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WBX Sandbox vs RELAI

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

WBX Sandbox compared with RELAI
Detail WBX SandboxRELAI logo RELAI
What it doesSecure execution for AI agents in isolated Firecracker microVMs or WebAssembly The continual learning engine for AI agents
StageNot given Not given
FoundedNot given Not given
Based inCZ Not given
Pricing modelNot given Freemium
PricingFree plan: no card required, 5 concurrent sandboxes, up to 1 hour per sandbox, one-time welcome credit. Pro: $49.00/month (excl. VAT) with $25.00 usage credit, 25 concurrent sandboxes, up to 24 hours per sandbox, 100 vCPU/100 GB RAM total. Team: $119.00/month (excl. VAT) with $50.00 usage credit, 10 RELAI Free: $0. RELAI Enterprise: Custom pricing.
Key features
  • Isolated Firecracker microVMs with own kernel and filesystem
  • Background jobs that survive disconnects
  • Batch execution with up to 1,000 jobs and dependency passing
  • WebAssembly jobs starting in milliseconds without VM boot
  • Budget Guard with cost reserves and limits
  • Execution receipts with runtime, isolation, resources and file hashes
  • Native MCP server integration for ChatGPT and Claude
  • Compact log digests instead of full 5,000-line logs
  • Server-side job waiting with signed webhooks
  • Verified package cache for PyPI, npm, Go and Rust
  • 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 different
  • Two runtimes with honest isolation labeling
  • 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)2 no change since last check23 no change since last check
Trust Flow (Majestic)013
Citation Flow (Majestic)025
Referring domains (Majestic)079

4 details filled in for both companies.

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