Twin Browser
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Comparison

Twin Browser vs ScrapingBee

ScrapingBee is one HTTP call for one rendered page, and per page it is cheaper than Twin. Pick Twin when one page is not the job — when the agent has to sign in, move through several steps, wait for an approval, and do it all again next week off a compiled skill.

Start freeWhy teams switch

Side by side

The spec table

ScrapingBee: A single-call web-scraping API — send a URL, get the rendered HTML or extracted data back, with proxy rotation and JavaScript rendering handled for you. Billed by API credits per request. Runs no LLM of its own.

Twin Browser compared with ScrapingBee, capability by capability.
CapabilityTwin BrowserScrapingBee
Billing unitUsage credits — flat action price, or metered run cost, whichever is higherAPI credits per request
Entry priceFree to start; usage credits from $29/moHobby $19/mo for 75,000 API credits
Simplicity for one pageA run is a goal plus a success conditionExcellent — one GET, rendered HTML back
Login / session across stepsYes — vault, persisted sessionsNo — one request, one page
Multi-step action (fill, click, submit)YesNot a stateful multi-step action layer
Semantic cache + deterministic replayYesNo
Human-in-the-loop handoffYesNo
Marginal cost curveFalls with usage (inverted)Flat — every page costs its credits again

A check marks a genuine strength on either side — including ScrapingBee’s; a dash marks only where a tool actually trails. The wedge is the bottom row: Twin’s marginal cost per run falls as usage grows.

Why teams pick Twin

Delegate the whole web — you set the guardrails.

ScrapingBee is a capable tool. Twin’s edge: you hand your agent any site and keep control of what it may touch — and, as it repeats work, three mechanisms make the marginal cost of the next run fall instead of rise.

Cost trends toward zero

Most browser infrastructure re-runs the LLM on every execution, so spend climbs with usage. Twin compiles a task once; repeats hit the cache and replay at ~$0 model cost.

Deterministic replay

A compiled skill blind-replays with no model in the loop — production-ready, not a debug recorder. The most-repeated workflows stop paying per run.

Cross-tenant skill corpus

Sanitized skill skeletons are pooled across the network, so your cache-hit rate climbs as everyone automates the same hosts.

In practice

One API call. Then the cache does the work.

Goal in, deterministic action out. The first run compiles a skill; the next re-phrased request matches it semantically and replays with no model in the loop.

run.shbash
# 1. Run a goal — Twin compiles the successful path into a skill
curl https://api.twin-browser.com/api/v1/run \
  -H "Authorization: Bearer $TWIN_KEY" \
  -d '{ "goal": "Export this month’s invoices as CSV",
        "url": "https://app.acme.com/billing" }'

# 2. A re-worded request vector-matches the same skill —
#    no model call, 2 credits instead of 10
curl https://api.twin-browser.com/api/v1/run \
  -H "Authorization: Bearer $TWIN_KEY" \
  -d '{ "goal": "Download the latest invoices",
        "url": "https://app.acme.com/billing" }'
app.acme.com/billing
  1. Vector-match request to compiled skilldone
  2. Adapt skill to new valuesdone
  3. Replay actions — zero LLM callsrunning
  4. Return invoices.csvqueued

A solved goal costs 10 credits. Once it is a compiled skill, a deterministic replay costs 1 and a semantic-cache hit on a re-worded request costs 2. A call is billed the higher of its flat action price or its metered cost — see the rate card.

Choose with eyes open

When to pick which

No tool wins every job. Here’s the honest split.

Pick Twin Browser when

  • The task spans several steps and at least one of them is behind a login.
  • You want the repeat of a workflow to cost less than the first attempt.
  • A 2FA or approval wall should park for a person rather than fail.

Pick ScrapingBee when

  • You need rendered HTML for a public page and nothing more.
  • The lowest possible price per page is the deciding factor.
  • You do not want to model a task at all — just fetch URLs.

Go deeper

Read the mechanics

The reason Twin’s cost curve inverts is the cache and the corpus. Here’s where each capability is explained — and where teams put it to work.

Cutting LLM cost

The guide to making cost fall with usage instead of rising.

Read it

Semantic dispatch cache

How re-phrased requests match a skill you already compiled.

Read it

Why teams switch

The narrative version of Twin vs ScrapingBee.

Read it

FAQ

Twin Browser vs ScrapingBee

ScrapingBee vs Twin Browser?
ScrapingBee is a per-request page-fetch API. Twin is a task execution layer: it plans and performs a multi-step, often authenticated, workflow and compiles the successful path into a skill it can replay with no model in the loop.

Hand over the work. Keep the guardrails.

Delegate the busywork, set the limits, and let repeated workflows compile into skills that replay at near-zero model cost. Free to start.

Start freeSee pricing