Kodus AI vs Mira

Kodus AI and Mira are both coding assistants tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Kodus AI

Kodus runs as an agent that watches pull requests across GitHub, GitLab, Bitbucket, and Azure Repos, posts inline comments, and can convert unresolved suggestions directly into tracked issues in Jira, Linear, or Notion. You write review rules in plain language — no DSL, no YAML policy files — and the agent applies them on every diff. Because you supply your own API keys and can self-host the full stack via Docker Compose, token costs are billed directly to your LLM provider, not marked up through Kodus. The ceiling appears when your rules grow complex enough that plain-language enforcement becomes ambiguous; at that point, teams either tighten the rule wording iteratively or accept occasional false-positive comments that engineers learn to dismiss.

Mira

The vendor states Mira hooks into GitHub via a self-hosted GitHub App, fires on every pull request open event, and posts inline comments within a median of 77 seconds — mapping call graphs and dependency blast radius before reading the diff. It flags bugs, auth bypasses, missing awaits, and style drift by reading the repo's own patterns rather than a ruleset you maintain. The self-host path is a single Docker command; the model is swappable via environment variable, so teams running Ollama or a private Anthropic endpoint are equally supported. Where it breaks: teams needing IDE feedback before a PR exists, or wanting issues surfaced in CI pipelines outside GitHub, hit a gap the tool does not currently fill.

Attribute	Kodus AI	Mira
Pricing	Paid	Free
Price	$10/dev monthly or $8/dev annual	—
Free trial	14 days	No
Open source	Yes	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	GitHub, GitLab, Bitbucket, and Azure DevOps	Docker, self-hosted
Pros	Bring-your-own-key model routing, so switching between OpenAI, Anthropic, or a local model when costs change is a configuration update, not a vendor conversation. Full self-hosted deployment via Docker Compose, so source code never leaves your infrastructure — which removes the blocker for teams with data-residency or compliance requirements that rule out third-party SaaS. Automatic issue creation from unresolved review comments, so technical debt surfaces in your existing tracker (Jira, Linear, Notion) instead of dying in a closed PR thread. Plain-language review rule definitions, so teams enforce custom standards without learning a DSL or maintaining a separate policy-as-code layer. Works across GitHub, GitLab, Bitbucket, and Azure Repos from a single deployment, so teams on non-GitHub platforms are not treated as second-class integrations.	Self-hosted by default with a single Docker command, so your source code never transits a third-party SaaS — which matters the moment a security or compliance audit asks where your code traveled. Model is swappable via a single environment variable, so switching from Anthropic to a local LLM when API costs or data-residency requirements change does not require re-architecting the deployment. Convention enforcement derives from the repo itself rather than a config file you maintain, which means teams avoid the ongoing cost of keeping a ruleset synchronized with how the codebase actually evolves. Blast radius reporting — listing dependent repositories and reference counts alongside each flagged issue — lets engineers triage by actual impact rather than debating whether a comment is worth addressing. Apache 2.0 license, so teams that need to audit, fork, or extend the reviewer are not blocked by proprietary terms — unlike SaaS alternatives where the review logic is a black box.
Cons	Self-hosting requires Docker Compose setup and ongoing infrastructure maintenance; teams that want managed, zero-ops AI code review hit this wall on day one and frequently move to a fully-managed SaaS alternative instead. Plain-language review rules hit an ambiguity ceiling as rule sets grow — when a rule is broad enough to produce frequent false-positive comments, the only remedies are iterative rewording or engineering team tolerance, neither of which scales cleanly past a few dozen active rules. MCP-based integrations with Jira, Notion, and Linear add context to reviews but require configuration and ongoing credential management; teams that skip this setup get shallower spec-aware review and lose the primary workflow integration advantage Kodus advertises over simpler linting-layer tools.	The only documented integration trigger is a GitHub pull request open event. Teams wanting feedback earlier — pre-commit, on push to a branch, or inside a CI pipeline gate — get nothing from Mira, and adding that coverage requires a separate toolchain running in parallel. No hosted option exists. Teams without the infrastructure capacity or operational appetite to run and maintain a containerized service, manage GitHub App credentials, and keep Postgres healthy will spend more time on the deployment than the review coverage saves them — at which point a hosted SaaS reviewer is the rational alternative. The benchmark cited on the product page is vendor-published against a single 50-PR dataset judged by a specific Claude model. Teams making a production bet need to validate false-positive rates against their own repos; community-independent benchmarks are not yet available, so quality claims cannot be verified externally before deployment.

Bottom line

Kodus AI is paid while Mira is free; only Kodus AI exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Kodus AI and Mira?

Kodus AI is Paid and open source, while Mira is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Kodus AI better than Mira?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.

Kodus AI vs Mira: which should I pick?

Pick Kodus AI if its pricing model, openness, or platform fit matches your constraints; pick Mira otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.