What is our primary use case?
Ona Enterprise is used at Roche as a secure cloud development and AI software engineering environment, where the main use case is to provide a standardized development environment and experiment with AI-assisted software engineering workflows in a controlled enterprise setup. In addition to this, it is also useful for tasks such as setting up development environments quickly, working with repositories in isolated workspaces, running tests and tools in the cloud, using AI agents, as well as coding assistance, refactoring, documentation, and pull request preparations. In an enterprise like Roche, the biggest value is not just the AI coding capability; it is the combination of developer productivity, environment standardization, security controls, and governance.
Ona Enterprise is usually connected to GitLab repositories, and then an environment will be automatically set up, after which the cloud coding assistant is used. It also provides better CPU and GPU configurations, so the terminal is usually used to build applications as well as for all other daily development tasks.
The secure cloud setup helps our team because initially, we had a Windows local environment, and sometimes packages break once moved from Windows to Linux. In Ona Enterprise, this is not a concern because there is a Linux terminal that is similar to what we have in AWS, allowing the same thing to be deployed there, making it easy to test it out. Regarding the secure cloud setup, whatever experiments are needed, they will occur in a separate environment in Ona Enterprise. This will not affect anything inside Roche, ensuring that any security breach will not occur in Roche. If any code execution occurs, it will not impact the Roche environment, which helps Roche in terms of security concerns.
What is most valuable?
The most valuable features Ona Enterprise offers are the secure cloud development environments, as well as background agents and enterprise guardrails. Ona Enterprise also helps reduce local environment issues because the developer and agents can work in a clean, policy-controlled environment. A Linux environment is also provided that is mostly favorable to the production-grade environment, which is similar to what is available in Ona Enterprise as well. The ability to delegate tasks to agents while keeping the work inside an isolated environment is also valuable. This is important in enterprise settings where source code, credentials, and internal systems need stronger controls than what normal local AI coding assistant tools provide. Ona Enterprise also offers SSL support, so it can be connected through local VS Code with SSL.
What needs improvement?
The main area of improvement for Ona Enterprise is transparency and simplicity around the pricing and usage consumption. Ona Enterprise uses compute units, and while this model makes sense because it combines environment runtime as well as agent usage, it can be difficult for teams to estimate the cost until they have real usage patterns.
Another area of improvement can be onboarding of enterprise users, which was also difficult. Ona Enterprise has many capabilities such as environments, agent automations, guardrails, identity management, and integrations. New users may need clearer guide paths depending on whether they want a basic cloud IDE, AI coding support, background automation, or full enterprise governance.
It would also be beneficial to see more out-of-the-box templates, such as an example of a regulated enterprise environment, especially around safe agent usage, approval workflows, test requirements, and audit practices.
For how long have I used the solution?
Ona Enterprise has been used at Roche for the last one year.
What do I think about the stability of the solution?
I have not experienced downtime with Ona Enterprise, but I have experienced issues while connecting with a local SSL server.
What do I think about the scalability of the solution?
Ona Enterprise is designed for scaling assisted software engineering in large organizations. It is especially valuable when many developers or teams need consistent environments, secure access, and controlled use of AI agents. The scalability benefits become more obvious as the number of repositories, developer environments, and AI-assisted workflows increases. For a single small project, it may feel like more infrastructure than necessary, but for an enterprise environment, the standardization and governance are the key advantages.
Which solution did I use previously and why did I switch?
No other solution was used before Ona Enterprise.
How was the initial setup?
Initially, for every development environment, a better local configuration had to be provided. For example, if a virtual VDI was being provided for multiple team members, it was necessary to decide what configurations to provide such as RAM, CPUs, and other components, and whether GPU was required based on those needs. After implementing Ona Enterprise, local machines no longer require this level of care because it runs on the cloud. It is no longer necessary to worry about what exactly the local configuration is because every configuration is being utilized by Ona Enterprise.
Which other solutions did I evaluate?
Before choosing Ona Enterprise, comparisons were made with GitHub Codespaces, GitHub Copilot Workspace, coding agents, Cursor, and Vercel. After comparing these options with simple AI coding assistance, Ona Enterprise is more enterprise platform oriented because it is not only about code completion. Ona Enterprise combines cloud development environments with CPUs and GPUs, background agents, policy enforcement, auditability, and enterprise deployment options.
What other advice do I have?
The built-in AI agent in Ona Enterprise already has guardrails. If there is an attempt to access security-sensitive features, it does not allow that. Additionally, when the AI agent needs to execute commands, it is very careful about commands such as delete operations. The platform is reliable for standardized cloud development environments and AI-assisted development workflows. The ability to create clean, isolated environments reduces many local machine problems, so there is no need to worry about local configurations because Ona Enterprise already provides much more configuration at the time of setup. For agentic workflows, human review, automated tests, and clear guardrails are still recommended so that the agent can accelerate work but should not be treated as a replacement for engineering quality controls.
Time management has definitely improved. There is no need to set up the environment every time, which makes development easier. Ona Enterprise automatically clones the repository and has a coding assistant that saves time in daily development purposes.
Ona Enterprise's pricing model is based on compute units, which encompasses whatever compute engines are used for CPU, RAM, and GPU. This covers both environment runtime and agent conversations, which is logical because the platform is not just an IDE but also runs cloud infrastructure and AI agent workloads. However, the model requires monitoring because usage can vary depending on how long environments run, how many parallel tasks are delegated to agents, and how much automation is used. For enterprise users, it is recommended to start with a controlled pilot, measure compute unit consumption, and then estimate the cost per team or per workflow. For large workflows, the cost can be justified if it reduces developer setup time, improves onboarding, and enables secure AI-assisted development. For a small team or lightweight use case, the value needs to be compared against similar tools.
Ona Enterprise would be recommended for other organizations, particularly large-scale organizations, because it is a strong fit when the organization has multiple repositories, complex development, and strict security requirements as well as needs a standardized developer workflow. It is very important to start with a focused pilot, choose a few repositories, and define the development container setup properly. Overall, I would rate this product a nine out of ten.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
