Google has officially launched its AI coding agent, Jules, out of beta, just over two months after its public preview debut in May. This move follows a period of intensive development and feedback, which saw the tool receive hundreds of UI and quality updates, according to Kathy Korevec, director of product at Google Labs.
Powered by Gemini 2.5 Pro, Jules operates as an asynchronous, agent-based coding tool designed to integrate with GitHub. It functions by cloning codebases into Google Cloud virtual machines, leveraging AI to fix or update code, thereby allowing developers to focus on other tasks. Google initially introduced Jules as a Google Labs project in December, making it available for public preview at its I/O developer conference.
Korevec told TechCrunch that the improved stability of Jules as a key factor in the decision to move it out of beta, stating, “The trajectory of where we’re going gives us a lot of confidence that Jules is around and going to be around for the long haul.”
With this wider rollout, Google has introduced structured pricing tiers for Jules. Previously, during beta, there was a 60-task limit. Now, an “introductory access” free plan is available, capped at 15 individual daily tasks and three concurrent ones. For users requiring higher limits, Jules’ paid tiers are integrated into the Google AI Pro and Ultra plans, priced at $19.99 and $124.99 per month, respectively. These plans offer 5x and 20x higher limits. Korevec explained that this new packaging and pricing strategy is based on “real usage” insights gathered over the past few months, with the 15-task daily limit designed to help users assess Jules’ suitability for their real project tasks.
Google has also updated Jules’ privacy policy to provide greater clarity on how AI training data is utilized. Korevec clarified that data from public repositories may be used for training, but “no data is sent” if a repository is private. She noted that this update was a response to user feedback requesting more explicit language, rather than a change in the underlying data handling practices.
During its beta phase, Jules saw significant engagement, with thousands of developers tackling tens of thousands of tasks, leading to over 140,000 publicly shared code improvements. Initial feedback from beta testers led the Google Labs team to implement several new capabilities. These include the ability to reuse previous setups for faster task execution, deeper integration with GitHub issues, and support for multimodal input.
Korevec identified two primary user groups for Jules during the beta: AI enthusiasts and professional developers. A distinguishing feature of Jules is its asynchronous operation within a virtual machine. This contrasts with other popular AI coding tools like Cursor, Windsurf, and Lovable, which operate synchronously and demand continuous user attention to output. Korevec elaborated on this advantage, stating, “Jules operates like an extra set of hands … you can basically kick off tasks to it, and then you could close your computer and walk away from it if you want and then come back hours later. Jules would have those tasks done for you, versus if you were doing that with a local agent or using a synchronous agent, you would be bound to that session.”
Recent enhancements to Jules include a deeper integration with GitHub, allowing the tool to automatically open pull requests, similar to its existing ability to open branches. Additionally, a new feature called “Environment Snapshots” has been introduced, enabling users to save dependencies and install scripts as a snapshot. This feature aims to facilitate faster and more consistent task execution.
Insights from the beta trials significantly informed Jules’ ongoing development. According to data from SimilarWeb, reviewed by TechCrunch, Jules recorded 2.28 million visits worldwide during its public beta, with a notable 45% of these visits originating from mobile devices. India emerged as the top market for traffic, followed by the U.S. and Vietnam. While Google did not disclose specific user base numbers or top geographies, Korevec shared observations from the beta period.
The team noted that many users transitioned from traditional “vibe-coding” tools to Jules, often to address bugs or enhance their vibe-coded projects for production readiness. Initially, Jules required an existing codebase, but Google quickly adapted it to work even with an empty repository, recognizing that many potential users, particularly those exploring other AI tools, might want to experiment without a pre-existing project. This adjustment significantly broadened Jules’ scope and usage.
Another key observation was the increasing number of users accessing Jules via their mobile devices, despite the absence of a dedicated mobile app. Korevec acknowledged this emerging use case, stating, “Since it’s a big use case that we’re seeing emerging, we’re absolutely exploring what the features are that people need on mobile a lot more.”
Beyond external beta testers, Google itself has been leveraging Jules internally for developing certain projects. Korevec revealed that there is now a “big push” within the company to utilize the tool on “a lot more projects.”
