Datadog, Inc.
DDOG · Nasdaq
DDOG · Nasdaq
Expected to report Nov 5, 2026 — estimated from last year’s reporting date.
Consensus is $0.15 EPS for Sep 2026 across 9 estimates, ranging $0.13 to $0.18.
Datadog's second quarter was a strong growth and cash-generation print, with revenue of $1.12 billion up 36% from $826.8 million a year ago and roughly 11% sequentially from $1.01 billion in Q1. The supplied earnings comparison shows reported EPS of $0.18 versus $0.13 expected, a 38.46% surprise. The release separately reports $0.12 of GAAP diluted EPS and $0.65 of non-GAAP diluted EPS. GAAP operating income improved to $5.5 million from a $35.5 million loss a year ago, although it declined from $7.3 million in the prior quarter. Non-GAAP operating income reached $257.0 million, or a 23% margin, up from $164.1 million and 20% a year ago.
The core business remained broadening and increasingly enterprise-led: 4,720 customers had at least $100,000 of ARR, up 23% year over year, and existing customers generated approximately 70% of revenue growth. Datadog also used the quarter to accelerate its AI platform strategy through new Bits products, more than 100 DASH capabilities and the Adaptive ML acquisition. Cash generation was substantial, with $315.9 million of operating cash flow and $278.7 million of free cash flow. The main offset is forward-looking: reduced usage from the largest customer beginning in Q3 could pressure growth, even as full-year revenue guidance stands at $4.45 billion-$4.47 billion.
Datadog's growth remained driven primarily by expansion within its installed base. Revenue increased $294.7 million year over year, with approximately 70% of the increase attributed to existing customers and 30% to new customers. The company had approximately 33,400 customers at quarter-end, versus 31,400 a year earlier. Its trailing 12-month dollar-based net retention rate was in the low-120% range, compared with about 120% a year ago.
AI was the main product and strategic theme of the quarter. Datadog positioned customer AI deployment as a demand driver for observability, security and automated remediation, while expanding its own platform with agentic capabilities. The company also acquired Adaptive ML, a frontier AI startup focused on reinforcement learning operations, to support Datadog AI Research's work on world models and post-training of agentic large language models.
The quarter showed a sharp improvement in reported profitability versus the year-ago loss, although GAAP operating profitability remained close to breakeven. GAAP gross margin declined to 79% from 80% as third-party cloud infrastructure hosting and software costs increased $64.1 million year over year. That pressure was more than offset below gross profit by operating leverage, with total operating expenses falling to 78% of revenue from 84%.
Datadog guided for Q3 revenue of $1.135 billion-$1.145 billion, non-GAAP operating income of $260 million-$270 million and non-GAAP diluted EPS of $0.63-$0.65. Full-year revenue guidance is $4.45 billion-$4.47 billion, alongside non-GAAP operating income of $1.01 billion-$1.03 billion and non-GAAP EPS of $2.50-$2.54. The outlook incorporates continued investment in product development, infrastructure and go-to-market capacity, but the company flagged a likely deceleration from lower usage by its largest customer.
Datadog continued to use acquisitions to extend its platform and AI capabilities. During the first six months of 2026, it completed three business combinations with aggregate purchase consideration of $191.5 million, comprising $98.5 million of cash, $14.3 million of deferred holdbacks and 796,509 restricted shares. The acquisitions added $178.7 million of goodwill and $11.5 million of intangible assets. Adaptive ML was specifically identified as the quarter's strategic AI acquisition.
