One of our Mentor Table members recently walked the community through something that made everybody pay attention: they had replaced roughly ten paid software subscriptions with tools they built for their own businesses using Claude.
They are not a developer. Before this year, they had never written a line of code.
They started with an inventory problem that their paid software could not solve. They explained the workflow to Claude, shared screenshots of the tool they were already using, and asked the model to help them rebuild it around the way their warehouse actually operates.
That first project turned into inventory software, financial reporting, project management, customer support, marketing attribution, SEO research, and conversion testing. By their estimate, the replacements are saving their businesses about $60,000 a year.
That is a very fun number. It is also the wrong place to start.
The useful lesson is the process: find one expensive tool that forces your team into a generic workflow, rebuild the smallest useful version, test it beside the original, and keep the paid subscription until the new system proves it can do the job.
You do not need to rebuild your entire software stack. You probably should not. But one annoying subscription? That may be worth a closer look.
The short version
- Start with an internal tool that is expensive, frustrating, and far away from payment processing.
- Give the AI a clear description of the current workflow, screenshots, examples, and the specific outcome you need.
- Let it interview you and plan the build before it starts writing code.
- Run the new tool beside the old one long enough to compare the output.
- Keep humans in charge of security, permissions, financial accuracy, and the final decision to switch.
What does it mean to replace a SaaS subscription with AI?
In this case, it means using an AI model to help you build custom software that lives on infrastructure your business controls.
You describe the problem in plain language. The model helps plan the system, writes the code, and walks you through connecting the pieces. You still make the decisions, approve permissions, test the output, and decide when the tool is ready for real use.
This operator used Claude, GitHub, Vercel, and Neon:
- GitHub stores the code and keeps a version history, so you can roll back a change if something breaks.
- Vercel hosts the application and makes it available on the web.
- Neon stores the data the tool needs to remember.
- Claude helps plan, build, troubleshoot, and update the software.
Similar workflows are possible with other large language models, including ChatGPT and Gemini. The member’s actual build used Claude, so that is what we are describing here.
His first project came from a very specific operational headache. their business receives inventory at its own warehouse, kits it, and then redistributes it across sales channels. The inventory software they were paying for could track stock levels, but it could not calculate reorder points and redistribution needs around that warehouse model.
So they showed Claude how the current software worked, explained what was missing, and rebuilt the system around the real operation.
That specificity is what made the project useful. “Build me inventory software” is a giant, vague request. “Help me calculate reorder points for this exact warehouse and channel workflow” gives the model a job it can scope.
How do you choose your first software subscription to replace?
Make a list of every software subscription your business pays for. Add the monthly cost, who uses it, what it does, how painful it would be to lose, and whether it touches money or sensitive customer data.
Then look for a first project with four qualities:
1. It costs enough to matter.
2. Your team uses only a small part of what you are paying for.
3. The workflow is easy to explain and verify.
4. A mistake would be annoying, not catastrophic.
That last point matters. A project board is a much friendlier first build than subscription billing.
Once you have a candidate, use this sequence:
1. Create a free GitHub account for the code.
2. Create a free Vercel account for hosting.
3. Create a free Neon account for the database.
4. Take screenshots of the current tool and document how your team actually uses it.
5. Write down the features you need, the features you do not use, and the problems you want the new version to solve.
6. Ask Claude to interview you before it scopes the project.
7. Review the plan before any code is written.
8. Connect each service one step at a time using the narrowest permissions the tool needs.
9. Test the important workflows with sample data before connecting production systems.
10. Run the replacement beside the paid tool until you trust the output.
The first build carries most of the setup work. Once GitHub, Vercel, and Neon are connected, later projects can reuse the same foundation. They still need their own testing and permissions, but you are no longer learning every part from zero.
Which tools are easiest to rebuild first?
The best early candidates have familiar workflows, clear inputs and outputs, and no payment processing. Project management, basic reporting, internal dashboards, SEO research, and simple support tools can fit that description.
This member rebuilt several categories, each one tied to a real problem:
- A project management and task tracking system, similar to a basic kanban board, built in an afternoon.
- A customer support help desk with shared inboxes, live chat, order history, and phone support added over time.
- An ecommerce financial reporting system that connects sales channels and bank data and supports the journal entries that their previous tool could not handle. Anything this close to the books still needs careful review by the people responsible for financial accuracy.
- A marketing attribution dashboard with post-purchase surveys and on-site pixel tracking.
- An SEO and keyword research tool built on a lower-cost third-party data provider.
