Technology

Datadog Announces Second Quarter 2024 Financial Results

Published

2 years ago

August 8, 2024

Second quarter revenue grew 27% year-over-year to $645 million

Strong growth of larger customers, with about 3,390 $100k+ ARR customers, up from about 2,990 a year ago

Announced general availability of LLM Observability

Announced innovations for Generative AI, Observability, Cloud Security, Product Analytics, Software Delivery, and Cloud Service Management at DASH 2024

NEW YORK, Aug. 8, 2024 /PRNewswire/ — Datadog, Inc. (NASDAQ:DDOG), the monitoring and security platform for cloud applications, today announced financial results for its second quarter ended June 30, 2024.

“Datadog executed well in the second quarter, with 27% year-over-year revenue growth, continued customer growth, and expanding multi-product adoption across our platform,” said Olivier Pomel, co-founder and CEO of Datadog.

Pomel added, “At our DASH 2024 user conference, we announced dozens of new products and capabilities, delivering innovations to help our customers observe their environments, secure their infrastructures and workloads, and act to remediate problems before they affect users.”

Second Quarter 2024 Financial Highlights:

Revenue was $645 million, an increase of 27% year-over-year.GAAP operating income was $13 million; GAAP operating margin was 2%.Non-GAAP operating income was $158 million; non-GAAP operating margin was 24%.GAAP net income per diluted share was $0.12; non-GAAP net income per diluted share was $0.43.Operating cash flow was $164 million, with free cash flow of $144 million.Cash, cash equivalents, and marketable securities were $3.0 billion as of June 30, 2024.

Second Quarter & Recent Business Highlights:

As of June 30, 2024, we had about 3,390 customers with ARR of $100,000 or more, an increase of 13% from about 2,990 as of June 30, 2023.Announced that Yanbing Li joined as Chief Product Officer. Li has more than 25 years of product, technology and engineering experience, having led global engineering, operations and infrastructure teams at Aurora, Google and VMware.Announced that David Galloreese joined as Chief People Officer. Galloreese has more than 20 years of human resources experience at tech companies and large-scale, high visibility brands, like Figma, Wells Fargo, and Walmart.Announced new additions to our security product portfolio, including Agentless Scanning, Data Security and Code Security, which allow security teams to easily secure their code, cloud environments and production applications.Announced the launch of Live Debugger, a new tool that enables developers to step through code in production environments and find the exact root cause of coding errors.Announced a unified experience integrating the Datadog Agent and OpenTelemetry Collector. With this announcement, customers can use Datadog’s fully supported Agent while accessing the complete capabilities of the OpenTelemetry Collector; onboard quickly using the existing collector configuration; and centralize the management of collectors remotely – all while benefiting from Datadog’s reliability and global support team.Announced Log Workspaces, a suite of capabilities in a powerful, collaborative space that allows analysts and engineers from all teams in an organization to connect logs and other datasets, and build multi-stage queries that allow for sophisticated analytics to answer complex questions on business, securities and application issues.Announced Datadog On-Call, a modern on-call experience with observability-enriched paging and seamless incident management workflows.Announced the general availability of LLM Observability, which allows AI application developers and machine learning engineers to efficiently monitor, improve and secure large language model applications.Announced Datadog Kubernetes Autoscaling, a set of capabilities that intelligently automates resource optimization and can automatically scale customers’ Kubernetes environments based on real-time and historical utilization metrics.Announced the general availability of Data Jobs Monitoring, a new product that helps data platform teams and data engineers detect problematic Spark and Databricks jobs anywhere in their data pipelines, remediate failed and long-running-jobs faster, and optimize over-provisioned compute resources to reduce costs.Announced the general availability of Datadog App Builder, a low-code development tool that helps teams rapidly create self-service applications and integrate them securely into their monitoring stacks.Released its State of Cloud Costs 2024 report. The report found organizations that use graphics processing unit (GPU) instances have increased their average spending on those instances by 40% in the last year.

Third Quarter and Full Year 2024 Outlook:

Based on information as of today, August 8, 2024, Datadog is providing the following guidance:

Third Quarter 2024 Outlook:Revenue between $660 million and $664 million.Non-GAAP operating income between $146 million and $150 million.Non-GAAP net income per share between $0.38 and $0.40, assuming approximately 360 million weighted average diluted shares outstanding.Full Year 2024 Outlook:Revenue between $2.62 billion and $2.63 billion.Non-GAAP operating income between $620 million and $630 million.Non-GAAP net income per share between $1.62 and $1.66, assuming approximately 360 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.

