response accuracy, and throughput, and prevent unintended regressions in AI application performance.
Michael Gerstenhaber, Vice President of Product at Anthropic, commented: AI agents are quickly graduating from concept to production. Applications powered by Claude 4 are already helping teams handle real-world tasks in many domains, from customer support to software development and R&D. As these agents take on more responsibility, observability becomes key to ensuring they behave safely, deliver value, and stay aligned with user and business goals. We're very excited about Datadog's new LLM Observability capabilities that provide the visibility needed to scale these systems with confidence.
Datadog has also introduced AI Agents Console, currently in preview, to allow organisations to centrally oversee both in-house and third-party AI agents, track their usage and impact, and monitor for potential security or compliance issues as external agents are embedded into critical business workflows.
Armita Peymandoust, Senior Vice President, Software Engineering at Salesforce, said: As enterprises scale digital labour, having clear visibility into how AI agents drive business impact has become mission critical. Customers are already seeing strong success with their AI deployments using Salesforce's Agentforce, which is built on a foundation of openness and trust. That foundation is further strengthened by our partner ecosystem that provides our customers even greater availability to tailored solutions that help them manage their AI agents confidently. Datadog's latest advances in deep observability will further support our vision and unlock another level of AI agent transparency and scale for organizations.
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Datadog Unveils Second Quarter 2025 Financial Results
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[1] GAAP net income per diluted share was $0.01; non-GAAP net income per diluted share was $0.46. 1 Operating cash flow was $200 million, with free cash flow of $165 million. Cash, cash equivalents, and marketable securities were $3.9 billion as of June 30, 2025. [1] The three months ended June 30, 2025 are adjusted for M&A transaction costs of $1.4 million, and these adjustments are applied prospectively, as these costs were not material to the consolidated results of operations in the prior periods. Second Quarter & Recent Business Highlights: As of June 30, 2025, we had about 3,850 customers with ARR of $100,000 or more, an increase of 14 per cent from about 3,390 as of June 30, 2024. Launched its full range of products and services on the Amazon Web Services' Asia-Pacific (Sydney) Region, adding to existing locations in North America, Asia, and Europe. Named a Leader in the Gartner Magic Quadrant for Observability Platforms, 2025. 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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
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Datadog Q2 revenue jumps 28 per cent to USD $827 million on AI, cloud demand
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Red Hat named leader for multicloud container platforms by Forrester
Red Hat has been named a Leader in The Forrester Wave: Multicloud Container Platforms, Q3 2025 report, based on its performance in the multicloud container platform market. Forrester's assessment The Forrester Wave report evaluated several vendors in the multicloud container platform market, focusing on both the current offering and company strategy categories. Red Hat was highlighted for scoring the highest among all evaluated vendors in these categories. The report described OpenShift as "a good fit for enterprises that prioritise support, reliability, and advanced engineering, particularly in regulated industries such as financial services." It also observed that "customers consistently praise Red Hat's enterprise-grade offerings and support, especially for managed services." Forrester noted Red Hat's capabilities in Kubernetes, saying, "Red Hat excels in core Kubernetes areas, offering robust operator options, powerful management, GitOps automation, and flexible interfaces via a GUI or command-line interface (CLI). OpenShift's SLAs of 99.95% for public cloud managed-service versions showcase Red Hat's capacity to engineer capabilities beyond those of native public cloud services." The report additionally stated, "Developers will find just about everything they need with Red Hat's above-par scores in developer experience, service and application catalogues, microservices, service mesh, DevOps automation, and integration." Technical focus and AI integration Beyond container management, Red Hat is extending its efforts in hybrid cloud solutions. The company is leveraging its stack - including Red Hat Enterprise Linux - to improve support for generative AI development and operations, with an emphasis on model serving and advanced inference. Customer priorities and market needs The report noted that OpenShift has demonstrated suitability for organisations operating in highly regulated industries, such as financial services, where support and reliability are considered essential. The platform's managed services, which offer defined service-level agreements, were singled out for positive feedback from customers. The importance of a strong enterprise support model for public cloud deployments was also highlighted in the analysis. Leadership statement Mike Barrett, Vice President & General Manager, Hybrid Cloud Platforms, Red Hat, said: "Red Hat continues to provide the leading platform for organisations navigating the complexities of multicloud environments. Being named a Leader in The Forrester WaveTM for Multicloud Container Platforms reinforces our commitment to delivering robust, enterprise-grade solutions that empower our customers to innovate with confidence across their hybrid cloud footprints. Our focus on core Kubernetes capabilities, strong developer experience and strategic AI integrations positions us well for the evolving needs of the market. Sovereign cloud, coupled with the digital independence required to get the most from AI, have made multicloud investments a leading priority for our global customers." Developer perspective The Forrester evaluation recognised Red Hat's OpenShift for the breadth of its support for developers, including tooling for DevOps automation, service catalogues, and integration features. The solution was described as delivering above-average scores in developer experience, microservices, and service mesh capabilities. Market context As enterprise IT organisations continue to adopt hybrid and multicloud strategies, platforms capable of delivering consistent operations and supporting evolving application needs are increasingly important. The 99.95% public cloud managed service SLA cited by Forrester underlines the attention to reliability and service continuity expected in this sector. Red Hat continues to broaden the reach of its hybrid cloud portfolio, applying the foundation of Red Hat Enterprise Linux to support both traditional enterprise workloads and emerging technologies such as generative AI.
