Should you build that internal ai tool — or just pay for it?

I’m also a proponent of building things yourself. With so many open APIs available, staying closely attuned to products reveals countless market examples that can inspire your work and motivate you to turn ideas into reality. And with today’s AI tools, bringing those ideas to life is remarkably convenient. Thanks to these advances, I’ve successfully built a game website of my own.

This framework is incredibly practical and something every founder should bookmark. I especially appreciate the point about maintenance costs - it's so easy to underestimate how much a side project can quietly consume your team's bandwidth. The question about whether building gives you an unfair advantage is a game changer for cutting through the noise.

This concept resonates with my experience working with AI-based programming tools such as Claude Code. I have learned to use existing APIs (Gemini, OpenAI) to perform complex tasks and focus on developing only unique workflow integrations — the part that really solves my specific problem.

The cost of service is clearly stated. “Weekend projects” have a tendency to turn into ongoing commitments. Now I ask myself, “Will this tool save me 10+ hours per month?” If not, I pay for a ready-made solution.

It's a great reminder to focus on what creates real differentiation.

Great! I think most founders overestimate the build part because it feels like progress. But often, buying frees up focus for what actually drives growth. The real question isn’t can we build it, it’s should we even spend our energy here.

Such a solid framework 👏 — love how you broke it down into simple, practical questions.
The reminder that speed > control (especially pre-PMF) really hits home.
“Build only where you have an unfair advantage” — that’s the golden rule every indie hacker needs to remember. 💡🚀

This framework is spot on. We're actually facing this right now with our hiring process at hirelens — debating whether to build our own AI interview analysis tool or use an existing solution.

This is a nicely structured summary of an important concept. A startup team may have little money, but time is an even scarcer resource. If a large language model (LLM) can do it, and LLM economics won't kill you, then just use a simple LLM tool call. There are three circumstances when the simple LLM call won't work:

1. You are working in an environment - mobile or laptop app - when Internet access is not guaranteed
2. You DO have massive usage - thousands of users <times> many LLM calls per click. That can get expensive.
3. It is something genuinely trivial to do. Just ask ChatGPT to write an optimized script using existing standard tools to do the job.

It is important to understand that there are many interesting applications where an LLM is NOT the right way to process the data. In areas like media processing there may be non-AI media processing libraries (think ffmpeg or OpenCV) that do equally well at zero cost, or media AI functions that can be leveraged from open source (e.g. YOLO object recogniton) or built from scratch.

Building AI models from scratch is not a small undertaking, but the rise of vibe coding is lowering the barrier to entry even here. The most critical steps are 1) finding lots of good data that represents your use-case well and 2) finding a way to curate and label that data for use in the training flow. Think closely about what analysis or transformation of the data stream you really need. If you can build a genuinely useful model of your own, it can become a foundation for your differentiation, agility and value. This strategy works best, though, when you're actually taking advantage of all the benefits of owning the model:
. you can run it anywhere you want - any server, and edge platform
. you have low or zero costs for model inference
. you can adapt it as needed to new functions and data sets.

If you're not taking advantage of all or most of these dimensions of flexibility, you may be best off with an existing LLM even for media processing.

Great framework! This resonates with our experience. We faced a decision whether to build Great framework! As a founder building an AI tool for converting bank statement PDFs to CSV, we initially considered developing our own data extraction pipeline from scratch. Instead, we used an off-the-shelf OCR library and focused our efforts on the features that matter most to accountants and small business owners. This saved us months of work while still allowing us to deliver a unique product. Thanks for sharing this decision tool!or buy a tool to convert bank statement PDFs into CSV files. Off-the-shelf solutions didn't handle different banks' formats well, so we decided to build our own. If there had been a robust off-the-shelf solution, we would've gone with it. Your questions help clarify that decision process. Thanks for sharing!

We’re experimenting with an AI startup factory model (90-day build cycles, ROI focus).
Totally agree with your framework - the “unfair advantage” question filters 80% of distractions.
Curious how other founders balance speed vs depth when building AI-native MVPs. Any lessons learned from rapid validation?