Conference Call Details:

What: Datadog financial results for the second quarter of 2024 and outlook for the third quarter and the full year 2024When: August 8, 2024 at 8:00 A.M. Eastern Time (5:00 A.M. Pacific Time)Dial in: To access the call in the U.S., please register here. Callers are encouraged to dial into the call 10 to 15 minutes prior to the start to prevent any delay in joining.Webcast: https://investors.datadoghq.com (live and replay)Replay: A replay of the call will be archived on the investor relations website

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 organizations 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 capitalize 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 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 2024 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 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 Annual Report on Form 10-K for the year ended December 31, 2023, filed with the SEC on February 24, 2024. Additional information will be made available in our Quarterly Report on Form 10-Q for the quarter ended June 30, 2024 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 amortization of acquired intangibles; (3) employer payroll taxes on employee stock transactions; (4) amortization of issuance costs; and (5) an assumed provision for income taxes based on our long-term projected tax rate. 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 capitalized 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 utilizes 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 amortization 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, amortization 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.

Amortization of issuance costs. In June 2020, Datadog issued $747.5 million of convertible senior notes due 2025, which bear interest at an annual fixed rate of 0.125%. 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 organization 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 annualized 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 annualized 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.

Datadog, Inc.

Condensed Consolidated Statements of Operations

(In thousands, except per share data; unaudited)

Three Months Ended

June 30,

Six Months Ended

June 30,

2024

2023

2024

2023

Revenue

$ 645,279

$ 509,460

$ 1,256,532

$ 991,174

Cost of revenue (1)(2)(3)

123,499

101,846

233,597

201,760

Gross profit

521,780

407,614

1,022,935

789,414

Operating expenses:

Research and development (1)(3)

274,599

239,494

544,587

468,972

Sales and marketing (1)(2)(3)

187,005

147,455

360,886

292,426

General and administrative (1)(3)

47,558

42,671

92,848

84,992

Total operating expenses

509,162

429,620

998,321

846,390

Operating income (loss)

12,618

(22,006)

24,614

(56,976)

Other income (loss):

Interest expense (4)

(1,477)

(1,526)

(2,851)

(3,707)

Interest income and other income, net

36,652

22,624

72,215

39,351

Other income, net

35,175

21,098

69,364

35,644

Income (loss) before provision for income taxes

47,793

(908)

93,978

(21,332)

Provision for income taxes

3,969

3,061

7,523

6,723

Net income (loss)

$ 43,824

$ (3,969)

$ 86,455

$ (28,055)

Net income (loss) per share – basic

$ 0.13

$ (0.01)

$ 0.26

$ (0.09)

Net income (loss) per share – diluted

$ 0.12

$ (0.01)

$ 0.24

$ (0.09)

Weighted average shares used in calculating net income (loss) per share:

Basic

334,941

322,215

333,373

320,788

Diluted

356,740

322,215

356,650

320,788

(1) Includes stock-based compensation expense as follows:

Cost of revenue

$ 6,393

$ 4,157

$ 11,920

$ 7,882

Research and development

87,105

75,730

175,518

150,433

Sales and marketing

29,201

25,884

57,732

48,898

General and administrative

11,953

12,566

24,515

23,852

Total

$ 134,652

$ 118,337

$ 269,685

$ 231,065

(2) Includes amortization of acquired intangibles as follows:

Cost of revenue

$ 1,281

$ 2,064

$ 3,308

$ 4,080

Sales and marketing

205

206

410

409

Total

$ 1,486

$ 2,270

$ 3,718

$ 4,489

(3) Includes employer payroll taxes on employee stock transactions as follows:

Cost of revenue

$ 68

$ 109

$ 260

$ 169

Research and development

6,589

5,360

17,408

9,953

Sales and marketing

608

1,253

2,761

2,028

General and administrative

1,521

1,143

3,578

2,108

Total

$ 8,786

$ 7,865

$ 24,007

$ 14,258

(4) Includes amortization of issuance costs as follows:

Interest expense

$ 910

$ 846

$ 1,760

$ 1,691

Total

$ 910

$ 846

$ 1,760

$ 1,691

Datadog, Inc.

