15 cloud scenarios. 43 merge-aware fixes. 100% loop is closed. 12 minutes to create, $17. Seconds and zero cost then apply everywhere.
Gomboc AI today published the first open benchmark for AI code repair, documenting the results of its deterministic repair platform across 15 real-world cloud scenarios across AWS, GCP, and Azure. Benchmarks are publicly available at https://www.gomboc.ai/show-your-work.
The methodology is open and the scenarios are published on GitHub. Any team can run the same benchmarks and publish their own numbers.
PR review time increased by 91%. Developers have merged over 98% of the code. Changes generated by AI are reaching production faster than any team can validate them. And each time the fix needs to be redone, the organization pays the token cost twice. Gomboc AI sits on top of the tools your team already uses (cursors, Claude Code, CoPilot) to precisely manage any modifications and optimize costs. The benchmarks are proof of that.
Also read: AIThority interview with Rohit Agarwal, Founder and CEO of Portkey
This benchmark covers 15 production cloud scenarios spanning security, reliability, and cost.
● Security findings include misconfigured IAM policies, open network security groups, and unencrypted storage across AWS, GCP, and Azure.
● Reliability findings include a production database processing 50,000 orders per day without backup or failover.
● Cost findings include amounts. $2,050 per month for a duplicate CloudTrail configuration, $279 per month for redundant NAT gateway routing, and $870 per month for an extra-large EC2 instance.
All fixes generated by Gomboc are idempotent, tested, and traceable to policy. Neither requires a human to interpret the output before applying it.
Benchmark numbers represent the worst case, not steady state. The 12 minutes and $17 token will cover the one-time task of creating a policy for a scenario Gombok has never encountered before. Once a policy exists, it takes seconds to apply it across all repositories, all pipelines, and every time the same issue occurs in the future, effectively reducing the token cost to zero.
This is the core of the Gombok economy. Companies pay once to codify a patch and then apply it indefinitely. All other AI coding tools don’t have the memory, policy layer, or governance to work with, so you pay the full cost of producing a similar problem every time it occurs.
“The days of Vibes-based AI tools are over. Any team evaluating AI for code repair needs to know five things: Is the output idempotent? Is it controlled? Is it compliant with policy? Is it reproducible? Is there an audit trail? The answer is yes to all five. We encourage all other tools in your stack to do the same. ” Ian Amit, Gomboc AI CEO and Co-Founder
The benchmark methodology is fully documented and reproducible. The 15 scenarios are published as open ORL files on GitHub, along with rule sets, test cases, and expected output for each fix. Any team can clone the repository, run benchmarks against the same scenarios, and publish their own results. This is intentional. Gombock isn’t asking the industry to take his word for it. We are asking the industry to conduct similar tests and demonstrate their success.
Also read: AI-powered risk intelligence: How financial institutions are anticipating systemic shocks
[To share your insights with us, please write to psen@itechseries.com ]
