Maced AI vs Mira

Maced 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.

Maced AI

Maced deploys AI agents that crawl, fuzz, and attempt exploitation across your web apps, APIs, source code, and cloud infrastructure — then deliver audit-grade reports with proof-of-exploit payloads and merge-ready fix PRs. Every finding is auto-validated before it surfaces, which means triage queues shrink instead of growing. The continuous monitoring model means your attack surface is tested on every deploy, not just once a quarter. The ceiling shows up when your environment demands the kind of adversarial creativity a seasoned human tester brings to a novel business-logic flaw — agents that follow a structured probe loop will miss what only lateral thinking finds. Teams with that requirement use Maced for baseline and point a human at what the agents flag as high-severity.

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	Maced AI	Mira
Pricing	Paid	Free
Price	$249/mo	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	Web-based SaaS; on-premises and air-gapped deployment available	Docker, self-hosted
Pros	Auto-validation with proof-of-exploit payloads for every finding, so your team stops spending sprint time manually reproducing scanner noise before deciding whether to act. Merge-ready fix PRs generated and retested automatically, which means remediation moves from 'ticket in backlog' to 'reviewed and merged' without a separate engineering investigation cycle. Continuous scanning triggered on every deploy rather than quarterly, so a misconfiguration introduced in Tuesday's PR is caught before it reaches production — not six weeks later in an audit. SOC 2 and ISO 27001 audit-ready report output, so compliance documentation is a byproduct of your normal security workflow rather than a separate manual engagement you schedule and budget for. Self-hosted deployment option, so teams operating in air-gapped or strict data-residency environments can run the platform without routing source code or infrastructure details through a third-party cloud.	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	Agents follow a structured crawl-fuzz-exploit loop, which means multi-step business-logic attacks that require contextual judgment — an attacker who knows your domain and chains three unrelated weak points — fall outside what the platform reliably discovers. Teams whose threat model centers on that class of vulnerability still require a human penetration tester; Maced becomes a first-pass filter, not a full engagement replacement. The platform is paid-only with no free tier beyond an initial scan, so teams evaluating at scale against a large or complex environment cannot fully assess fit before committing to a subscription — at which point switching cost is real if the agents' coverage does not match the environment's actual attack surface. White-box testing requires handing over source code access, and for teams at organizations where that creates legal, contractual, or procurement friction, onboarding stalls at the approval stage rather than the technical one — a problem self-hosting solves only if your ops team has bandwidth to stand up and maintain the infrastructure.	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

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

Frequently asked questions

What is the difference between Maced AI and Mira?

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

Is Maced 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.

Maced AI vs Mira: which should I pick?

Pick Maced 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.