Most companies sign a web scraping Service Level Agreement (SLA) that looks solid on paper but fails the moment data quality drops or pipelines break.
At ScrapeHero, a leading web scraping service, we have worked with enterprise programs across e-commerce pricing, availability tracking, Minimum Advertised Price (MAP) monitoring, and competitive intelligence. We have identified one pattern that repeats: unclear KPIs shift all the risk to the buyer.
A strong SLA is not about promises. It is about measurable protection.
Below are the KPIs that actually matter, along with example benchmarks that hold vendors accountable.
1. Data Quality KPIs (The Most Important)
In real-world scraping engagements, data quality failures cause more damage than downtime.
Your SLA should explicitly define the following metrics:
Accuracy
Accuracy measures the percentage of correctly extracted values for critical fields such as price, stock status, SKU, and seller.
Example benchmark: Greater than or equal to 99.5% accuracy on critical attributes.
Completeness
Completeness measures the percentage of target URLs, listings, or SKUs successfully captured per run.
Example benchmark: Greater than or equal to 98% coverage of the defined scope.
Duplication Rate
Duplicate records inflate datasets and corrupt analytics.
Example benchmark: Less than or equal to 1–2% duplication.
If these KPIs are missing, you are paying for volume, not usable data.
2. Service Reliability KPIs
Reliability extends beyond job completion status.
Key metrics include:
Run Success Rate and Uptime
This metric measures the percentage of scheduled runs completed as planned.
Example benchmark: Greater than or equal to 99.9% for production pipelines.
Incident Resolution Time
This metric measures the time required to detect, respond to, and fix failures.
Example benchmarks:
- Critical issues: Less than or equal to 4 hours
- Non-critical issues: Less than or equal to 24 hours
3. Data Timeliness KPIs
Late data is often worse than missing data.
Your SLA should define:
End-to-End Latency
This metric measures the time between a source update and the delivery of data.
Example benchmark: Less than or equal to 60 minutes for high-frequency datasets.
4. Operational Integrity and Compliance KPIs
Finally, protect against hidden risks by ensuring:
- Adherence to pre-agreed scraping rates
- Zero collection of Personally Identifiable Information (PII), with no exceptions
- Documented change management for logic or scope updates
