AI CodeOps vs FixBugs

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

AI CodeOps compared with FixBugs
Detail	AI CodeOps	FixBugs
What it does	AI coding agents with quality gates that require human approval before merging	AI debugging for modern engineering teams
Stage	MVP	Startup
Founded	Not given	2025
Based in	Not given	US
Pricing model	Free trial	Freemium
Pricing	Free during open beta with 2M AI tokens per month on platform models, or unlimited with your own model key. Team plan coming after beta with 20M economy tokens, 5M standard tokens, and 1M premium tokens per month. Enterprise priced per seat with unlimited economy models, 50M standard tokens, and 10M	Free Trial: 3 bug fixes per month. Pro: $33/month for 15 bug fixes per month (or $24.75/month billed as $297/year with annual discount). Super User: $60/month for 30 bug fixes per month (or $45/month billed as $540/year with annual discount). Enterprise: Custom pricing with custom bug fix volume.
Key features	Multi-agent workflows with 20+ specialised agents Mandatory human approval gates before merge Quality gates: tests pass, review clear, human approval required Workspace budgets and spend caps Support for GitHub, GitLab, Bitbucket, Gitea and Forgejo Runs in browser with no installation required AI planning phase before code generation	Automated alert triage from monitoring stack Root cause analysis across distributed systems Issue reproduction in controlled environment Validated fix generation with test cases VS Code extension for local debugging GitHub App for automated analysis Self-hosted deployment option Integration with logs, traces, metrics and alerts Visible reasoning chain for every fix Triage sessions shareable and reloadable
What makes it different	Works with multiple Git hosts including self-hosted instances User code is not used to train AI models Transparent cost tracking with workspace spend controls Browser-based with no installation, sandboxed execution	Validation-first approach with reproduction tests and regression verification Built specifically for debugging existing bugs, not writing new code Handles complex distributed systems with stateful, auditable workflows No hard upper limit on context, processes automated log and trace ingestion Local deployment keeps code on user's machine
Community votes	0	0
DR (Domain Rating by Ahrefs)	Not checked yet no change since last check	36 no change since last check
Trust Flow (Majestic)	0	0
Citation Flow (Majestic)	0	13
Referring domains (Majestic)	0	73

6 details filled in for both companies.

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