Condensed Consolidated Balance Sheets

(In thousands; unaudited)

June 30,
2024

December 31,
2023

ASSETS

CURRENT ASSETS:

Cash and cash equivalents

$ 410,963

$ 330,339

Marketable securities

2,549,143

2,252,559

Accounts receivable, net of allowance for credit losses of $12,902 and $12,096 as
of June 30, 2024 and December 31, 2023, respectively

533,292

509,279

Deferred contract costs, current

49,518

44,938

Prepaid expenses and other current assets

49,174

41,022

Total current assets

3,592,090

3,178,137

Property and equipment, net

198,911

171,872

Operating lease assets

166,941

126,562

Goodwill

350,864

352,694

Intangible assets, net

5,804

9,617

Deferred contract costs, non-current

77,040

73,728

Other assets

20,741

23,462

TOTAL ASSETS

$ 4,412,391

$ 3,936,072

LIABILITIES AND STOCKHOLDERS’ EQUITY

CURRENT LIABILITIES:

Accounts payable

$ 115,991

$ 87,712

Accrued expenses and other current liabilities

104,791

127,631

Operating lease liabilities, current

24,565

21,974

Convertible senior notes, net, current

743,970

—

Deferred revenue, current

801,562

765,735

Total current liabilities

1,790,879

1,003,052

Operating lease liabilities, non-current

193,835

138,128

Convertible senior notes, net, non-current

—

742,235

Deferred revenue, non-current

14,049

21,210

Other liabilities

6,320

6,093

Total liabilities

2,005,083

1,910,718

STOCKHOLDERS’ EQUITY:

Common stock

Additional paid-in capital

2,484,264

2,181,267

Accumulated other comprehensive loss

(9,716)

(2,218)

Accumulated deficit

(67,243)

(153,698)

Total stockholders’ equity

2,407,308

2,025,354

TOTAL LIABILITIES AND STOCKHOLDERS’ EQUITY

$ 4,412,391

$ 3,936,072

Datadog, Inc.

Condensed Consolidated Statements of Cash Flow

(In thousands; unaudited)

Three Months Ended

June 30,

Six Months Ended

June 30,

2024

2023

2024

2023

CASH FLOWS FROM OPERATING ACTIVITIES:

Net income (loss)

$ 43,824

$ (3,969)

$ 86,455

$ (28,055)

Adjustments to reconcile net income (loss) to net cash provided by operating activities:

Depreciation and amortization

12,440

10,550

25,335

20,825

(Accretion) amortization of (discounts) premiums on marketable securities

(12,569)

(8,096)

(26,695)

(13,291)

Amortization of issuance costs

910

846

1,760

1,691

Amortization of deferred contract costs

12,450

9,348

24,294

17,996

Stock-based compensation, net of amounts capitalized

134,652

118,337

269,685

231,065

Non-cash lease expense

6,781

6,252

13,591

12,196

Allowance for credit losses on accounts receivable

3,842

2,579

6,574

6,311

Loss on disposal of property and equipment

300

333

343

421

Changes in operating assets and liabilities:

Accounts receivable, net

(86,076)

31,366

(30,586)

60,139

Deferred contract costs

(19,534)

(15,868)

(32,170)

(27,618)

Prepaid expenses and other current assets

5,632

(1,013)

(8,443)

(16,823)

Other assets

(443)

2,077

2,171

2,241

Accounts payable

48,692

6,352

31,570

24,897

Accrued expenses and other liabilities

(8,423)

(16,009)

(15,856)

(44,089)

Deferred revenue

21,946

10,073

28,666

39,039

Net cash provided by operating activities

164,424

153,158

376,694

286,945

CASH FLOWS FROM INVESTING ACTIVITIES:

Purchases of marketable securities

(602,950)

(632,547)

(1,240,301)

(1,390,334)

Maturities of marketable securities

564,319

520,669

965,985

1,018,317

Proceeds from sale of marketable securities

15,292

36,633

Purchases of property and equipment

(4,415)

(2,339)

(18,573)

(11,078)

Capitalized software development costs

(16,229)

(9,087)

(27,594)

(17,798)

Cash paid for acquisition of businesses; net of cash acquired

(444)

(2,025)

(444)

(2,025)

Net cash used in investing activities

(59,711)

(110,037)

(320,919)