Don't really go over AI hype, it depends on your product so be specific if possible to implement it otherways, don't

As an engineer, it’s way too easy to justify building everything “because we can.”

I’ve learned the hard way that internal tools rarely stay small — they quietly turn into products you have to maintain forever.

Great piece. The decision framework around build vs. buy for internal AI tools really hits home. If it doesn’t give you a strong edge, you’re usually better off buying. Excellent reminder.

Really like this take; it nails the build vs. buy dilemma most teams ignore until they’ve sunk months into a “quick internal tool.” The rule of thumb you outlined — if it’s not core to your differentiation, pay for it; feels right.

Where I’ve seen teams get it wrong:

Overestimating their edge (“we’ll customize it better”) instead of just needing reliability.

Underestimating the maintenance drag once that “MVP” becomes critical.

Forgetting that vendor costs are often cheaper than engineer context switching.

Curious; where do you personally draw the line now? Feature depth (e.g. fine-tuning or data access) or speed of iteration?

P.S. I’m with Buzz; we build conversion-focused Webflow sites and pragmatic SEO for product launches. Happy to share a 1-page GTM checklist if useful.

Really solid advice. I agree that not every idea needs a complex build — sometimes a simple, focused tool can make a big difference. A good example is Docudown
, a lightweight project that lets students instantly download study materials without unnecessary steps. It’s a smart solution built around a clear user need, which is exactly what this post talks about.

a very smart breakdown of build vs buy. I’ll be showing this framework to my team when we next evaluate internal tooling. Thanks for the clarity and the reminder that speed, leverage and focus matter more than building every capability ourselves.

That's an interesting point about prioritizing speed early on. While I understand the need for rapid iteration, I wonder if completely disregarding structure might lead to unsustainable technical debt and scalability issues later. Perhaps a balance is key, considering the long-term consequences of certain shortcuts.

This is one of the most critical strategic decisions a founder will make in 2024/2025. The "build vs. buy" question for AI tools has a hidden third option: "Assemble."

Here’s a simple framework I use:

1. Build ONLY if the AI tool IS your core defensible moat.

· Is the unique data it uses or the specific way it processes information the reason you will win in the market? If yes, you must build it. This is your secret sauce.
· Example: A hyper-personalized recommendation engine for a niche market where your proprietary user data is the key input.

2. Buy (or use a paid API) if it's a standardized, non-differentiating task.

· Are you just adding a common AI feature (e.g., generic text summarization, standard sentiment analysis, basic image generation)? Then paying for an API or a SaaS tool is almost always cheaper and faster.
· Example: Using OpenAI's API to add a "summarize this article" feature to your app. Don't waste cycles building your own model.

3. ASSEMBLE if it's a complex workflow that provides internal leverage.

· This is the most overlooked option. Use no-code/low-code platforms (like n8n, Make, or even custom GPTs) to assemble existing AI APIs and services into a powerful internal tool.
· Example: Building a "Marketing Content Qualifier" that uses the OpenAI API to analyze a draft, checks it for SEO keywords via another API, and then logs the result to a Google Sheet—all automated in n8n.
· Why assemble? You get 80% of the custom functionality for 10% of the development cost and time. It's the ultimate lean approach for internal tools.

The Final Question: Before you write a single line of code, ask: "Will this AI tool directly generate revenue or save me a minimum of 40+ engineering hours per month?" If the answer is no,the decision is almost always to buy or assemble.

Great post for sparking this essential discussion.

Hi springboard,
Sounds frustrating—I've hit that shift visibility snag in FSM before; turns out, the Workforce Optimization plugin is usually the key (peek under System Definition > Plugins). If it's active, try flipping the "Show Availability" toggle in calendar prefs or double-check the cmn_schedule "Active" flags on user records. Quick config tweak fixed it for us. Fingers crossed—let us know how it goes!

Question 4 is always the toughest, I think. But part of the necessary legwork. Thanks again