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Outcrawl vs RELAI

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Outcrawl compared with RELAI
DetailOutcrawl logo OutcrawlRELAI logo RELAI
What it doesWeb infrastructure for AI agents The continual learning engine for AI agents
StageNot given Not given
FoundedNot given Not given
Based inUS Not given
Pricing modelNot given Freemium
PricingFree: 1,000 credits/month, no card; Builder: $49/month for 10,000 credits; Pro: $199/month for 50,000 credits; Scale: $799/month for 250,000 credits. Enterprise with custom terms available. Credits cost approximately $0.0032-$0.0049 per page, $3.20-$4.90 per 1,000 pages, or about $0.19-$0.29/hour of RELAI Free: $0. RELAI Enterprise: Custom pricing.
Key features
  • Scrape: Convert any web page into clean markdown or structured JSON
  • Crawl: Fetch every page on a site from a single link and receive results as they are ready
  • Search: Query the web with top results already read and parsed
  • Browsers: Real browser instances in the cloud controlled via Playwright, Puppeteer or SDK
  • Agents: Autonomous task execution in a real browser with plain English descriptions
  • Session recordings: Every browser session is recorded for debugging and verification
  • Monitor: Watch pages on a schedule and receive notifications when content changes
  • Saved logins: Store credentials securely for agent runs requiring authentication
  • MCP integration: Connect to Claude Code, Codex or Cursor with one command
  • Real browser handling: Sites treat agents as humans so pages load and sessions persist
  • Capture signals from code, requirements, and production traces
  • Build and maintain EvalsWiki as versioned behavioral knowledge
  • Find missing eval coverage and unclear expectations
  • Generate complete evals with scenarios, environments, and evaluators
  • Optimize harness across prompts, tools, workflows, and code
  • Multi-objective optimization for quality, cost, latency, and KPIs
  • Model selection, migration, and routing with evaluation
  • Regression-controlled improvement with evaluation evidence
  • Versioned storage in repository with pull requests
What makes it differentNot given
  • Turns failures into replayable learning environments
  • Prevents regressions by keeping previous evals in the loop
  • Integrates with existing observability stacks and traces
  • Preserves business intelligence versioned in repository
  • Adapts harness to model changes without redefining use cases
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)24 +9 (up) since last check23 no change since last check
Trust Flow (Majestic)913
Citation Flow (Majestic)3125
Referring domains (Majestic)6079

3 details filled in for both companies.

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