(366,285)

CASH FLOWS FROM FINANCING ACTIVITIES:

Proceeds from exercise of stock options

1,753

5,436

3,944

7,534

Proceeds for issuance of common stock under the employee stock purchase plan

22,507

19,986

22,507

19,986

Repayments of convertible senior notes

(25)

—

(25)

—

Net cash provided by financing activities

24,235

25,422

26,426

27,520

Effect of exchange rate changes on cash, cash equivalents and restricted cash

(203)

213

(1,577)

836

NET DECREASE IN CASH, CASH EQUIVALENTS AND RESTRICTED CASH

128,745

68,756

80,624

(50,984)

CASH, CASH EQUIVALENTS AND RESTRICTED CASH—Beginning of period

282,218

222,548

330,339

342,288

CASH, CASH EQUIVALENTS AND RESTRICTED CASH—End of period

$ 410,963

$ 291,304

$ 410,963

$ 291,304

RECONCILIATION OF CASH AND CASH EQUIVALENTS WITHIN THE CONDENSED CONSOLIDATED BALANCE SHEETS TO THE AMOUNTS
SHOWN IN THE STATEMENTS OF CASH FLOWS ABOVE:

Cash and cash equivalents

$ 410,963

$ 291,304

$ 410,963

$ 291,304

Total cash and cash equivalents

$ 410,963

$ 291,304

$ 410,963

$ 291,304

Datadog, Inc.

Reconciliation from GAAP to Non-GAAP Results

(In thousands, except per share data; unaudited)

Three Months Ended

June 30,

Six Months Ended

June 30,

2024

2023

2024

2023

Reconciliation of gross profit and gross margin

GAAP gross profit

$ 521,780

$ 407,614

$ 1,022,935

$ 789,414

Plus: Stock-based compensation expense

6,393

4,157

11,920

7,882

Plus: Amortization of acquired intangibles

1,281

2,064

3,308

4,080

Plus: Employer payroll taxes on employee stock transactions

109

260

169

Non-GAAP gross profit

$ 529,522

$ 413,944

$ 1,038,423

$ 801,545

GAAP gross margin

81 %

80 %

81 %

80 %

Non-GAAP gross margin

82 %

81 %

83 %

81 %

Reconciliation of operating expenses

GAAP research and development

$ 274,599

$ 239,494

$ 544,587

$ 468,972

Less: Stock-based compensation expense

(87,105)

(75,730)

(175,518)

(150,433)

Less: Employer payroll taxes on employee stock transactions

(6,589)

(5,360)

(17,408)

(9,953)

Non-GAAP research and development

$ 180,905

$ 158,404

$ 351,661

$ 308,586

GAAP sales and marketing

$ 187,005

$ 147,455

$ 360,886

$ 292,426

Less: Stock-based compensation expense

(29,201)

(25,884)

(57,732)

(48,898)

Less: Amortization of acquired intangibles

(205)

(206)

(410)

(409)

Less: Employer payroll taxes on employee stock transactions

(608)

(1,253)

(2,761)

(2,028)

Non-GAAP sales and marketing

$ 156,991

$ 120,112

$ 299,983

$ 241,091

GAAP general and administrative

$ 47,558

$ 42,671

$ 92,848

$ 84,992

Less: Stock-based compensation expense

(11,953)

(12,566)

(24,515)

(23,852)

Less: Employer payroll taxes on employee stock transactions

(1,521)

(1,143)

(3,578)

(2,108)

Non-GAAP general and administrative

$ 34,084

$ 28,962

$ 64,755

$ 59,032

Reconciliation of operating income (loss) and operating margin

GAAP operating income (loss)

$ 12,618

$ (22,006)

$ 24,614

$ (56,976)

Plus: Stock-based compensation expense

134,652

118,337

269,685

231,065

Plus: Amortization of acquired intangibles

1,486

2,270

3,718

4,489

Plus: Employer payroll taxes on employee stock transactions

8,786

7,865

24,007

14,258

Non-GAAP operating income

$ 157,542

$ 106,466

$ 322,024

$ 192,836

GAAP operating margin

2 %

(4) %

2 %

(6) %

Non-GAAP operating margin

24 %

21 %

26 %

19 %

Datadog, Inc.

