When you shop for a ready-made scraper on the Apify Store, you'll often find one tool charging $0.50 per 1,000 results and another charging $10 for the same website. It's tempting to assume the expensive one is better built, or at least breaks less often.
I tested that assumption on public data and found no support for it.
The data
Apify's public store API reports, for every listed tool, its list price and the outcome of every public run in the last 30 days. ScraperWatch downloads this every day. This analysis uses the 2026-10-19 snapshot (open JSON, CC BY 4.0).
I kept only established tools with 300+ runs and 20+ users in the last 30 days and a per-result price. That leaves 1,568 scrapers. "Success" means succeeded runs รท all runs. "Price" means the list price per 1,000 results on Apify's free plan (methodology).
Finding 1: price tells you almost nothing about reliability
The Spearman rank correlation between price and 30-day success rate is 0.023, which is effectively zero. Grouping the tools by price shows the same thing:
| Price per 1k results | Tools | Median success | Share failing >10% of runs | Users (30d) |
|---|---|---|---|---|
| under $1 | 386 | 99.19% | 8.5% | 142,064 |
| $1 โ $2 | 270 | 99.29% | 9.6% | 90,773 |
| $2 โ $5 | 494 | 99.24% | 9.1% | 463,330 |
| $5 โ $10 | 235 | 99.60% | 8.1% | 95,307 |
| $10 and up | 183 | 99.14% | 12.0% | 48,610 |
The median tool in every price band succeeds about 99% of the time. The share of tools failing more than 1 run in 10 stays between 8% and 10% up to $10/1k. Above that it rises to 12%. The priciest tier is the least predictable, not the most.
Users seem to have noticed. 83% of the monthly users of these 1,568 tools run one priced under $5 per 1,000 results.
Finding 2: within a single website, reliable tools still vary 16โ33x in price
Price vs. reliability across all websites could be skewed by mixing easy targets with hard ones. So I also looked within individual websites, keeping only tools with at least 95% success. The table below shows the 10th to 90th percentile price range. I didn't use min/max because several tools list $0.01 prices that probably aren't comparable.
| Website | Reliable tools | p10 | Median | p90 | p90 / p10 |
|---|---|---|---|---|---|
| LinkedIn jobs & companies | 119 | $0.50 | $4.80 | $8.00 | 16x |
| Amazon | 26 | $0.60 | $2.00 | $20.00 | 33x |
| 132 | $0.40 | $2.40 | $10.00 | 25x | |
| TikTok | 104 | $0.30 | $2.00 | $6.25 | 21x |
Even after removing the outliers, two scrapers for the same site with similar track records can differ in price by more than an order of magnitude.
The most popular tools also don't tend to be the most expensive. The most-used LinkedIn jobs scraper (16,358 users last month, 98.7% success) lists at $2 per 1,000 results, below the $4.80 median for reliable LinkedIn tools. The most-used Instagram scraper (53,856 users, 99.6% success) lists at $2.70, close to the $2.40 median.
Caveats
- A "result" isn't the same unit everywhere. A Google Maps place with 40 fields and a single review are both "one result". Some expensive tools return richer records, and this data can't measure that.
- A failed run isn't always the tool's fault. Bad input causes failures too, so small differences in success rate are noise.
- List price only. These are free-plan prices. Some tools also charge per run or for add-ons, which aren't included here.
- Success โ data quality. A run that "succeeds" can still return incomplete data. Spot-check the output before you commit to a tool.
What I'd take from this
- Don't use price as a proxy for quality. Across 1,568 tools, the two are essentially unrelated.
- Filter on usage and success first, then sort by price. A tool with thousands of monthly users and 98%+ success has already been tested by other people, whatever its price.
- Compare against the median for the site. If a tool costs 3โ4x the site median, check what extra fields you get before you pay for them.
Per-site tables with users, success rate and price for every tool are on scraperwatch.com, for example LinkedIn jobs. The analysis is about 80 lines of standard-library Python over the open JSON, and you can reproduce every number above from it.
Written with AI assistance. Every number comes from code over public data.
