DDOG · Q1 2026 Earnings
BullishDatadog
Reported May 7, 2026
30-second summary
Datadog printed $1.006B in Q1 (+32% YoY, +4.8% above the $960M consensus and $45M above the high end of its own guide), with non-GAAP EPS of $0.60 beating consensus $0.51 by 17.6% — and management raised the FY26 revenue guide from $4.06–4.10B (18.7–19.7% growth) to $4.30–4.34B (25–27% growth), a $240M midpoint lift in one quarter. The cautious FY26 setup laid down on the Q4 call is gone: Q2 is guided to $1.07–1.08B (29–31% YoY) on broad-based ARR strength, non-AI customer growth accelerated to ~20% YoY, and AI-native customers spending $1M+ reached 22 with five at $10M+. The Q4 deceleration scare was a head fake.
Headline numbers
EPS
Q1 FY2026
$0.60
+17.6% vs est.
Revenue
Q1 FY2026
$1.01B
+32.0% YoY
+4.8% vs est.
Gross margin
Q1 FY2026
80.0%
Free cash flow
Q1 FY2026
$0.29B
Operating margin
Q1 FY2026
22.0%
Key financials
Q1 FY2026| Metric | Q1 FY2026 | YoY | Q4 FY2025 | QoQ |
|---|---|---|---|---|
| Revenue | $1.01B | +32.0% | $0.95B | +5.6% |
| EPS | $0.60 | — | $0.59 | +1.7% |
| Gross margin | 80.0% | — | 80.4% | -40bps |
| Operating margin | 22.0% | — | 1.0% | +2100bps |
| Free cash flow | $0.29B | — | $0.29B | -0.7% |
Guidance
Guidance is issued for both next quarter and the full year. Both may appear below.
Actuals vs prior guidance
| Metric | Period | Prior guide | Actual | Δ | Result |
|---|---|---|---|---|---|
| Revenue | Q1 FY2026 | $951 million to $961 million | $1,006.426 million | +$45.4-55.4 million above guide | Beat |
| Non-GAAP EPS | Q1 FY2026 | $0.49 to $0.51 | $0.60 | +$0.09 above guide | Beat |
| Non-GAAP Operating Income | Q1 FY2026 | $195 million to $205 million | $224 million | +$19-29 million above guide | Beat |
New guidance
| Metric | Period | Guide | YoY |
|---|---|---|---|
| Operating Margin | FY2026 | 22% to 23% | — |
| Net Interest and Other Income | FY2026 | approximately $170 million | — |
| Cash Taxes | FY2026 | $30 million to $40 million | — |
| Capital Expenditures and Capitalized Software | FY2026 | 4% to 5% of revenue | — |
| Revenue | Q2 FY2026 | $1.07 billion to $1.08 billion | +29% to +31% |
| Non-GAAP EPS | Q2 FY2026 | $0.57 to $0.59 | — |
| Non-GAAP Operating Income | Q2 FY2026 | $225 million to $235 million | — |
Changes to prior guidance
| Metric | Period | Prior guide | New guide | Δ | Result |
|---|---|---|---|---|---|
| Revenue | FY2026 | $4.06 billion to $4.10 billion | $4.30 billion to $4.34 billion | +$200-280 million (midpoint +$240M, +5.9%) | Raised |
| Non-GAAP EPS | FY2026 | $2.08 to $2.16 | $2.36 to $2.44 | +$0.28-0.36 (midpoint +$0.32, +15.1%) | Raised |
| Non-GAAP Operating Income | FY2026 | $840 million to $880 million | $940 million to $980 million | +$100-140 million (midpoint +$120M, +14.3%) | Raised |
Platform metrics
Q1 FY2026| Segment | Q1 FY2026 |
|---|---|
| $100k+ ARR customers | 4,550 |
Profitability
Q1 FY2026| Segment | Q1 FY2026 |
|---|---|
| Operating cash flow | $335 million |
| Free cash flow margin | 29% |
Management tone
Q4-24 customer optimization hangover → Q2-25 AI-native cohort named as accelerant → Q3-25 acceleration broadens to base business → Q4-25 platform consolidation and cautious FY26 setup → Q1-26 AI elevated to co-equal secular driver with cloud migration, FY26 ripped open.