Reconciliation from GAAP to Non-GAAP Results

(In thousands, except per share data; unaudited)

Three Months Ended

June 30,

Six Months Ended

June 30,

2024

2023

2024

2023

Reconciliation of net income (loss)

GAAP net income (loss)

$ 43,824

$ (3,969)

$ 86,455

$ (28,055)

Plus: Stock-based compensation expense

134,652

118,337

269,685

231,065

Plus: Amortization of acquired intangibles

1,486

2,270

3,718

4,489

Plus: Employer payroll taxes on employee stock transactions

8,786

7,865

24,007

14,258

Plus: Amortization of issuance costs

910

846

1,760

1,691

Non-GAAP net income before non-GAAP tax adjustments

$ 189,658

$ 125,349

$ 385,625

$ 223,448

Income tax effects and adjustments (1)

36,693

23,905

75,038

41,613

Non-GAAP net income after non-GAAP tax adjustments

$ 152,965

$ 101,444

$ 310,587

$ 181,835

Net income per share before non-GAAP tax adjustments – basic

$ 0.57

$ 0.39

$ 1.16

$ 0.70

Net income per share before non-GAAP tax adjustments – diluted

$ 0.53

$ 0.36

$ 1.08

$ 0.64

Net income per share after non-GAAP tax adjustments – basic

$ 0.46

$ 0.31

$ 0.93

$ 0.57

Net income per share after non-GAAP tax adjustments – diluted

$ 0.43

$ 0.29

$ 0.87

$ 0.52

Shares used in non-GAAP net income per share calculations:

Basic

334,941

322,215

333,373

320,788

Diluted

356,740

348,551

356,650

347,311

Non-GAAP financial information for the periods shown are adjusted for an assumed provision for income taxes based on our long-term projected tax rate of 21%. Due to the differences in the tax treatment of items excluded from non-GAAP earnings, our estimated tax rate on non-GAAP income may differ from our GAAP tax rate and from our actual tax liabilities.

Datadog, Inc.

Reconciliation of GAAP Cash Flow from Operating Activities to Free Cash Flow

(In thousands; unaudited)

Three Months Ended

June 30,

Six Months Ended

June 30,

2024

2023

2024

2023

Net cash provided by operating activities

$ 164,424

$ 153,158

$ 376,694

$ 286,945

Less: Purchases of property and equipment

(4,415)

(2,339)

(18,573)

(11,078)

Less: Capitalized software development costs

(16,229)

(9,087)

(27,594)

(17,798)

Free cash flow

$ 143,780

$ 141,732

$ 330,527

$ 258,069

Free cash flow margin

22 %

28 %

26 %

Contact Information
Yuka Broderick
Datadog Investor Relations
IR@datadoghq.com

Dan Haggerty
Datadog Public Relations
Press@datadoghq.com

Datadog is a registered trademark of Datadog, Inc.

All product and company names herein may be trademarks of their registered owners.

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SOURCE Datadog, Inc.

Technology

AI Is Writing Code Faster Than Security Can Review It, Finds New Linux Foundation Research Sponsored by ActiveState

Published

32 minutes ago

November 9, 2026

Study reveals 58% of teams ship code faster than security review can keep up, as AI coding tools add dependencies that teams vet only about half the time

VANCOUVER, BC, Nov. 9, 2026 /PRNewswire/ — ActiveState, a sponsor and contributor to a new Linux Foundation research study supported by the Open Source Security Foundation (OpenSSF) and LF AI & Data Foundation, today announced findings from the State of AI Impact on Software Security and Cybersecurity 2026, revealing a growing gap between the pace of AI-driven software development and organizations’ ability to secure the code being produced. The study found that 58% of surveyed teams ship code faster than security reviews can keep up, while 60% say AI coding assistants have increased the number of external packages in their codebases. Yet teams review the indirect, or “transitive,” dependencies introduced by those packages only about half the time.

These challenges are becoming more urgent as AI accelerates both code generation and vulnerability discovery, leaving organizations less time to identify and remediate security flaws before attackers exploit them. The October 2026 PHP2Shell vulnerabilities underscored the urgency, with emergency patches issued and exploitation attempts reported within hours of disclosure. As vulnerabilities can be identified and exploited at increasing speed, organizations face mounting pressure to ensure security reviews, dependency vetting, and remediation keep pace.

