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Datadog doubles down on APAC, targets faster growth

The observability tools supplier is executing a multi-year growth plan for Asia-Pacific and Japan, focusing on data residency, localisation and AI-driven observability to grow its market share

Aaron Tan
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Published: 15 Jul 2025 6:12

Observability and monitoring platform Datadog has been expanding its footprint in Asia-Pacific and Japan (APJ), launching datacentres and expanding local teams with a goal of growing faster than the company’s global average.

Rob Thorne, vice-president of Datadog for the region, said in an interview with Computer Weekly that a key part of his role since taking the job more than two years ago has been to help the company’s global executives understand the opportunities in the region and identify growth markets.

This has resulted in a multi-year growth plan that has seen Datadog open offices in Tokyo, Singapore and Sydney, with two more planned for the near future. The company is focusing its efforts on five major markets: Japan; Australia and New Zealand (ANZ); Southeast Asia; South Korea; and India.

A cornerstone of the company’s expansion efforts is addressing the data residency requirements of customers in the region. “Solving the data residency question was top of mind for customers in the region so that they can utilise the full functionality of the platform,” said Thorne.

To solve this, Datadog launched a datacentre in the Amazon Web Services region in Tokyo in the first half of 2023, and is preparing to launch another one in ANZ very soon. “We’d like to deploy more when the time is ripe,” he said.

Datadog’s expansion comes as the company reports strong global growth, with a 25% year-over-year revenue increase to $762m in the first quarter of 2025. Thorne said his personal ambition and aspiration is to ensure that the APJ business is growing faster than the company’s average, fuelled by the booming economies of India and Indonesia.

But rather than treating APJ as a single region, Datadog is pursuing a localised go-to-market strategy, customising its approach for each market, such as providing support and training materials in local languages like Japanese.

Read more about observability in APAC

The company has also deployed cross-functional teams on the ground. “My job is to make sure customers receive the same level of service from Datadog that a large bank in New York would receive,” said Thorne.

This includes pre-sales, customer success, and technical experts such as the recently hired field chief technology officer for the region, Yadi Narayana, to help customers harness the full capabilities of the Datadog platform.

Thorne cited an example of a large airline in India that was struggling with homegrown tools. By implementing the three pillars of observability – logs, traces and metrics – the airline was able to reduce its median time to detection for issues by over 50%. A major food delivery service in India is also using the Datadog platform to handle massive spikes in traffic during major events.

Tool consolidation and AI

Datadog, which spends about 30% of its revenue on research and development every year, is capitalising on two major trends – tool consolidation and the explosion of artificial intelligence (AI) – as it expands its reach in the region.

“What customers are looking for is a single pane of glass across all of their tech, so that when issues arise, they’re able to resolve them very quickly,” said Thorne.

While digital-native companies are consolidating to a small number of cloud-based observability tools, those with complex hybrid environments are opting for a “coexist strategy” that involves the use of multiple cloud and on-premise platforms.

The second major trend is AI, where Datadog is playing on two fronts. The first is AI for observability, embedding AI into its own products to automate workflows, detect anomalies and proactively predict issues.

The second, and an increasingly popular use case, is observability for AI. As companies invest heavily in developing their own AI models and agents, they need to monitor them. “They want to make sure that the performance, reliability and fairness of the models is in line with their expectations,” said Thorne.

When asked if there have been more inquiries from customers of rivals such as Splunk, which was acquired by Cisco, he would only say that a popular use case for Datadog is helping customers optimise the cost of logs stored on other cloud-based security information and event management platforms.

The biggest hurdle to the company’s growth plans is talent. “Raising the bar with every hire is difficult when you’re doing it at scale,” said Thorne. “That’s probably my biggest challenge.”

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