The single most important shift: AI moved from product category to macro-equivalent growth pillar. Two quarters ago AI was the cohort cut; last quarter management deliberately retired the cohort disclosure as "less relevant"; this quarter management is explicit: "There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. But we now have an additional secular growth driver with AI." That elevates AI to the same conceptual tier as the cloud-migration thesis Datadog has used to anchor its TAM story since IPO — a structural re-rating, not a cyclical tailwind framing. Pairing it with hyperscaler training-workload wins (a market management explicitly said "wasn't a market" last year) signals real expansion of the addressable opportunity.
Non-AI growth flipped from "stable" defense to "accelerating" offense as the primary validation metric. A quarter ago management led with $1M+ ARR customer counts (603, +31%) as the new replacement disclosure, with non-AI growth left implicit. This quarter management led with the non-AI cohort at ~20% YoY: "Even more impressive was the growth in our non-AI customers. Non-AI customer revenue growth accelerated again this quarter to meet 20% year-over-year." The reframing matters because it directly defuses the AI-concentration anxiety that has shadowed the stock for four quarters — the bull case now rests on two independent growth engines, not one.
The cautious FY26 setup laid down last quarter has been abandoned in a single print. Last quarter's brief flagged that the FY26 revenue growth guide of 18.7–19.7% paired with 1.5–5.4% EPS growth implied either deep conservatism or structural moderation. This quarter answers the question: pure conservatism. The FY26 revenue growth band lifted to 25–27% — essentially restoring the FY25 growth profile — and FY26 EPS midpoint moved from $2.12 to $2.40, a 13% growth print vs FY25's $2.05 actual. Management's tone in the press release qualitative statements is correspondingly more expansive: "each has a potential to grow to more than $100 million over time" (referring to multiple new product categories).
New logo language reached its most confident pitch in the coverage window. From Q2's "becoming productive" to Q3's "more than doubled YoY and set a new record" to this quarter's "new logo annualized bookings a new all-time record by a significant margin and more than double versus a year ago quarter… new logo average land size also set a record and more than doubled year over year." Two consecutive quarters of doubled new logo bookings, now with land size also doubling, validates the H2 2024 sales hiring class as a structural step-change rather than a one-quarter pull-forward.
One hedge worth flagging: management noted it is "applying a higher degree of conservatism to our largest customer" — a quiet acknowledgment that single-customer concentration risk on the largest AI-native account is being modeled into guidance. This is the closest management has come to re-publishing the ex-largest-customer disclosure they retired last quarter, and the modeling caveat is itself evidence that the concentration is non-trivial.
Recurring themes management leaned on this quarter:
Risks management surfaced:
Q&A highlights
Mark Murphy · JP Morgan
How should we conceptualize the exponential growth in code generation from AI tools like Codex and Cursor, and how much of that code reaches production to drive Datadog activity? Additionally, how does the heterogeneity of silicon architectures (custom chips from Amazon, Google, Microsoft) create tailwinds for Datadog's monitoring capabilities?
Management confirmed seeing inflection points in consumption and code reaching production across both AI-native and non-AI companies. On silicon heterogeneity, they noted it plays in Datadog's favor as complexity increases, though currently only a small number of companies have heterogeneous environments. Notably, they pivoted from saying training wasn't a market last year to now seeing training as an emerging opportunity with hyperscaler customers using Datadog in their AI labs.
David Dumbrava · Morgan Stanley
Given the strong guidance, what is the underlying macro backdrop assumption, particularly regarding geopolitical tensions and potential consumer discretionary impacts in e-commerce and retail? Also, how will Datadog's pricing model evolve as autonomous agents consume the data platform at higher rates than human engineers today?
Management indicated no observed impact yet from macro headwinds in consumer/e-commerce despite monitoring. For agent-driven usage, they noted their usage-based model naturally adapts regardless of whether consumption comes from humans or agents. Currently seeing simultaneous growth in both agent adoption (via MCP servers) and human web interface usage, with no need to change pricing structure.
