Delegate real work on the web — and stay in control
Hand your agent the whole web: it reaches any site, pulls any data, and signs into the accounts you connect, while you set the guardrails and approve what matters. And because repeated workflows replay from cache, your cost per run falls as you scale — instead of climbing with every run.
Browser infra for agents bills you to think twice
The unit changes — browser-hours, steps, gigabytes — but the economics rhyme: the LLM drives the page on every execution, so cost scales linearly with usage.
Run a 50-step authenticated workflow a thousand times and you’ve paid the model to re-read the same DOM and re-decide the same clicks a thousand times. Whether the bill says browser-hours (Browserbase, Steel), steps and tokens (Browser Use), or gigabytes (Bright Data), the curve points the same way: up and to the right, in lockstep with how much your agents actually work.
That’s backwards. The most valuable workflows are the repetitive, authenticated ones you run at volume — exactly the ones a per-run LLM bill punishes hardest. See the full breakdown on the comparison pages and the alternatives index.
Compile once, replay free
Twin moves the LLM to the planning stage, where it belongs — not onto every execution. Four mechanisms bend the curve down.
Compile once
A successful run compiles into a skill — a deterministic action plan over a token-efficient, numerically-indexed map of the page, not raw HTML. The expensive planning happens one time.
Match semantically
A re-phrased request (“book a demo” vs “schedule a call”) is matched to the compiled skill by a vector cache — not an exact selector key — so the next similar task hits the cache instead of cold-starting.
Replay free
On a cache hit, Twin replays the compiled skill deterministically with zero LLM calls. The cheapest model call is the one you never make.
Compound across tenants
A skill compiled once can be safely reused across tenants through a cross-tenant skill corpus, so the cache-hit rate climbs as the whole network runs — savings you didn’t have to earn alone.
Read the deep dive in Cutting LLM cost in browser automation or the mechanism on the how-it-works page.
Two cost curves, one decision
Illustrative shape, not a quote — the point is the direction. As the same workflow runs more often, a re-run-the-LLM model holds flat (or rises); Twin falls toward the replay floor.
Re-run the LLM every time
Cost per run is roughly constant — the model reads the page and decides the clicks again on every execution. 1,000 runs cost ~1,000× a single run. Volume is a liability.
Twin: compile once, then replay
Run #1 pays the cold compile. After that, semantic cache hits replay deterministically at ~$0 LLM, so the blended cost per run falls as usage grows — a cache hit is ~5× cheaper, and the marginal cost per run trends toward zero.
Numbers are illustrative to show the shape of the curve, not a benchmark or a guarantee. LLM cost is metered and passed through at 1× — see the live rate card at pricing.
Built for the teams that run the browser at volume
If you need an agent that does real web work you can hand off and still govern — accounts you authorize, steps you approve, a full audit trail — with a cost curve that falls as you scale, Twin is the layer.
- Teams building AI agents that need a browser they can trust to repeat.
- RPA-replacement products running authenticated, multi-step flows on a schedule.
- Anyone whose per-run model bill has become the line item that scales with success.