Results from the 1,000-URL Firecrawl Dataset Benchmark

Executive Summary

In our 1,000-URL benchmark based on the Firecrawl scrape-content dataset, fastCRW reached:

• 92.0% coverage
• 833ms average latency
• 446ms p50 latency
• 88.5% noise rejection

That is the benchmark cluster behind the main proof statements on the site.

The value of this page is not a single headline number. It is that the numbers come from a named dataset and are accompanied by interpretation rules instead of being thrown into marketing copy without context.

Test Environment and Framing

This page summarizes the internal benchmark report used to support fastCRW cloud and self-host evaluation. The point is not to claim universal dominance. The point is to show why fastCRW is a credible choice for:

• Firecrawl replacement evaluations,
• AI-agent scraping workloads,
• and teams that care about low-overhead deployment.

Results Table

Internal benchmark results

Metric	fastCRW	Firecrawl v2.5
Coverage	92.0%	77.2%
Average latency	833ms	4,600ms
P50 latency	446ms	n/a
Noise rejection	88.5%	expressed differently in public data
Idle RAM framing	6.6MB	450MB to 500MB+

Read the latency and RAM rows as benchmark and deployment framing, not as a guarantee for every site or every infrastructure shape.

Interpretation

The benchmark supports three strong product claims:

1. fastCRW is a credible our deep Firecrawl comparison.
2. fastCRW is materially stronger on operational footprint.
3. fastCRW is well-positioned for AI-agent scraping because faster responses and lower overhead improve iteration speed.

Why These Metrics Matter Together

Coverage alone is not enough. A crawler that succeeds more often but is dramatically slower or heavier can still be the wrong operational choice for some teams.

Likewise, latency alone is not enough. A fast system that fails on too many targets is not useful either.

That is why this page keeps coverage, latency, and runtime weight together. The point is to show the tradeoff cluster, not cherry-pick one flattering metric.

What This Benchmark Does Not Prove

This benchmark does not prove that fastCRW wins every category against every crawler.

It also does not erase the fact that some competing products may still lead on:

• edge-case site coverage,
• bundled feature surface,
• or product maturity in adjacent workflows.

That is why this page should be read together with the methodology page and the comparison pages under alternatives.

Recommended Use of This Page

Use this benchmark as:

• a starting point for evaluation,
• a source of concrete metrics to compare against your own workload,
• and a way to understand the claims on the marketing pages.

Do not use it as proof that you can skip testing your own target sites.