Ramo Chow · Barclays
What is the real-world dynamic between open source tooling consolidation and Datadog adoption? Are customers truly consolidating from fragmented open source tools to Datadog, or is observability just remaining fragmented with different categories served by different vendors?
Management confirmed that while companies have open source tools scattered across the organization, the consolidation motion is real and universal. Customers with 4-25 different point solutions consolidate to Datadog for unified platform benefits: single pane of glass, end-to-end workflows, no blind spots, and cost savings. Importantly, even hyperscalers with resources to build themselves are coming to Datadog for some workloads, validating the platform consolidation trend.
Gabriella Borges · Goldman Sachs
Why is the training opportunity inflecting now versus previous years? How should we think about attach rates and observability spend as a percentage of training spend versus inference spend? Is the benchmark different?
Management attributed the training inflection to professionalization of the workload—it's shifting from artisanal, one-off researcher projects to production, must-be-reliable workloads that are scaling by orders of magnitude. Every failure costs a week of competitive advantage. On attach rates, management indicated 6,500 customers use integrations (20% of customers, 80% of ARR), but training is still early days. The larger attachment story currently remains inference, though training is beginning to contribute.
Carl Kersted · UBS
What is driving the confidence in Q2 guidance, which implies the largest sequential dollar ARR addition in company history? Can you provide color on the ramp of the larger research labs that renewed in Q4 and landed in Q1?
Management emphasized that Q1 ARR addition was record-breaking and broadly based rather than concentrated. Even excluding the largest customer that renewed in Q4, Q1 would still have been a record ARR quarter. They landed additional large customers in Q1 with no Q1 revenue contribution but expected to be big contributors in future quarters. The confidence in Q2 comes from taking the already-signed ARR and applying conservative growth discount assumptions.
Answers to last quarter's watch list
What to watch into next quarter
Whether Q2 revenue clears the $1.08B high end and prints above $1.10B. Q1 beat the high end by $45M (4.7%); a comparable beat on Q2's $1.07–1.08B guide would imply ~$1.12B. Anything inside the range would mark the first non-beat in over a year and re-open the question of whether 32% YoY growth was a one-quarter peak.
Whether the FY26 revenue guide gets raised again on the Q2 call, and by how much. A $240M midpoint raise in Q1 set the bar; a comparable Q2 raise into the high 20s% growth range would frame FY26 as a full restoration of the FY25 trajectory. A smaller-than-Q1 raise (e.g. $100M) would suggest the Q1 raise pulled forward most of the conservatism.
Whether non-AI customer growth holds at 20%+ YoY in Q2. This is the disclosure that defuses concentration risk. Management called it out explicitly this quarter; if they stop providing it, or if it decelerates back toward the mid-teens, the AI-concentration narrative re-asserts itself.
Whether the $1M+ ARR customer count returns to disclosure and sustains +25%+ growth. The press release skipped the $1M+ count this quarter. A return of the metric on the Q2 call at 700+ (vs 603 in Q4) at +25%+ YoY would validate the large-deal motion; continued silence would suggest management is curating disclosure around what is accelerating.
First quantified training-workload revenue contribution. Management explicitly named hyperscaler training workloads as a new addressable market this quarter, with deals landed in Q1 that did not contribute Q1 revenue. A Q2 datapoint quantifying training contribution — even directionally (e.g. "single-digit millions of ARR from training") — would mark the next concrete step beyond the architectural framing.
Whether non-GAAP gross margin holds at 80% or compresses further. Two consecutive quarters at 80–80.4%, down from the 81.0% Q3 2025 peak. The cloud-efficiency upside narrative from H1 2025 has visibly tapped out; a Q2 print below 79.5% would suggest infrastructure intensity from AI workloads is starting to show.
Sources
- Datadog Q1 2026 press release (8-K exhibit 99.1, filed with SEC): https://www.sec.gov/Archives/edgar/data/1561550/000162828026031677/ex-991x20260331x8k.htm
- Datadog Q1 2026 earnings call commentary and Q&A (management prepared remarks and analyst exchanges).
Get the next brief, free.
We publish analyst-grade earnings briefs the same day or morning after every call — headline numbers, segment KPIs, Q&A highlights, and tone analysis. Free during beta.
This is not investment advice.