The survey of 172 security practitioners across companies and open source projects also found that AI is part of the solution, albeit with some trade-offs. Respondents credited AI-assisted tools with detecting 30% of the vulnerabilities they found in the past year, and 69% said the tools had meaningfully improved their security posture. On average, AI tools made security work 17% faster, but false-positive vulnerability reports increased practitioners’ workloads by 30%, highlighting the need to balance AI-driven efficiency with effective security oversight.

“AI coding assistants don’t just write code. They choose your dependencies, and this research shows teams are checking only about half of what comes along for the ride,” said Abby Kearns, CEO of ActiveState. “That matches what we hear from customers every week. Slowing developers down won’t fix it. The answer is to decide what’s trusted before it reaches the codebase, so developers and their AI tools start from components security has already approved.”

Other key findings:

Skills are at risk. 82% of respondents agree that overreliance on AI tools weakens developers’ security skills.Concern outpaces readiness. For every AI-related threat measured, more practitioners are concerned than feel prepared. The widest gap, 41 points, is for AI-automated exploit generation and fuzzing.The supply chain tops the worry list. Supply chain attacks are the leading concern among security experts (93%), followed by prompt injection (90%) and the maintenance burden of high component-update volume (87%).Nobody owns it. Only 30% of teams have a dedicated role responsible for AI-related security threats, and only about one in three have a formal security policy that addresses AI.Ownership pays off. 75% of teams with a dedicated AI security role say they are well prepared for adversarial attacks on AI systems, compared with 50% of all respondents.Human review isn’t a given. Among teams with an AI security policy, only 57% require human review of AI-generated code.

Together, the findings point to a need to make security responsibilities explicit and strengthen governance as AI adoption grows. Dedicated roles, clear policies, and properly implemented tools correlate with greater preparedness; the survey does not establish that those practices cause the difference.

“As AI accelerates software development, organizations need to be just as deliberate about how they govern and secure the code being produced,” said Laura Guazzelli, Security Architect for AI/ML/LLM/Agentic Systems at OpenSSF. “That starts with understanding where AI is introducing risk into the software supply chain, establishing clear ownership for addressing it, and ensuring security practices can keep pace with how software is built.”

The report recommends that organizations assign a clear owner for AI-related security threats, formalize AI security policies that include human review of generated code, and strengthen controls on direct and transitive dependencies. Building on those findings, ActiveState recommends governing open source at the point of intake, alongside scanning and review, so security teams decide which components enter the software supply chain before developers or AI assistants pull them in.

Read State of AI Impact on Software Security and Cybersecurity 2026 for the findings and practical recommendations: https://www.linuxfoundation.org/research/ai-impact-on-cybersecurity-2026

About ActiveState

ActiveState secures the software supply chain at the source for the AI coding era. With the ActiveState Curated Catalog, a private, vetted repository of open source components, security teams decide what gets into enterprise software before developers or AI coding assistants pull it in. Security gets control, and engineering keeps its speed. The Curated Catalog works with the artifact repositories, scanners, and AI coding assistants that teams already use. Learn more at activestate.com.

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SOURCE ActiveState

Technology

OpenSSF Welcomes New Members and Expands AI Security Resources

Published

32 minutes ago

November 9, 2026

Broadened AI security guidance, relationships, and research to help close the gap between AI-driven development and software security

SALT LAKE CITY, Nov. 9, 2026 /PRNewswire/ — The Open Source Security Foundation (OpenSSF), a cross-industry initiative of the Linux Foundation focused on sustainably securing open source software, today announced new membership growth, welcoming Beautiful Majestic Dolphin, DevGuard, and UElement Technologies to the foundation. The OpenSSF also notes new and expanded efforts to help organizations secure software in the age of AI, including membership with the Multi-Organization Secure AI Coordination (MOSAIC) for better AI security, a new guide to finding and fixing vulnerabilities using AI, and new research on AI’s impact on software security.

“Security must keep pace with this new software development lifecycle.”

AI is redefining the Software Development Life Cycle (SDLC). It impacts both the rate at which code is produced and how quickly vulnerabilities are discovered and reported – for attackers and defenders alike. With bugs easier to find and reporting windows shrinking under regulations like the EU Cyber Resilience Act (CRA), organizations need clear, practical guidance on using AI to secure software. OpenSSF continues to serve as a trusted, neutral hub for AI security education, collaboration, and open source software security.

