ev3ry vs the scraping
& browser-agent market.
<Ev3ry/> occupies a real white space: chat-authored, then LLM-free, schedulable, self-hostable extraction. No single competitor sits in that exact box — and here’s the honest map of who’s near it, and where they’re ahead.
Snapshot 2026-06-22; vendor pricing and limits change monthly. Credit definitions differ per vendor and aren’t directly comparable — verify on each vendor’s own page.
Spend the model once. Replay forever for free.
Two camps usually stay separate: AI agents that re-run the LLM on every execution, and no-code recorders that replay deterministically but can’t author by chat. ev3ry combines the AI authoring UX with no-code-grade replay economics.
Author with the agent
The LLM is spent once, at authoring time, in chat — vs Browser Use / Skyvern / Stagehand, which re-burn the model on every run.
Replay with no LLM
Scheduled runs replay the recipe over the browser with zero model calls — so a nightly sweep costs browser-seconds, not tokens.
Drift, handled honestly
When a page changes, ev3ry catches the drift, flags the run, and the agent drafts an updated Playbook for you to approve. Not auto-magic repair — a fix you sign off on, instead of re-recording clicks (Browse AI) or re-burning the model every run (AI agents).
Six platforms, the axes that decide the cost.
Paradigm, whether replay is LLM-free, authoring, scheduling, output, self-host, pricing and drift robustness. The “is replay LLM-free?” row is the one most worth scrutinizing.
| Dimension | ev3ry | Firecrawl | Apify | Browse AI | Skyvern | Browser Use |
|---|---|---|---|---|---|---|
| Paradigm | Chat AI-agent → declarative recipe | API-first web-data layer for AI | Marketplace of “Actor” scrapers | No-code point-and-click recorder | Vision + LLM browser agent | OSS AI agent framework + cloud |
| Replay is LLM-free? | Yes — zero model calls on replay | No — LLM runs each /extract | Deterministic per Actorunless the Actor uses an LLM | Yes — deterministic replay | No — re-reasons every run | No — LLM drives every run |
| Chat-agent authoring | Yes — the agent writes the recipe | NL prompt per request | Code / pre-built Actors | Manual click-recording | NL + no-code builder | NL task per run |
| Scheduling | cron / once / loops + inbound triggerswebhook · Slack · Discord · Teams | Monitor endpoint; no rich scheduler | Yes — cron scheduler | Yes — monitor 5-min → hourly | Yes — scheduled extraction | Cloud scheduling |
| Structured output | Versioned DataSchema (JSON Schema) | JSON via NL / schema prompt | Dataset export (JSON/CSV/Excel) | Rows → Sheets / Airtable / API | JSON / CSV | JSON via output model |
| Self-host | Yes — free, full engine | OSS — but no stealth/proxy in OSScloud-only anti-bot | No (cloud-only) | No (cloud-only) | Yes — AGPL-3.0, Docker | Yes — OSS framework |
| Pricing / metering | Multi-meter creditsbrowser-sec + proxy + LLM markup | Per-page credits | Compute units + proxy GB | Credits (~10 rows each) | Credits + usage | ~$0.02/hr + $5/GB + LLMour pricing anchor |
| Drift robustness | Detects drift; agent drafts fix to approvehuman-in-the-loop, not auto | Re-reasons (LLM each call) | Code-maintained Actors | Breaks; re-record by hand | Re-reasons every run | Re-reasons every run |
Bright Data, Zyte, Octoparse, ScrapingBee, Diffbot, Stagehand / Browserbase and AgentQL are in the full research but omitted here for width. Browse AI’s and ev3ry’s replay are both LLM-free; the difference is who authors — a person clicking, or the chat agent.
Where we’re differentiated — and where we’re behind.
The wedge is real, but so are the gaps. Both, straight.
Genuinely differentiated
- Author once with the LLM, replay forever LLM-free. Almost every AI competitor (Firecrawl /extract, Skyvern, Browser Use, Stagehand) spends tokens on every run; every LLM-free recorder (Browse AI, Octoparse) needs manual authoring. ev3ry is the rare combination — chat-agent authoring and deterministic, model-free, schedulable replay. For high-frequency scheduled jobs that is a structural cost advantage no agent framework matches.
- Free, genuine self-host of the full engine. Self-host meters nothing because ev3ry hosts nothing. Of the set, only Skyvern (AGPL, LLM-driven), Browser Use (framework) and a crippled Firecrawl OSS (no stealth/proxy) self-host at all. A complete, free, BYO self-host of the whole platform is a real differentiator.
- Project-scoped Skills + versioned Schemas. A reusable, shared “how to do X for this project” memory the agent authors, plus first-class versioned output schemas. Competitors have output schemas, but not a project-scoped shared agent-authored skill layer — the ChatGPT/Claude-Projects mental model, genuinely novel here.
- Multi-meter credits that reward the architecture. Browser-seconds + proxy + LLM fold into one credit balance pooled across projects. And because replay is LLM-free, scheduled runs largely drop the LLM meter entirely — the pricing model rewards the design.
- Inbound triggers across webhook / Slack / Discord / Teams. Signature-verified inbound events that wake an LLM-free replay. Native multi-platform inbound triggers feeding a deterministic scraper run are uncommon in this set, and pair naturally with the replay story.
Where competitors lead
- Brand, scale, and proof. Firecrawl (130k+ stars), Browser Use (top of the WebVoyager leaderboards), Skyvern (~20k stars), Browserbase, and the enterprise incumbents (Bright Data, Apify, Zyte) have years of deployments, community and logos. We have no public benchmark numbers and no community yet — the biggest gap.
- Pre-built scrapers and ready datasets. Apify's Actor marketplace, Octoparse's 500+ templates and Bright Data's pre-collected datasets give instant time-to-value. Our agent authors every Playbook from scratch — higher activation cost, nothing to buy off the shelf.
- Anti-bot / proxy maturity. Bright Data's proxy + unblocker network is the industry benchmark; Browserbase, Zyte, Apify and Skyvern have captcha/stealth/proxy stacks battle-tested at scale. Our Camoufox stealth is promising but unproven against the hardest targets, and lelantos is still maturing.
- Integration breadth. Browse AI (7,000+ via Zapier), Apify and Octoparse have large connector sets. Our connector surface is comparatively early.
- Robustness vs. UI drift — the flip side of our wedge. An LLM-driven agent (Skyvern, Browser Use) re-reasons when a page changes; a frozen Playbook can break and needs re-authoring. We detect drift and the agent drafts a fix for you to approve — but a deterministic recipe is inherently more brittle to layout change. We frame this as a cost/robustness tradeoff, not a free lunch.
The nearest single competitor is Skyvern (agentic + self-host) — but its replay is LLM-driven and it’s AGPL. Browse AI is nearest on LLM-free deterministic replay — but has no chat agent and no self-host. The box we sit in is empty.
Author a scraper by chatting.
Drop a URL, describe the data, and let <Ev3ry/> write the Playbook — then schedule it to replay with no LLM in the loop. 500 free credits, no card.