"Market data is just scraping exchanges — why pay for it?" That is the usual starting point for a build-your-own plan. The problem is that scraping accounts for a small fraction of the total work, and the rest consumes engineering capacity indefinitely.
This article works through a three-year total cost of ownership comparison using a transparent set of assumptions.
The real cost structure of building it yourself
Self-built cost is easy to underestimate because only the first line item is visible. The full picture has five components:
| Cost component | Description | Commonly underestimated? |
|---|---|---|
| Initial development | Collection, parsing, storage, query API | No — usually the first thing estimated |
| Ongoing maintenance | Source changes, field drift, incident fixes | Severely |
| Infrastructure | Servers, storage, bandwidth, monitoring | Moderately |
| Data quality | Reconciliation, backfill, anomaly detection | Severely |
| Compliance and licensing | Source legality, permitted usage | Most often ignored |
Ongoing maintenance and data quality are the real sinkholes. The reason: upstream changes are not a one-off event, they are continuous. Exchanges adjust fields, sites get redesigned, endpoints add validation, rate limit policies shift — and each one needs a human response.
Assumptions
To make the comparison meaningful, here are the assumptions stated plainly. Substitute your own:
| Parameter | Value | Note |
|---|---|---|
| Fully-loaded engineer cost | $12,000 / person-month | Salary, benefits, equipment, management overhead |
| Initial build | 2 engineers × 6 months | Multi-market collection, storage, query API |
| Ongoing maintenance | 0.5 person-month / month | Source changes, incident fixes, operations |
| Infrastructure | $1,500 / month | Collector nodes, storage, bandwidth, monitoring |
| Commercial plan | $299 / month | All-market live tier |
| Integration effort | 0.5 person-month | One-off, wiring up the commercial API |
Three-year TCO comparison
Option A: build in-house
| Component | Calculation | 3-year total |
|---|---|---|
| Initial build | 2 × 6 months × $12,000 | $144,000 |
| Ongoing maintenance | 0.5 × $12,000 × 36 months | $216,000 |
| Infrastructure | $1,500 × 36 months | $54,000 |
| Total | $414,000 |
Option B: buy an API
| Component | Calculation | 3-year total |
|---|---|---|
| Subscription | $299 × 36 months | $10,764 |
| Integration effort | 0.5 × $12,000 | $6,000 |
| Total | $16,764 |
The three-year gap is roughly $397,000. Even if you halve every self-build assumption — cutting the initial build to 1 engineer for 3 months and maintenance to 0.25 person-months — building in-house still costs more than ten times as much.
One caveat: this compares cost only, not time. Building in-house means your team spends six months not shipping product features, and the opportunity cost of that window can exceed the subscription savings.
When building in-house genuinely makes sense
Building is not always wrong. These cases can justify it:
- You need data no commercial API offers — depth for a niche market, non-standard sources, or an exclusive partnership. This is the strongest reason to build.
- Unit economics at extreme scale — if your volume already sits in the top commercial pricing band, your requirements are highly stable, and you have spare engineering capacity, self-built unit cost can be lower.
- Data is your core asset — if the data itself is your product's differentiation rather than infrastructure, building has strategic value.
- Extreme latency requirements — co-located exchange connectivity with a microsecond latency budget.
Notice what these share: none of them is about saving money. They are about commercial options being unable to do the job. If cost saving is the only reason to build, the math rarely works.
When buying genuinely makes sense
- Early product and validation stage. Validate the workflow on a free tier at zero cost, then pay once demand is proven.
- Multi-market coverage needed. One API covering equities, FX, crypto, futures, indices, and commodities — building means tackling each market separately.
- Small team. No spare capacity to maintain a dedicated data pipeline team.
- SLA and compliance guarantees needed. A 99.99% availability commitment, clear licensing scope, and auditable service records.
- Time to market matters more than cost. If a competitor ships first, those six months spent on a collection pipeline are six months not spent on the product.
The overlooked hidden cost: compliance
This is routinely missing from cost comparisons and carries the highest risk. Self-built collection runs into three common problems:
- Source licensing. Publicly accessible is not the same as licensed for commercial use. The licensing boundary for scraping public pages into a commercial product is often unclear.
- Usage restrictions. Some sources prohibit redistribution or use in derived data products.
- Terms of service conflicts. Circumventing rate limits, CAPTCHAs, or anti-bot measures can breach terms directly.
A commercial API resolves these up front — data licensing, permitted usage, and redistribution rules are all explicit in the terms of service. For products sold to enterprise customers, this often matters more than cost.
The pragmatic conclusion: buy first, build only if forced
For most teams the sensible path is:
- Start on a commercial API. Validate on the free tier, run production on a paid tier.
- Spend engineering on the product. Data ingestion is infrastructure, not differentiation.
- Build only where commercial options genuinely cannot deliver — and only that piece, keeping everything else on the API.
- Hybrid is usually optimal. Build your exclusive data sources; buy general market coverage.
In short: buy time with a commercial API, and spend that time where differentiation actually happens.
FAQ
Is building really that much more expensive?
At most scales, yes. The dominant cost is not initial development but ongoing maintenance and data quality assurance — upstream changes never stop and require permanent staffing. Under the assumptions here, the three-year gap is about $397,000.
When is building in-house justified?
When a commercial API cannot provide the data you need — niche markets, non-standard sources, or exclusive channels. If cost saving is the only motive, the math usually does not work out.
What are the risks of scraping public pages?
Publicly accessible does not mean licensed for commercial use. The main risks are unclear licensing boundaries, prohibitions on redistribution, and terms of service violations from circumventing rate limits or anti-bot measures. Products sold to enterprises need a clear licensing chain.
Can I build first and switch to a commercial API later?
Technically yes, but the migration is not cheap — collection, storage, and query layers all need rework. The more common and safer sequence is to launch quickly on a commercial API and build only once a data source is confirmed as core differentiation.
How should a hybrid split be drawn?
By substitutability: use a commercial API for general market coverage (equities, FX, crypto) and build only genuinely exclusive sources. That preserves differentiation while avoiding duplicate maintenance of commodity pipelines.
Do open-source collectors remove the build cost?
They reduce the initial build, not the ongoing cost. Source drift, data quality, and compliance responsibility still sit with you — and you now also maintain an unfamiliar codebase. Treat them as a head start on option A, not as a substitute for option B.
Next steps
List all five self-build cost components first, then compare against a commercial option — the conclusion is usually obvious. For finer comparisons see crawler versus commercial API and free versus paid market data. If your interest is validating on a free tier before upgrading, the pricing page lays out market coverage by tier. If you do build, the pipeline design is covered in the market data database guide, and multi-market coverage in global equities. If the numbers still look close, the honest answer is that your requirements are genuinely unusual — and that is exactly when building starts to make sense.