You’re paying thousands per month for Salesforce, Zendesk, or HubSpot. Your team uses maybe 20% of the features. You’ve spent weeks configuring workflows that still don’t quite match how your business actually operates.
There’s another option now. Custom internal tools tailored to your business can be built faster and cheaper than you’d spend configuring enterprise software.
The Real Cost of Enterprise SaaS
Enterprise software promises flexibility. In practice, you get:
- Per-seat pricing that scales painfully as your team grows
- Features you’ll never use subsidizing features you need
- Workflows that force you to adapt your process to the software
- Consultants and integrators to customize what should have fit from the start
The dirty secret? Most businesses don’t need 80% of what these platforms offer. They need a few core workflows that match exactly how they operate.
Why Custom Used to Be Out of Reach
Building custom software used to mean six-figure budgets and six-month timelines. Only enterprises could afford it. Everyone else made do with SaaS that was “close enough.”
That equation has changed. Chris Gregori puts it well:
“Code is cheap now. Software isn’t.”
What used to take developers weeks now takes hours. AI coding tools have collapsed the cost of writing code. But maintenance, edge cases, and long-term reliability still require human judgment.
For internal tools, that tradeoff works in your favor. You control the inputs. You’re the only user. You can update when requirements change.
Theo Browne asks a question worth sitting with:
“What are some things that you would build if you had more time and knowledge?”
A CRM that matches your exact sales process. A support tool that handles your specific workflow. A dashboard showing the three metrics you actually care about, not 42.
These tools don’t exist because they’re too specific. No SaaS company would build them. That’s exactly what AI makes possible now.
AI Makes It Possible, But Not Automatic
Antirez, creator of Redis, recently filed a PR replacing 3,800 lines of C++ with a minimal C implementation:
“This code was written by Claude Code using Opus 4.5 and tested carefully. The code review was independently performed by Codex GPT 5.2.”
AI wrote the code. A different AI reviewed it. A human made the final call.
This is the pattern that works: AI as a force multiplier, not a replacement for judgment. As Theo puts it:
“We’re all managers now.”
But the demos are misleading.
Non-coders watch someone build an app in 10 minutes and assume they can do the same. They can, for prototypes and simple tools. Anything beyond that falls apart without the knowledge to evaluate what the AI actually produced.
Junior devs get real benefits: faster iterations, quicker bug fixes, less time stuck on boilerplate. Genuine productivity gains.
But the multiplier effect lives with senior devs. They accept more AI-generated code than anyone, not because they’re less careful, but because they know how to direct it. Clear specs. Small chunks. Fast reviews. They have what AI coding rewards: clarity, delegation, and orchestration. The result is production-ready code that’s well-architected and easy to maintain.
What matters now is knowing what to build and clearly describing why.
The New Math
Enterprise SaaS: thousands per month, 20% feature utilization, workflows that don’t quite fit.
Custom internal tool: one-time build, exactly what you need, no per-seat scaling.
The SaaS model made sense when custom software required months of development. AI-assisted development changes that equation.
But AI won’t build reliable tools on its own. You need engineers who can translate business requirements into clear specs, review AI output, and maintain systems over time.
At Flowful, we’re senior developers and AI engineers. We build custom internal tools that replace expensive enterprise licenses with software that fits how you actually work.