Datadog is close to releasing an updated AI model that it thinks will help it avoid the so-called SaaSpocalypse – customers using AI to build their own tools. The observability tools vendor already created a model called Toto-Open-Base that the company's explanatory paper says it built with 151 million parameters, trained on more than two trillion time-series data points – apparently the largest pretraining dataset for any open-weights time-series foundation model. All the data used to train the model came from Datadog itself, gathered in the course of operating its SaaSy observability services. In conversation with The Register, Datadog chief product officer Yanbing Li said the company is reviewing its next model but sees that effort as the means to an end. "What is the SaaS company's role?" she asked, before answering: "To innovate in their domain." For Datadog, that means creating a model specific to its domain – observability – rather than relying on a generic LLM. Li thinks developing models brings two things to Datadog. One is that AI becomes part of its platform, rather than requiring customers to set a token budget on another service. The other is better agents that detect and predict anomalies more effectively. She claimed Datadog's site reliability agent can already investigate incidents, provide root cause analysis, and suggest remediation actions. AI remains a flaky field and agents make mistakes. The Register therefore put it to Li that operators of mission-critical IT must be wary before letting agents suggest changes to their systems, let alone enact those changes without supervision. She agreed and said for AI systems to win trust, their output must be both explainable and verifiable. Using its own models makes that easier for Datadog, she said. They have also helped the company to create a tool that watches AI platforms while they work and can detect signs they are producing hallucinated output. "I do not worry about a race to develop models, but ap...
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