Million
Open-source tools and benchmarks to verify AI-generated code before it ships.
Million is an ambitious bet on a real problem: verifying AI-generated code before it ships. The team's open-source track record is solid, and the investor list is impressive. But with no public product or pricing, it's only for teams willing to engage directly and wait for something to use. If you need a verification solution today, consider alternatives like Cody by Sourcegraph or internal CI checks.
Verified 4d ago · liveness 55/100 · cite: rightaichoice.com/tools/million
- Engineering teams running AI coding agents in production who need a verification layer
- Agent infrastructure builders looking for benchmarking and training resources
- Companies with high correctness requirements for AI-generated code
- Teams already using React Scan or React Doctor who trust the team's open-source work
- Individual developers seeking a free code generation tool
- Teams needing a ready-to-use verification product today
- Users looking for hosted model APIs or pre-built agent frameworks
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Skip Million if you need a working verification product today—there is no public tool, API, or pricing yet; it's a mission statement and a contact email.
Pricing is not published; it is contact-sales only. As an early-stage startup, expect custom enterprise pricing that may be lower than established competitors but with more uncertainty.
In short
Million — Open-source tools and benchmarks to verify AI-generated code before it ships. Best for Engineering teams running AI coding agents in production who need a verification layer, Agent infrastructure builders looking for benchmarking and training resources, Companies with high correctness requirements for AI-generated code. Contact Sales pricing.
What people actually say about Million — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
82 mentions across 6 sources (Hacker News, Product Hunt, App Store, GitHub, Lemmy, Tech Press) · researched Jul 3, 2026.
- +Backed by Y Combinator W24 and notable investors like Scott Wu.
- +Team has a track record of successful open-source projects.
- +Addresses a critical gap: verifying AI-generated code works.
- +Targets a high-value problem for teams using AI coding agents.
- +High-level positioning is clear and compelling.
- −No verifiable community feedback or user reviews exist.
- −There are no documented integrations or platform support details.
- −Pricing is contact-only, no transparency on costs.
- −Other products with the same name cause confusion in reviews.
- −Product Hunt reviews for a different 'Million' app show customer support issues.
- • No public pricing; potential for high enterprise costs
Viability Score
How well maintained and how widely used is Million? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- ReactBench benchmark for evaluating coding agents
- Custom datasets for training coding agents
- Reinforcement learning environments for coding agents
- Trace collection and analysis for agent behavior
- Verification of AI-generated code correctness
- Production-ready validation for AI coding workflows
- Agentic testing tools for web development
- Integration with AI coding agent pipelines
- Open-source web debugging tools: React Scan and React Doctor
- Backed by Y Combinator (W24)
- Hiring – early contributor opportunity
About Million
Million Software, Inc. is an early-stage startup on a mission to fix the web by building the verification layer for AI coding agents. The company, founded by the team behind popular open-source debugging tools React Scan (22k stars) and React Doctor (14k stars), develops ReactBench, a benchmark for evaluating coding agents, along with custom datasets, reinforcement learning environments, and trace collection systems. These resources train and evaluate frontier coding agents on realistic web development tasks. For engineering teams already using AI coding tools like GitHub Copilot or Cursor, Million aims to bridge the gap between code generation and production trust. The company is backed by Y Combinator (W24) and prominent angels including Scott Wu, Amjad Masad, Evan You, and David Cramer. As of now, the homepage is a mission statement with a contact email; no public product, pricing, or documentation is listed. The company is hiring, so early adopters may have an opportunity to shape the product. If you are running AI coding agents in production and feel the pain of unverified output, Million is worth a serious look—but you'll need to reach out directly to learn more.
Behind the Verdict
Million is a mission-driven startup rather than a shipped product. The team's prior open-source tools—React Scan and React Doctor—have proven their capability in the React debugging space, which lends credibility to their new endeavor. The core idea is compelling: as AI coding agents generate more code, the bottleneck shifts from generation to verification. Million's investment in benchmarks like ReactBench and custom datasets is a smart long-term play, as these assets become more valuable as the field matures. However, for buyers, the lack of any public product, API, or pricing means the value proposition is currently speculative. If you're a large engineering organization already facing the pain of unverified AI output, engaging with Million directly might let you shape the direction, but you'll be an early adopter with no guarantees. For most teams, waiting for a concrete offering or using established CI checks and human review is more practical. The team's open-source contributions are worth watching, and if they ship a verification product, it could be significant.
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Real-world workflow fit
Concrete scenarios for the personas Million actually fits — and what changes day-one when you adopt it.
You're evaluating ways to ensure code generated by Copilot or Cursor meets quality standards before production.
Outcome: You reach out to Million to learn about their roadmap and possibly contribute feedback, but you continue relying on existing CI checks and code review for now.
You're building an evaluation harness for coding agents and need standardized benchmarks and datasets.
Outcome: You monitor Million's open-source releases like ReactBench to potentially incorporate them into your own tooling.
You're familiar with the team's debugging tools and are curious about their new venture.
Outcome: You follow their careers page and reach out to learn about potential collaboration or early access.
Use Cases
- Verify correctness of code generated by AI coding agents before deployment.
- Integrate verification checks into CI/CD pipelines for AI-generated code.
- Build safer agent workflows by validating each code output.
- Reduce manual review overhead in AI-assisted development teams.
- Ensure production reliability when scaling AI agent usage.
Limitations
- The product is in an early stage with a focus on open-source tools and datasets for training coding agents.
- The homepage provides limited concrete details about pricing, API documentation, or specific usage.
- Direct contact is encouraged for more information.
as of 2026-08-19
Verification history
We have re-verified Million 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Million's pricing actually pencils out — and where peers do it cheaper.
Pricing is not published; it is contact-sales only. As an early-stage startup, expect custom enterprise pricing that may be lower than established competitors but with more uncertainty.
Setup time & first value
How long it actually takes to get something useful out of Million — broken out by persona, not the marketing-page minute.
Not applicable—no product is available yet. If you contact the team, you may get a demo or early access, but timeline is unknown.
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Million
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Featured Head-to-Head Comparisons
Million vs Temporal Ai
Million and Temporal AI serve very different needs. If your pain point is trusting AI-generated code to be correct before merging, choose Million. If you need to build resilient, long-running AI agents that survive crashes and retries, choose Temporal. For most teams, these are complementary – use Million for verification and Temporal for orchestration.
Million vs Spider Cloud
Million and Spider Cloud serve entirely different needs. If your pain point is ensuring AI-generated code actually works before deployment, Million is the specialized tool—but it's unproven at scale and requires a sales conversation. If you need fast, reliable web data for AI agents or RAG, Spider Cloud is production-ready with a freemium model and clear pricing. Choose based on your primary bottleneck: code correctness vs. data ingestion.
Million vs Voyage Ai
Choose Million if you are an engineering team deploying AI-generated code and need to prove correctness before production. Choose Voyage AI if you are building enterprise RAG pipelines that demand high retrieval accuracy on domain-specific documents like finance or legal. They solve different problems: verification vs. retrieval.
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Frequently Asked Questions
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