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Qualifire vs DidWork

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

Qualifire compared with DidWork
DetailQualifire logo QualifireDidWork logo DidWork
What it doesReal-time guardrails and evaluation for LLM applications Independent verification for AI agent outcomes
StageNot given Startup
FoundedNot given Not given
Based inNot given Not given
Pricing modelFreemium Freemium
PricingFree plan includes 3 million tokens per month, unlimited users, and community support. Basic plan is $0/month with 300k monthly tokens and up to 3 protection rules. Pro plan is $550/month including 1 billion monthly tokens, unlimited protection rules, and email and Slack support. Additional tokens a Hobbyist: free forever with 250 checks/month; Pro: $9/month with 2,500 checks/month, $0.01 per extra check
Key features
  • Real-time guardrails with sub-20ms latency
  • Hallucination detection and prevention
  • Small language model judges tailored to business domain
  • Content moderation and safety checks
  • PII detection and masking
  • Prompt management and studio
  • Complete observability and tracing
  • Rogue red team platform for agent evaluation
  • Multiple deployment options
  • Policy enforcement and custom rules
  • Independent outcome verification with VERIFIED, FAILED, or UNKNOWN status
  • First-party adapters for Stripe, GitHub, Linear, Sentry, Resend, and HTTP endpoints
  • MCP integration with Claude Code, Cursor, and any MCP host
  • GitHub Actions required check that fails on unverified claims
  • Audit trail storing claims, evidence, and verdicts separately
  • Capability inspection before agents call tools
  • Read-only credential access with AES-256-GCM encryption
  • Continuous monitoring with watches every 5-10 minutes on Pro plan
  • Evidence retention for 7 days on Hobbyist, 1 year on Pro
What makes it different
  • 99.6% faster evaluation than alternatives
  • 97% cost reduction compared to traditional solutions
  • Small judges providing low latency and cost efficiency
  • Contextual guardrails that consider application context
  • Purpose-built for agentic systems, RAG, and chatbots
  • Refuses to guess: returns UNKNOWN rather than false positives when evidence is unreachable
  • Neutral gate between agent execution and success verification
  • Runs the same way in production as in development
  • Never needs write access, only reads authoritative state
  • Structured audit trail for debugging and agent evaluation
Community votes00
DR (Domain Rating by Ahrefs)28 no change since last check2 no change since last check
Trust Flow (Majestic)111
Citation Flow (Majestic)2623
Referring domains (Majestic)7114

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