Build or buy? Why a homemade competitor price analysis tool — even with AI agents — rarely pays off

The prototype is the cheapest 5% of the problem

A scraper that worked today and data you can rely on every day are two different things. The prototype handles the first. Your margin depends on the second.

A prototype built in one afternoon proves only one thing: that a specific page, on a specific day, from a specific IP address, could be read by a script. It proves nothing of what turns data into money: that the same data will flow tomorrow, that products are matched correctly, that an error will be caught before it hits your prices, and that someone will fix an outage at three in the morning, before the morning price update.

The entire cost difference hides in this gap — between “works on our test” and “you can build a business on this.” Let’s walk through it step by step.

Cost #1: getting the data at all — reliably and at scale

Large e-commerce sites actively defend against automated data collection. The DIY path immediately means proxy networks, browser emulation, JavaScript rendering, and a constant tug-of-war with anti-bot systems. An AI assistant will helpfully suggest all these tools — but it won’t change either the costs or the fragility of such a solution.

• Commercial proxy traffic and rendering infrastructure are monthly bills without end — and at the scale of real competitor monitoring, these bills alone can match a provider’s subscription or exceed it.
• The fact that it works today means nothing next week. If a site temporarily tolerates your company’s IP address, it’s very likely that after a week of regular scraping it will block you — and the whole “solution” will grind to a halt overnight.

For comparison: behind Dealavo’s data stands dedicated infrastructure — external and in-house proxy mechanisms, rendering, techniques for reading modern dynamic pages — maintained by a specialized five-person engineering team whose entire job is reliable data acquisition. This whole chain is also cost-optimized: for each source we start with the cheapest effective method, and reach for more expensive techniques only when the site forces us to. That’s exactly why the math works at a thousand sources — and the team exists because at this scale the problem is never solved once and for all.

Cost #2: ongoing maintenance that no one puts in the budget

Here’s the number that ends most “build or buy” conversations. Dealavo maintains around 1,000 data sources — and on an average day about five of them break, because a shop changed its layout, rebuilt the site, or deployed new bot protection. Most fixes are quick. But every day or two there’s one that in practice means rebuilding the connection to that source from scratch.

Scale that down to a DIY project watching 20 competitor sites, and the statistics still catch up with you: something meaningful breaks every few weeks — silently. No warning light turns red. The dashboard just shows yesterday’s prices or drops the most important competitor, and your team makes pricing decisions on outdated data without knowing.

Dealavo maintains a second infrastructure layer that does only one thing: watches whether the data collection itself is working — plus a five-person team that fixes outages: for standard sources by morning after the overnight pull, and for critical sources with night duty and reaction within about an hour of an anomaly signal. Clients get information from us about what broke and when it will be fixed — before they notice themselves.

And now a practical question: who at your company will own this system — permanently? What will that person not do in exchange? And what happens when they leave — because an internal single-author tool usually retires along with them?

Cost #3: matching — the error you don’t see until it starts costing money

Collecting prices is the visible half of the work. The other half is determining which offer actually refers to the same product — and that determines whether the data is worth anything at all.

Where there’s a clean EAN code, matching is simple — and a DIY script will handle it. But in a real assortment, the code often isn’t there or is misleading: private labels, sets, multipacks, variants. Then you need matching by names, attributes and images — with AI help — and for really hard cases, manual verification. Dealavo has anomaly detection for this: if an offer matches with strong signals but the price is drastically off, the system flags it instead of trusting it.

Why is this so important? Because matching errors don’t look like errors. They look like data. And if that data feeds automatic repricing, one bad match becomes a bad price — published in your shop, across many products, before anyone asks.

Cost #4: application layer — easy to copy, hard to run at scale

The dashboard is honestly the easiest part to reproduce with AI help — and for a small catalog a DIY panel can be perfectly fine. The trap starts at scale. Competitor price data grows fast: many sellers per product, several channels, daily updates, plus a dimension without which analysis loses meaning — price history. Dealavo pulls tens of millions of prices daily, and getting this amount of data — with history — to display instantly took years of engineering work. A panel built over the weekend on the same data won’t crash spectacularly; it will just get slower and slower until eventually no one opens it.

Cost #5: the last step — ERP and price automation, where errors get expensive

The riskiest part of the whole chain is at its end: writing price recommendations into your systems and turning on automation.

• ERP integrations mature over years on non-standard edge cases. Mature connectors have years of such situations behind them — including safeguards against a mistake that sounds absurd until it happens: for example, writing a price recommendation into the wrong ERP field.
• Repricing needs safeguards more than cleverness. A pricing rules automation engine should have an extensive system of limits and safeguards so that a rash recommendation doesn’t slip into the system and destroy the margin on a product. This isn’t theoretical caution: we’ve already seen cases on the market where a naive repricing rule pushed prices significantly below margin and generated losses larger than the cost of any tool — before anyone noticed.

And this asymmetry decides most “build or buy” debates: the consequence of a DIY error isn’t “we need to fix the script,” it’s a weekend of selling below cost.

“But we also use AI agents” — yes. Intensively. And that’s exactly the point.

This isn’t an anti-AI text. Dealavo developers actually have an obligation to work with agentic coding tools — with full human responsibility for code and architecture. We maintain fine-tuned agent tools that support both the launch and maintenance of our crawlers, and we use AI throughout data operations, including product matching by images. Our scale of AI usage is genuinely large.

And that’s exactly why we know where AI ends today: even specialized agents working on this exact problem every day can’t do without a human. They speed up our engineers’ work — they don’t replace their judgment, night duty, or responsibility.

The simplest test of our honesty: if agentic AI could run this operation on its own, we would have done it that way long ago — few have a stronger motivation to check it every day in practice than we do. Today the answer is: it can’t.

See what it looks like from the inside: a free, Dealavo demo runs on your real catalog and sources you choose — and for larger organizations a paid PoC (up to three months) will let your team compare data quality with anything you could build yourselves.