Comparison
GetFlowing vs AI coding tools.
AI coding tools help you produce code. GetFlowing is an AI-powered software delivery platform that covers the whole lifecycle around that code: planning, building, testing, deploying, monitoring and maintaining software in production.
Short answer
An AI coding tool sits inside the editor and makes a developer faster. GetFlowing sits around the software itself: it plans the work, implements it in your GitHub repository, runs the tests, builds the container, deploys to your cloud, watches production and keeps improving the application afterwards. Code generation is one stage of GetFlowing, not the product.
Still required after the code exists
Code is the cheap part now.
AI has made it easy to generate working code. The expensive part moved: businesses now struggle to turn that code into a reliable product they can release, operate and keep changing.
- Deciding what to build, in enough detail that the work is safe to start.
- Reviewing changes and keeping them small enough to review honestly.
- Running tests that mean something, on every change, before merge.
- Containerising the application and keeping images versioned.
- Provisioning and updating cloud infrastructure without downtime.
- Watching traffic, latency, errors and uptime once real users arrive.
- Investigating incidents with the application context that caused them.
- Maintaining the software months later, when the original context is gone.
Side by side
The same six stages, from two different places.
An assistant finishes when the code compiles. These are the stages that decide whether it ever reaches a customer, and whether it survives once it does.
- Start
- With an AI coding assistant
The prompt is the specification. What the change was meant to do is reconstructed from the diff afterwards, by whoever reviews it.
- With GetFlowing
Ask for the change and agents begin. Work big enough to be worth agreeing up front can go through a planning workspace first, which is optional.
- Implement
- With an AI coding assistant
Code appears in the editor of whoever asked for it, at whatever size the session happened to produce.
- With GetFlowing
Agents work in your own GitHub repository and open a pull request small enough for a person to actually read.
- Prove
- With an AI coding assistant
Tests are one more thing to ask for. Running them on every change is a pipeline you build and then keep working.
- With GetFlowing
Test suites run against the change and the results are attached to it, before it is allowed to merge.
- Ship
- With an AI coding assistant
Outside the tool entirely. Containers, registries and cloud configuration are assembled by hand and maintained by hand.
- With GetFlowing
The container is built, stored privately and rolled out to AWS behind health checks, with production pausing for approval.
- Operate
- With an AI coding assistant
Production lives in a different product, so the release and the error it caused are recorded in two places that do not know about each other.
- With GetFlowing
Traffic, latency, errors and incident context sit next to the deployment that introduced them. A failed release or an app that stops serving opens an incident by itself, so the first person to notice does not have to be you.
- Continue
- With an AI coding assistant
Six weeks later the session has gone and the reasoning with it. The next change starts from the code and a guess.
- With GetFlowing
The next change starts from the application as it is now, which is the part that gets hard once nobody remembers writing it.
When an AI coding tool is the right choice.
If you already have an engineering team with its own planning, review, testing, deployment and on-call practices in place, an assistant that accelerates individual developers may be exactly what you need. GetFlowing is the better fit when the gap is delivery capability rather than typing speed, or when software the business depends on has no team looking after it.
Comparison questions
AI coding tools, app builders and delivery platforms
How GetFlowing sits next to the other categories teams evaluate.
Is GetFlowing an AI coding assistant?
No. An AI coding assistant helps a developer write code inside an editor. GetFlowing is an AI software delivery platform: it plans work, implements it in your repository, tests it, containerises it, deploys it to cloud infrastructure, monitors it in production and maintains it afterwards.
What does GetFlowing do that an AI coding tool does not?
Tests attached to the pull request before merge, container builds and a private image registry, deployment to AWS with health checks, production monitoring and incident context, approvals with an audit trail for risky actions, a planning workspace when a piece of work is worth agreeing before it is built, and the maintenance that continues long after the first release.
Does GetFlowing write code inside my editor?
No. GetFlowing works in your repository rather than in your editor. Agents implement the change on a branch and open a pull request, and you review it in GitHub or from the dashboard alongside the test results for that change.
Can I keep using an AI coding assistant alongside GetFlowing?
Yes. Your repositories stay in your GitHub account, so developers can keep whatever editor and assistant they prefer. GetFlowing contributes through the same review process everyone else uses, which means both kinds of work meet in one pull request history.
When is an AI coding tool the better choice?
If you have an engineering team with its own planning, testing, deployment and on-call practices already in place, an AI coding assistant that accelerates individual developers may be all you need. GetFlowing is the better fit when the gap is delivery capability rather than typing speed.
Something not covered here? Email hello@getflowing.ai and we will answer directly.
Private beta
Bring software delivery into one system.
Build something new or take control of what already exists. Get the planning, engineering, deployment, and operational capability of an experienced software team.