Datadog (DDOG) Q2 FY26 earnings call: guidance and Q&A

Can you provide more details on the new contract with your largest customer, specifically regarding its duration, pricing, and whether the lower usage is due to a lower unit price or actual downsell?

Management stated they fully de-risked the guidance for the rest of the year due to the usage reduction from this customer, but cannot share specific customer details. They emphasized that the rest of the business is accelerating steadily, with 5 quarters of continuous acceleration, and they feel very good about the market.

“if you back out our largest customer from our growth, you get pretty much the same growth rate as the rest of the business has been accelerating very steadily. Actually, we've seen, I think, now 5 quarters of continuous acceleration from the rest of the business.”

asked by Sanjit Singh · answered by Olivier Pomel

What does the enterprise AI application development cycle look like for Datadog over the last couple of quarters, given the increasing AI initiatives among enterprises?

Olivier noted broad AI adoption manifesting in two ways: increased cloud adoption and workloads (driving acceleration in non-AI customers) and a rapid increase in the usage of Datadog's AI-first products, such as those measuring agents and LLMs, where traffic has exploded.

“The same thing we see is a very rapid increase in the usage of all of our AI first surfaces. So that would be the products that measure agents and LLMs, where we see an explosion of traffic in terms of the LLM and [ tool calls ] that we're getting.”

asked by Sanjit Singh · answered by Olivier Pomel

How much more observability is needed for AI inference workloads, considering components like vector databases, guardrails, and container monitoring?

Olivier explained that there are opportunities at every layer of the stack for inference, from infrastructure (GPUs) to agents and LLMs. He noted growing adoption of GPU monitoring and agent monitoring products, and David added that several large enterprise deals included AI products.

“There's opportunity at every layer of the stack in inference. So we do think at the end of the day, inference will be the dominant workload.”

asked by Raimo Lenschow · answered by Olivier Pomel

How are CFO-level conversations evolving, particularly regarding budget allocation for Datadog, and is 'infinite cardinality' helping address cost concerns?

Olivier stated that Datadog helps customers either make more money or save money, especially with AI adoption by optimizing costs for building, running operations, or AI agents. He highlighted that infinite cardinality addresses customer frustration with unpredictable bills from fine-grain tags, which is particularly relevant as customers build more AI applications.

“In terms of Infinite cardinality, that's — I would say it's been one of the longer-standing source of frustration for customers when sometimes they send more data or they send more fine-grain tags with their data and to get some unpredictability on the [ bills ] because of that because it increases the [indiscernible] of the data we're getting.”

asked by Gabriela Borges · answered by Olivier Pomel

Given recent headlines on AI security, how does Datadog's positioning in observability and security allow it to address the increasing opportunity around AI agent security and a 'guardian model'?

Olivier explained that AI security products require a complete rebuild, moving away from human-in-the-loop processes to integrated, application-proximate, AI-agent-driven auto-remediation. He believes this aligns with Datadog's integrated platform approach, where data streams flow directly from observability to security agents.

“I mean look, there's a complete switch in the way the AI security products need to work. So you can't wait basically for putting humans in the loop.”

asked by Aleksandr Zukin · answered by Olivier Pomel

How does Datadog ensure that the increased automation driven by Bits AI doesn't eventually reduce the volume of activity that traditionally drove Datadog consumption?

Olivier asserted that providing more value through automation (fixing issues, preventing problems) will lead to more software usage, not less. He noted that customers using Bits AI tend to deploy more products, create more bots and alerts, and have more users, indicating it's not a zero-sum game.

“I think the future of observability is not just observing, it's fixing. It's not waking your people in the middle of the night because something -- bot is fixing it for them.”

asked by Koji Ikeda · answered by Olivier Pomel

Is the acceleration in the non-AI part of the business sustainable, and is it driven by new customer logos or increased usage? Also, how does CFO caution impact non-AI budgets?

Olivier stated the non-AI acceleration is broad-based and largely driven by existing customers increasing volume and adopting newer products. He believes it's sustainable, as current growth rates are well within historical ranges. David added that investments in go-to-market are also contributing to this growth.

“I mean, from what we can tell, it's very broad-based. And it's largely driven by existing customers because that's the majority.”

asked by Samik Chatterjee · answered by Olivier Pomel

New customer additions appear to be weaker this quarter, especially after DASH. Can you provide color on this trend?

David clarified that gross customer additions remain strong and on trend. The variability in total customer count is primarily at the very low end (border between free and contract users), which has a minimal effect on revenue. Olivier added that customer counts above $100K or $1M ARR thresholds are trending very well.

“Our gross customer additions continue to be strong and on trend line. And that's the vast majority of our revenues. We have, at the very low end, the border between free and contract and that has variability very low effect on revenue.”

asked by Ittai Kidron · answered by David Obstler