- A conversion testing system with more flexibility than the paid platform it replaced.
The cost ranges below are category estimates from the original working session. Vendor pricing and rebuild difficulty will vary by business, scope, integrations, and data requirements.
| Software category | Typical monthly cost | Difficulty to rebuild with AI | What it requires |
| Project management / task tracking | Free to $50 per user | Low | A basic database and interface; no outside integrations needed |
| Customer support help desk | $500 to $1,000 | Moderate | Email, live chat, and order history pulled from your ecommerce platform |
| Marketing attribution and analytics | $2,000 to $3,000 | Moderate to high | Website pixel tracking, ad platform connections, and ecommerce data |
| SEO and keyword research | $700 to $1,000 | Low | Access to a low-cost third-party SEO data provider |
| Conversion rate optimization / testing | $500 to $800 | Moderate to high | Direct code access to your website and audience segmentation logic |
| Legacy CRM with tagging and automation | Up to $1,800 | High | Deep integration with your website or storefront, plus a full data migration |
The easiest tool on your list may not be the most expensive one. That is okay. A small internal win teaches your team how the process works without putting a critical system at risk.
How much can ecommerce brands save?
The answer depends on how many tools you replace and what it costs to run the new ones.
In this member’s case, the subscriptions they replaced had totaled several thousand dollars a month. their custom stack now runs for well under $100 a month in basic hosting, plus one AI subscription. Across roughly ten tools, they estimate annual savings of about $60,000.
Those are their numbers across their businesses. Treat them as a case study, not a universal promise.
The more important part is how they validate the switch. Before canceling a paid tool, they runs the replacement beside it for about two weeks and compares the output.
For their marketing attribution dashboard, the two systems landed within about 98% of each other on ad spend, return on ad spend, and revenue attribution. That was close enough for their team’s daily decision-making.
For a project board, you may not need two weeks. For reporting that influences media spend or financial decisions, the parallel test is worth the extra time. Pick the validation period based on the risk of being wrong.
Which subscriptions should you leave alone?
Some software is expensive because the job itself is complicated.
Payment processing, subscription billing, affiliate payouts, payroll, tax, and compliance-heavy systems carry financial and legal risk. A model can help you explore the requirements, but these are poor choices for a first AI-built replacement.
This operator has intentionally kept a few subscriptions:
- Their affiliate platform is possible to rebuild, but accurate payout tracking makes it a longer project.
- They have not tried to replace subscription billing because stored payment methods and recurring charges deserve a much higher level of scrutiny.
- A social media engagement tool was harder than expected because it has to pull comments from several platforms into one inbox. A working version exists, but the paid tool is inexpensive enough that finishing the replacement is not a priority.
That is good operating discipline. The goal is not to cancel the most subscriptions. The goal is to own the workflows where custom software creates a meaningful advantage or saves enough money to justify the work.
How do you manage access across a team?
There are two kinds of access to think about: access to the AI that helps build the software, and access to the finished software itself.
Claude team plans can give teammates different usage levels based on their roles. Someone using the model for occasional support work may need less capacity than the person actively building and maintaining internal tools.
The finished applications need their own controls. Each tool should live in its own code repository and hosting project so a change to one system does not accidentally break another. Team members should get only the access their roles require.
For a larger build, keep the project context, requirements, decisions, and update history together. That makes future changes much easier because the model can see why the system was built a certain way instead of guessing from the current code.
And please treat credentials like credentials. Keep API keys and production secrets out of shared documents and ordinary chat messages. Use the secure environment and permission settings provided by the connected services, and review access when a teammate changes roles or leaves the company.
Where should you start?
Give yourself 30 minutes and make the software list.
Circle one tool that costs enough to annoy you, forces your business into a workflow that does not quite fit, and can be tested without risking payments or customer trust.
Then ask the AI to help you scope the smallest version that would be genuinely useful. Not the dream version. Not every feature the paid platform has collected over ten years. The version your team would use next week.
Build that. Test it. Keep the old tool running while you compare the two. If the replacement works, you have saved money and learned a capability your business can use again. If it does not, you have learned where the complexity lives before canceling something important.
These are the kinds of conversations happening inside Mentor Table: real operators bringing in real workflows, real costs, and what happened when they tried the idea in their businesses.
We will also be covering more AI-built workflows live at Smart Marketer Live 2026 on October 20 and 21 at the Denver Art Museum in Denver, Colorado.
If this article has you eyeing your software bill a little differently, start with the list. One useful project is plenty.
