Kritt vs Taranify

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Kritt Agents	Taranify Agents
Tagline	AI-assisted code security research with parallel agents and verified findings	Mood-based entertainment recommender that picks your movies, music, and books from a 30-second color quiz.
Category	Agents	Agents
Pricing	Freemium· open-kritt: Free (self-hosted, AGPL-3.0) / Single managed scan: $5,000 (up to 200k LoC) / Enterprise: custom pricing for CI/CD integration and dedicated support	Free· 100% free, unlimited recommendations
Model	Codex and Claude Code (as agent harnesses)	Custom neural network (color-psychology)
Editorial score	—	6.8 / 10
Use cases	Pre-launch security audit of a new serviceContinuous vulnerability scanning in CI/CDBug bounty research on open-source targetsAttack-surface mapping for acquired codebasesVerified proof-of-concept generation for reported bugsCustom-rule appsec review at scaleTriage reduction for existing SAST pipelinesDeep-dive review of security-sensitive modules (auth, crypto, parsers)	movie-recommendationsmusic-discoverybook-recommendationsmood-matchinggroup-picks
Pros	Parallel-agent architecture covers large codebases faster than sequential AI review passes Verification stage attempts to confirm exploitability, cutting the LLM false-positive rate that plagues single-shot audits Open-source self-hosted build (AGPL-3.0) lets teams run it against private code with their own model keys Bring-your-own harness: works with Codex and Claude Code so you can pick the frontier model that fits your budget Custom severity rules and verification scripts let security teams encode their own threat model Fixed-price managed scan option ($5k up to 200k LoC) gives a predictable path for teams without in-house AI-sec expertise	Genuinely free with no login or tracking required Novel color-quiz UX that takes about 30 seconds Group mode reconciles multiple people's moods at once Covers movies, TV, music, books, and food in one place
Cons	Managed scan pricing is steep for indie developers or small startups compared to traditional SAST tools Self-hosting still requires paying for the underlying frontier model API usage, which can be substantial on large repos AGPL-3.0 licensing on the open-source build is incompatible with some proprietary product integrations No standalone REST API documented publicly; automation lives at the CLI/workflow layer Effectiveness is bounded by the underlying LLMs' understanding of the language and framework; niche stacks may see weaker coverage Newer entrant relative to established SAST/DAST vendors, so tooling maturity and integrations are still evolving	Consumer-only: no API, no developer hooks "Custom neural network" claims are not independently verifiable Recommendation quality hinges on a fuzzy color-to-mood mapping Limited to TMDB/Spotify/Netflix catalogs
Website	kritt.ai	taranify.com

Pick Kritt if

✅ Parallel-agent architecture covers large codebases faster than sequential AI review passes
✅ Verification stage attempts to confirm exploitability, cutting the LLM false-positive rate that plagues single-shot audits
✅ Open-source self-hosted build (AGPL-3.0) lets teams run it against private code with their own model keys
✅ Bring-your-own harness: works with Codex and Claude Code so you can pick the frontier model that fits your budget

Pick Taranify if

✅ Genuinely free with no login or tracking required
✅ Novel color-quiz UX that takes about 30 seconds
✅ Group mode reconciles multiple people's moods at once
✅ Covers movies, TV, music, books, and food in one place

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