North v3 Brings Azure, AI, and Data Spend Into One Financial View

North.cloud released v3, adding Azure support, AI spend tracking, and an ML-powered commitment engine called Autobot to unify cloud, AI, and data costs into one financial view. CEO Matt Biringer said the release reflects that cloud spend now includes AI models, data platforms, GPUs, and multiple providers. The update integrates with Snowflake, ChatGPT, and Anthropic, and extends Rightsize to GCP.

North.cloud's v3 release brings Azure coverage, AI spend tracking, and automated commitment management into one financial view. Cloud cost management tools were built to track compute and storage, across one provider or several. AI model usage and modern data platforms became new costs those tools were never built to see. AI spend used to be small enough to overlook. Now it is one of the largest and fastest-growing costs on the infrastructure bill. "Cloud spend no longer stops at compute and storage," said Matt Biringer, CEO of North.cloud. "It includes AI models, data platforms, GPUs, and multiple cloud providers. North v3 reflects that shift by bringing those systems together in one place and automating more of the work required to manage them." North.cloud's v3 release treats those costs as one financial picture: cloud, AI, and data spend tracked together, instead of three separate line items in three separate places. Microsoft's OpenAI anxiety shows cloud loyalty has limits https://startupfortune.com/microsofts-openai-anxiety-shows-cloud-loyalty-has-limits/ Microsoft's worries about OpenAI shifting leverage toward Amazon show how fragile the AI infrastructure market has become. The partnership still matters, but cloud exclusivity is giving way to bargaining power, customer access, and compute scarcity. - how to build customer loyalty in AI partnerships https://startupfortune.com/microsofts-openai-anxiety-shows-cloud-loyalty-has-limits/ - why Microsoft fears losing OpenAI to Amazon cloud https://startupfortune.com/microsofts-openai-anxiety-shows-cloud-loyalty-has-limits/ Extending across all three major hyperscalers With v3, North.cloud is adding support for Azure, joining the existing coverage of AWS and GCP, following a beta program with early customers. That brings all three major hyperscalers into a single view, which matters for the growing number of companies running workloads across more than one cloud provider and previously stitching together separate reports to understand total spend. Alongside cloud infrastructure, North v3 also integrates natively with Snowflake, ChatGPT, and Anthropic, extending the platform's visibility into AI and data spend rather than treating those categories as an afterthought. The result is that infrastructure, AI, and data costs are pulled into one place instead of living across a patchwork of dashboards and billing exports, which is what most teams are still doing today when cloud, AI, and data spend sit in three different systems answering to three different owners. Autobot and automated commitments The release also introduces Autobot, a new ML-powered engine that models a customer's usage patterns and automatically ladders commitments to match. Rather than teams manually estimating usage tiers and negotiating commitment levels by hand, Autobot is designed to adjust those commitments based on actual, observed usage over time, balancing a mix of one and three year terms as needs evolve. Because Autobot scales commitments up or down as usage changes, customers avoid paying for capacity they do not use, while still capturing savings on what they do. North.cloud pairs Autobot with Flexbot, their existing engine that holds three year commitments in North's own accounts, so customers can combine the flexibility of month to month coverage with the deeper discounts of longer terms rather than choosing one approach for an entire infrastructure footprint. Optimization and visibility beyond the commitments engine The v3 release carries several other pieces built around the same idea, that infrastructure spend should be managed as one system rather than several. Rightsize, which monitors infrastructure in real time and recommends the optimal size for each resource based on usage and risk, now extends to GCP alongside AWS, with additional providers planned. TokenFlow, available in early beta, governs token usage, spend, budgeting, and model health, extending the platform's optimization logic into the AI layer specifically rather than treating it as a cloud cost like any other. On the visibility side, Generative Dashboards, in beta, let users build several customizable views and surface insights using natural language within Noros, without waiting on an analyst. A redesigned commitment management experience also adds more granular visibility along with interactive simulations for evaluating savings strategies before a purchasing decision is made. The release also brings a simplified interface aimed at both engineering and finance teams, who are often working from the same numbers but asking different questions of them, alongside a refreshed brand and updated site to go with it. The scale behind the release Together, the hyperscaler coverage and the automated commitment engine point toward North.cloud's broader goal with v3. The company describes it as becoming the financial operating system for cloud, AI, and data spend, a single system of record for infrastructure costs that companies increasingly manage as one rather than splitting the work across a cloud team, a data team, and whoever ends up owning the AI bill. North.cloud is approaching two billion dollars in managed cloud spend and says it has helped customers save more than four hundred million dollars through optimization and automation to date, the kind of base that makes a bet on managing AI and data spend the same way worth watching as those categories keep growing. Harvey Built Its Own Legal AI Model Instead of Renting One From OpenAI https://startupfortune.com/harvey-built-its-own-legal-ai-model-instead-of-renting-one-from-openai/ Harvey, the legal AI startup valued near $15.5 billion, has launched Tenet, its first proprietary model, built on a customized Kimi K3 base and post-trained on attorney-generated case files. The model ships inside a broader Harvey II relaunch that also introduces a Memory feature for law firms. - why legal AI startups build their own models https://startupfortune.com/harvey-built-its-own-legal-ai-model-instead-of-renting-one-from-openai/ - how law firms fine-tune AI with case files https://startupfortune.com/harvey-built-its-own-legal-ai-model-instead-of-renting-one-from-openai/ North.cloud positions itself as a cloud cost optimization tool https://north.cloud/ built for that shift specifically, one system tracking cloud, AI, and data spend together rather than three tools stitched together after the fact.