Datadog unveiled the first two launches from Datadog AI Research, which is tackling research challenges that are firmly rooted in real-world problems within cloud observability and security.
Datadog AI Research is actively contributing to the broader research community by publishing findings and open-sourcing model artifacts.
The initial releases from Datadog AI Research are Toto and BOOM.
Toto is an open-source foundation model focused on observability. Time series foundation models (TSFMs) are to time series what LLMs are to language. A type of AI model trained on massive datasets that can be adapted to a wide range of downstream tasks, foundation models learn general patterns and can be fine-tuned for various applications.
Toto is an open-weights model that is trained with observability data sourced exclusively from Datadog’s own internal telemetry metrics, which achieves state-of-the-art performance by a wide margin compared to all other existing TSFMs. Its zero-shot forecasting will enable instant anomaly detection and capacity planning with no per-series tuning, which is critical when monitoring billions of ephemeral time series. While existing TSFMs struggle with telemetry data, Toto heightens performance—not only for observability data but for time series forecasting more broadly—and is freely available.
BOOM introduces a time series benchmark that focuses specifically on observability metrics, which contain their own challenging and unique characteristics compared to other typical time series. It instantly becomes the largest public benchmark of observability metrics, providing 350 million observations across 2,807 real-world multivariate series to capture the scale, sparsity, spikes and cold-start issues unique to production telemetry. BOOM is an actively maintained resource for the community and will allow researchers to advance their forecasting models.
“Today marks the launch of our first open-source foundation model and we expect to continuously release AI projects through Datadog AI Research,” said Ameet Talwalkar, Chief Scientist at Datadog. “The lab offers an exciting opportunity to develop research ideas and build prototypes that will contribute to the community. We will also collaborate with applied AI teams to build tools that will solve customer problems and transform how engineers work.”
Collaboration between Datadog AI Research and Datadog’s product and engineering teams will help translate research advances, like Toto and BOOM, into tangible benefits for Datadog customers.
Toto and BOOM are immediately downloadable under a permissive license and Datadog invites the research and the OSS communities to push observability forecasting forward with these open-source projects.
The Latest
Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...
Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...
One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...
As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...
Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...
The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...
Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...
Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to third party services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...
If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...
Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...