“Security must keep pace with this new software development lifecycle,” said Laura Guazzelli, Security Architect for AI/ML/LLM/Agentic Systems at OpenSSF. “This new SDLC has collapsed the window between bug discovery and automated attack, exposing defenses that are unprepared for AI-driven threats and offensive campaigns. This is why we are focused on equipping developers with practical resources, from guidance on finding and fixing vulnerabilities with AI to new data on how AI is shaping software development.”

New OpenSSF members include Beautiful Majestic Dolphin, DevGuard, and UElement Technologies who join the Foundation as General Members. These organizations join a community of working groups, technical groups, and experts that are shaping the future of OpenSSF and software security. Their membership reinforces the long term sustainability of open source and the projects that are critical to modern infrastructure.

Q4 Foundation Achievements
In the fourth quarter of 2026, OpenSSF achieved the following milestones:

OpenSSF joins MOSAIC – Membership with MOSAIC allows OpenSSF and MOSAIC members to pool knowledge and resources for consistent standards and guidance on AI security. A major goal of the group is a standardized map of the participating initiatives, which includes aligning with other initiatives such as NIST SP 800-53 Control SC-42, building on existing frameworks, a general consensus on MOSAIC definitions, and more.State of AI Impact on Software Security and Cybersecurity 2026 report – The study, supported by OpenSSF, ActiveState and LF AI & Data Foundation, found a growing gap between the pace of AI-driven software development and organizations’ ability to secure the code being produced: 58% of surveyed teams ship code faster than security reviews can keep up, while 60% say AI coding assistants have increased the number of external packages in their codebases. This data underscores the importance of trusted, reliable guidance as AI deployment continues to grow.Secure AI Associate (SAIA) certification – OpenSSF and Linux Foundation Education are launching the Secure AI Associate (SAIA) certification. This new credential will provide learners with skills to find and fix security risks across the full AI/ML lifecycle, from training data to production pipelines. The certification launches in December 2026 and interested parties can sign up for exam availability notifications here.New guide to finding and fixing vulnerabilities using AI – The OpenSSF Best Practices and AI/ML working groups released Finding and Fixing Software Vulnerabilities Using AI: A Guide for Developers, a free guide for developers and security researchers on using AI to find and fix vulnerabilities before attackers can exploit them. Built on principles that outlast any single tool, it covers the full process, from preparing and sandboxing AI to validating, fixing, and preventing issues, and stresses that AI-generated patches should never be trusted without automated tests and human review. An online course (LFD126) is planned for the coming months.Case study: GitHub expands malware advisories across eight ecosystems – A new case study shows how GitHub integrated OpenSSF’s Malicious Packages data into its Advisory Database, expanding Dependabot malware alerts beyond npm to eight package ecosystems, including PyPI, Maven, RubyGems, NuGet, Go, crates.io, and PHP Composer. The community-driven repository now holds more than 238,000 malicious package reports, showing how shared, open data can help defenders keep pace with fast-moving supply chain attacks.Zarf 1.0 marks a major milestone for the OpenSSF – the release helps teams package and deploy software in environments disconnected from the internet. Version 1.0 strengthens package signing and verification workflows, improves configuration management, and establishes a foundation for long-term compatibility and community-driven development.

Supporting Quotes

“Scanning tells you what went wrong. Knowing where your software came from tells you whether it should have been trusted at all. ActiveState builds open source from verified source so that secure is the default, and joining the OpenSSF is a natural next step in making that true for everyone who builds on open source.”
– Abby Kearns, CEO, ActiveState

“We think that you should be able to determine why things happen. Who, what, when, where, and how? Those questions can all easily be understood, but why is one of the hardest parts of cybersecurity. That’s why we made GoRapide open source, under an MIT license. We know why we build for our customers, but ultimately we can’t know every reason why someone might ask, ‘Why?’ For us, joining the Open Source Security Foundation made sense because open source is integral to our company mission.”
– Shane Morris, CEO, Beautiful Majestic Dolphin

“We built open-source DevGuard to make secure software development the default, not the exception. We already use OpenSSF projects and data to help our users secure their entire software development lifecycle, so joining the OpenSSF feels like a natural step. We’re proud to become part of the community shaping the future of open-source security and look forward to giving back.”
– Tim Bastin, CTO, DevGuard – L3montree GmbH

