ScrapingBee vs Bright Data (2026): Which Scraping Service — and a Third Option

By the fastCRW team · Comparison · We make fastCRW; both ScrapingBee and Bright Data are described as fairly as we can. Verify pricing independently.

The quick verdict

ScrapingBee and Bright Data both solve "get data off hard websites," but at different scales and price points. ScrapingBee is a developer-friendly scraping API: send a URL, get HTML, proxies and headless Chrome handled — simpler, mid-market, and now part of Oxylabs (as of early 2026). Bright Data is an enterprise proxy giant with one of the largest residential IP pools, scraper builders, datasets, and compliance machinery — heavier, sales-led, upmarket. This post compares them honestly, then explains where a third option — an open-core, self-hostable web-data API — fits for AI/RAG teams.

Side by side

ScrapingBee vs Bright Data: the real differences

1. Scale and proxy depth

Bright Data's defining asset is the size and sophistication of its proxy network — for large-scale scraping of heavily anti-bot-protected targets, that depth is a genuine moat. ScrapingBee, especially post-Oxylabs, also has strong proxy backing, but it is positioned as an API for developers rather than a proxy-infrastructure giant. If your job is "millions of requests against hostile sites," Bright Data is built for that. If it is "reliable general scraping without managing proxies," ScrapingBee is the simpler fit.

2. Pricing shape

As of early 2026, ScrapingBee uses a credit model with multipliers — JS rendering and premium/stealth proxies cost several times more per request, and JS render may bill by default. Bright Data uses per-GB / per-request pricing with enterprise minimums and a sales-led motion. We are deliberately not quoting hard dollar figures (both move); the durable point: ScrapingBee's cost surprise is the multiplier math; Bright Data's is enterprise commitment + per-volume opacity. Neither is flat.

3. Ease and buying motion

ScrapingBee is self-serve and quick to integrate. Bright Data is more of a platform with a sales process, scraper builders, and compliance review — more capable, more overhead. For a small team that wants to ship this week, ScrapingBee is lower friction; for an enterprise data program, Bright Data's depth and contracts may be worth the process.

4. Independence

ScrapingBee is now part of the Oxylabs group (as of early 2026) — its roadmap is tied to a proxy vendor's strategy. Bright Data is an independent proxy giant. If post-acquisition roadmap independence matters to you, that is a real consideration for ScrapingBee.

The third option: where fastCRW fits

Both ScrapingBee and Bright Data are cloud-only, raw-output-oriented, and metered with non-flat pricing. For AI/RAG and agent teams, there is a different profile that matters:

• LLM-ready output by default. fastCRW returns clean markdown / schema JSON; no HTML-cleanup stage before RAG.
• Flat, predictable pricing. 1 credit = 1 page on every renderer, and no per-GB enterprise minimums.
• Free self-host + data locality. AGPL-3.0, ~6 MB Rust binary, unlimited self-host, data never leaves your infra — neither ScrapingBee nor Bright Data offers this.
• Crawl/map/search built in and a built-in MCP server for agents.
• Lower-latency, local-first — no browser stack on the hot path; see the public benchmark at /benchmarks.

Honesty note: fastCRW Cloud's free tier is a one-time lifetime 500 credits (not monthly). The distinctive free path is unlimited free self-host.

Decision guide

Why the middle gets squeezed

There is a structural reason the ScrapingBee-vs-Bright-Data choice is unstable for many teams: the workload itself is bimodal. Most targets are easy (open content) and a small minority are genuinely hostile. ScrapingBee positions in the middle — more than a DIY script, less than a proxy giant. But a bimodal workload does not want a middle tool; it wants a cheap/fast option for the easy bulk and a heavy option for the hard tail. Paying mid-market scraping-API rates for documentation sites is overpaying for the easy 90%, while the mid tier may still not be enough for the hardest 10%. The middle is exactly the position a split architecture erodes from both ends.

The split-architecture pattern in practice

Concretely, the resilient design is: a fast, flat-priced, self-hosted engine as the default path, with its own residential-proxy egress tier auto-escalating for the domains that need it. fastCRW fills that slot well because it is self-hostable (zero per-request cost on the bulk, data stays local) and its renderer ladder steps up through a residential-proxy egress tier for hardened domains automatically. The result: proxy spend tracks only the hostile fraction, the easy bulk is essentially free, and downstream code sees one clean-markdown interface regardless of path. Whether ScrapingBee's middle ground survives in your stack usually comes down to whether your workload is actually uniform (middle tool fine) or bimodal (split wins) — most AI scraping workloads are bimodal.

Decision factors beyond the feature grid

Three non-feature factors decide this more often than the table does. Procurement: Bright Data is sales-led with contracts and minimums; ScrapingBee and fastCRW are self-serve — a two-person team that cannot start without a sales cycle will not pick the contract tool however capable it is. Output shape: if the data feeds LLMs, raw HTML from either incumbent means you own a cleanup pipeline forever, while a content-extraction-native API removes that stage. Exit path: both incumbents are closed and cloud-only, so a pricing or roadmap change has no escape; an open-core engine you can self-host is the only option here that lets you leave without a rewrite. Weigh those alongside proxy depth, not after it.

Getting started with the third option

docker run -p 3000:3000 ghcr.io/us/crw:latest

Free self-host (AGPL-3.0) or fastCRW Cloud (one-time 500 free credits, no card). GitHub.

Further reading

• ScrapingBee vs fastCRW
• Bright Data vs fastCRW
• Bright Data alternatives