In-House vs Managed Price Data: The Real 12-Month Cost

"Just build it in-house" sounds cheap until someone puts a number on it. So does "just hire an assistant to check it" and "just pay for a tool." Every option in competitor price monitoring looks affordable in isolation, because most cost comparisons only count the line item that's easy to see — the SaaS invoice or the contractor's day rate — and ignore the hours that quietly pile up around it.

This is a three-column, 12-month comparison of the three real paths retailers take: manual checking, an in-house build, and a managed feed. The costs are stated as assumptions you should adjust to your own numbers, not as universal truths — but the structure of where the cost hides is the same for every team we've seen run this comparison honestly.

The Assumptions, Stated Up Front

To keep this arithmetic honest, here's what's fixed across all three columns. Change any of these and the totals move — that's the point; the model is meant to be redone with your own numbers.

• Catalog: 2,000 SKUs tracked against 5 competitors, checked daily.
• Loaded cost of an in-house employee (salary + payroll tax + benefits): €60,000/year for a junior/mid analyst or engineer, €85,000/year for a senior data engineer. Adjust for your market — these are illustrative, not a market survey.
• Time horizon: 12 months, first-year cost including setup.
• What "done" means: daily price data per SKU, matched to your catalog, with less than 5% missing/stale rows on any given day.

Column 1 — Manual Checking

The visible cost: one merchandising assistant, roughly 30% of their time, at the junior loaded rate. 0.3 × €60,000 = €18,000/year in headcount.

The hidden cost: manual checking doesn't scale linearly with catalog size — it scales worse, because coverage degrades under time pressure long before the assistant's hours run out. At 2,000 SKUs × 5 competitors = 10,000 checks, even a fast 90 seconds per check is 250 working hours — over six working weeks of nothing but price-checking, which never happens in practice. What actually happens is what we documented in the real cost of manual price collection: coverage shrinks to whatever fits around other work, data goes stale between checks, and errors go uncaught because nothing flags them.

Column 1 total: ~€18,000/year in visible headcount cost, for a dataset that in practice covers a fraction of the catalog at unreliable freshness. The real cost isn't the salary line — it's the decisions made on the 70% of the catalog nobody had time to check.

Column 2 — In-House Build

The visible cost: most teams underestimate this to one engineer for a month. The realistic build for 2,000 SKUs across 5 competitor sites, including anti-bot handling and product matching, is closer to a senior engineer for 3 months of initial build (0.25 × €85,000 = €21,250), plus ongoing maintenance.

The maintenance line is the one that gets skipped in every in-house estimate we've seen. Competitor sites redesign, add anti-bot measures, or change their HTML structure — that's not a hypothetical risk, it's a certainty on any 12-month timeline, and every scraper break means checks silently fail until someone notices the data going stale. Budgeting even a conservative 20% of a mid-level engineer's time for ongoing fixes across 5 sites is 0.2 × €70,000 (blended junior/senior rate) = €14,000/year.

Column 2 total: ~€35,250 in year one (€21,250 build + €14,000 maintenance), and ~€14,000–20,000/year every year after — plus the opportunity cost of a senior engineer's time not spent on your product. This is the build vs. buy tradeoff most teams actually face: the build cost is one-time-ish, but the maintenance cost never goes away, and it's paid in your best engineering hours, not the cheapest ones.

Column 3 — Managed Feed

The visible cost: a managed data feed for 2,000 SKUs × 5 competitors, quoted rather than fixed-tier, typically lands in the low-to-mid four figures per month depending on refresh frequency and site difficulty — call it €2,000–4,000/month as a planning range, so €24,000–48,000/year.

What's not hidden, because there's nothing to hide: no engineer hours, no maintenance line, no assistant reallocating 30% of their week. The provider absorbs site-redesign breakage as part of the service, because that's the job. The internal cost is closer to a few hours a month reviewing the data and acting on it — not collecting it.

Column 3 total: ~€24,000–48,000/year, fully loaded, no hidden maintenance tail. More expensive than the manual column's visible number, cheaper than the in-house column's real number once maintenance is counted honestly — and the coverage and freshness are actually what was promised, not degraded under deadline pressure.

Side by Side

Where Each Column Actually Wins

Manual checking wins on paper cost alone at very small scale — under a few hundred SKUs and one or two competitors, where the true hourly commitment is genuinely light and the coverage gap doesn't hide much. Past that, the visible cost stops reflecting reality.

In-house builds make sense when price data is a genuine strategic differentiator you need deep control over, or when the engineering team already has slack capacity and the skill set. They rarely make sense as a cost-saving move — the maintenance tax means an in-house build is usually a control decision, not a cheaper one. Our in-depth build-vs-buy breakdown covers the technical side of this tradeoff.

Managed feeds win when the honest 12-month number matters more than the headline monthly invoice — which, once maintenance and opportunity cost are counted, is most mid-market retailers above a few hundred SKUs.

How ScrapeWise Fits

ScrapeWise is the managed-feed column: a scheduled data feed matched to your catalog, with site-breakage, anti-bot handling, and product matching absorbed into the service rather than landing on your team's calendar. Pricing is quoted against your SKU and competitor count rather than a flat tier, so the number in Column 3 above is a planning range, not a quote — the actual figure depends on refresh frequency and how difficult your competitor set is to monitor.

Honest limitations: ScrapeWise doesn't disappear the internal cost entirely — someone still needs to review the data and act on it, and repricing logic or deeper analysis sits outside the feed itself. For teams with genuine engineering capacity to spare and a strategic reason to own the stack, in-house remains a legitimate choice — just not usually a cheaper one once maintenance is counted honestly.

Conclusion

The manual column's €18,000 and the in-house column's €21,250 build cost both look cheaper than a managed feed until the hidden lines get added back in — degraded coverage on one side, a permanent maintenance tax on the other. Redo this model with your own headcount rates and catalog size before deciding; the structure of where the cost hides won't change, even if the numbers do.

Want the real 12-month number for your own catalog and competitor set? Book a call →