Datadog Acquires Adaptive ML to Enhance Its Enterprise AI

Datadog Acquires Adaptive ML to Boost Its Enterprise AI

Julien Launay, CEO of ADAPTIVE ML

TL;DR

Datadog accelerates its AI strategy with the acquisition of Adaptive ML, a startup specializing in Reinforcement Learning Operations (RLOps), to strengthen its internal artificial intelligence research lab.

The acquisition primarily focuses on talent and expertise in post-training of models, which has become the new battleground for AI following the race for large language models. The value is shifting from the model to its continuous improvement: companies are now looking to specialize, control, and optimize their AI agents through reinforcement learning rather than training new LLMs.

Datadog has a significant competitive advantage thanks to the billions of operational data collected on its platform, a strategic asset for training specialized models in observability and cybersecurity. This move illustrates a broader trend: infrastructure platforms are seeking to transform their proprietary data into differentiating artificial intelligence.

Adaptive ML raised $20 million in 2024 from Index Ventures, ICONIQ Capital, Motier Ventures, IRIS, and Olivier Pomel. The purchase price is not public, but a valuation between $100 and $200 million seems plausible.

The case also highlights Europe's weaknesses: despite high-level research and available funding, large European companies remain too slow to adopt technologies developed by their own startups.

Transforming into an Artificial Intelligence Lab

Datadog continues its transformation into an artificial intelligence lab with the acquisition of Adaptive ML, a specialist in Reinforcement Learning Operations. The American publisher, founded by Olivier Pomel and Alexis Lê-Quôc, aims to internalize expertise that could become crucial in the next generation of enterprise software. Behind this acquisition lies a deeper evolution: infrastructure platforms now want to transform their operational data into proprietary intelligence.

The next battle is now focused on a much less visible layer, that of post-training. This refers to the ability to adapt, improve, and continuously evolve models once deployed at client sites. It is precisely on this ground that Datadog has positioned itself by announcing the acquisition of Adaptive ML, a Franco-Canadian startup specializing in Reinforcement Learning Operations (RLOps).

Investing in Research Rather Than a Product

Adaptive ML will integrate into the Datadog AI Research, an internal lab tasked with developing specialized models for observability and cybersecurity. Datadog is primarily acquiring a team of researchers and rare expertise in one of the most complex areas of current artificial intelligence: the post-training of models using reinforcement learning.

In fact, Adaptive ML had never sought to develop a new large language model. Its ambition was to build tools that enable large organizations to create, improve, and deploy their own specialized agents from their operational data.

Value Shifts Towards Continuous Improvement

Since the arrival of ChatGPT, the industry has mainly focused its investments on the pre-training of models. The performance of LLMs was largely determined by the size of the datasets, the computing power mobilized, and the number of parameters. This logic is now reaching its limits.

Generalist models are gradually converging in terms of performance, and companies are discovering that their true differentiation no longer solely depends on the chosen model, but on their ability to specialize, control, and continuously improve it. This is precisely what Reinforcement Learning enables. Unlike traditional fine-tuning, which involves adapting a model from a static corpus, Reinforcement Learning allows the system to learn progressively from its interactions with its environment, correct its mistakes, and optimize its decisions over time.

Adaptive ML has specialized in this industrial layer, called RLOps.

Datadog's Data as a Competitive Advantage

This acquisition makes perfect sense when observing the assets that Datadog possesses. Every day, its platform collects vast amounts of data from its clients' infrastructures: event logs, system metrics, distributed traces, alerts, incidents, human interventions, and applied fixes. This information constitutes exactly the type of data that specialized models need to progress.

Julien Launay emphasizes that "the hardest part has never been the algorithm, but the production scale." Datadog precisely brings this scale, as the company has a continuous flow of operational data from thousands of organizations worldwide. Few players possess such an informational heritage.

A Natural Acquisition

During its $20 million funding round in 2024, Adaptive ML brought together Index Ventures, ICONIQ Capital, Motier Ventures, and IRIS, alongside Olivier Pomel, who participated as a business angel.

The financial terms of the acquisition have not been disclosed. Given the multiples observed for this type of acquisition in 2025 / 2026, a valuation between $100 and $200 million is likely.

A French Success… Highlighting the Limits of the European Market

The story of Adaptive ML also illustrates the limitations of the European market. Despite having an office in Paris and a French ecosystem, the startup conducted most of its business in North America, with no major French clients in its portfolio at the time. This absence is due to the slow pace at which large European companies adopt innovations developed by startups and their limited capacity to contract with them.

While Europe effectively funds its tech startups, it still struggles to create a sufficiently dynamic domestic market to allow them to become independent global leaders.