“Open source is the common ground the world’s digital economy is built on and India is now one of its largest builders. Keeping that ground secure isn’t one company’s or one country’s job; it belongs to all of us. UElement joins OpenSSF to help make it trustworthy today and quantum-safe tomorrow, so the systems people depend on, from banks to public infrastructure, can be built in the open and trusted everywhere.”
– Chaitanya Vijay Ghate, Co-founder & CEO, UElement Technologies

Events and Gatherings
OpenSSF members are gathering this week in Salt Lake City at Open Source SecurityCon North America and onsite at KubeCon + CloudNativeCon North America (November 9-12). Those interested in joining OpenSSF at events in 2027 should mark their calendars for Open Source SecurityCon Europe, which is planned for March 15, 2027.

Additional Resources

View the complete list of OpenSSF membersContribute efforts to one or more of the active OpenSSF working groups and projectsSign up for the OpenSSF newsletter to receive updates on upcoming events, resources, and community news.

About the OpenSSF
The Open Source Security Foundation (OpenSSF) is a cross-industry organization at the Linux Foundation that brings together the industry’s most important open source security initiatives and the individuals and companies that support them. The OpenSSF is committed to collaboration and working both upstream and with existing communities to advance open source security for all. For more information, please visit us at openssf.org.

About the Linux Foundation
The Linux Foundation is the world’s leading home for collaboration on open source software, hardware, standards, and data. Linux Foundation projects, including Linux, Kubernetes, Model Context Protocol (MCP), OpenChain, OpenSearch, OpenSSF, OpenStack, PyTorch, Ray, RISC-V, SPDX and Zephyr, provide the foundation for global infrastructure. The Linux Foundation is focused on leveraging best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org.

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see its trademark usage page: www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

Media Contact
Grace Lucier
The Linux Foundation
pr@linuxfoundation.org

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Technology

Fatbeam Fiber Welcomes Kerry Conn as Chief Operating Officer

Published

32 minutes ago

November 9, 2026

Seasoned broadband executive Kerry Conn joins Fatbeam to strengthen operations, scale business performance, and support the company’s next phase of growth.

COEUR D’ALENE, Idaho, Nov. 9, 2026 /PRNewswire/ — Fatbeam Fiber, a leading provider of fiber optic broadband in the western United States, is thrilled to announce the addition of Kerry Conn as Chief Operating Officer (COO). Bringing 25 years of leadership experience across broadband, operations, technology, and finance, Kerry will help guide Fatbeam’s operational strategy and support the company’s continued growth and commitment to delivering reliable connectivity to the communities it serves.

Kerry brings a strong track record of scaling broadband organizations, optimizing operational execution, and bridging the gap between technology and financial strategy to drive sustainable growth. Most recently, she served at Rise Internet, an internet service provider serving residential customers across 16 states, where she led Operations, IT, and Strategic Initiatives.

Prior to Rise Internet, Kerry served as both Chief Financial and Operating Officer at AtLink Services, an Oklahoma-based fixed wireless provider. During her 15-year tenure, she helped grow the company from approximately 400 subscribers to 20,000, contributing to its significant expansion and operational development.

“At Fatbeam, everything comes back to the customer experience, and Kerry shares that commitment. Kerry has a proven track record of building high-performing teams, and I’m confident she will help us keep raising the bar for service and reliability across every market we serve.” – Paul Merritt, CEO, Fatbeam Fiber

Passionate about solving complex challenges, building high-performing teams, and creating the processes and systems that enable people to excel, Kerry brings deep expertise in field operations, network reliability, technology infrastructure, and financial planning. Her broad experience provides a well-rounded perspective on the operational and strategic priorities critical to scaling and sustaining a successful broadband business.

“I’m excited to join Fatbeam Fiber and get to know the team. I look forward to working together to strengthen our operations, empower our people, and support the company’s continued growth and success.” — Kerry Conn, COO, Fatbeam Fiber

For more information, please visit www.fatbeam.com.

About Fatbeam Fiber

Fatbeam provides reliable fiber-based network solutions to enterprise, healthcare, government, education, and residential customers in the Western United States. They believe in giving residential customers peace of mind and enabling businesses to concentrate on operations by providing a superior product at the right price to meet needs now and in the future.

Media Contact:

Keeley Fenley
Marketing Manager
marketing@fatbeam.com
(509) 344-1008

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SOURCE Fatbeam