CleanSlop vs Harden: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of CleanSlop and Harden — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

CleanSlop

Freemium

Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.

Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
GitHub App Integration: Scan only the repositories you select, read-only access.
Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.

A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
A side-project builder verifies that one user's data cannot be read by another through database policies.
A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
A team lead gets a short prioritized list instead of hundreds of linter warnings.
An indie hacker books an expert triage call to confirm an ambiguous finding.

Free

Local security layer for AI coding agents that inspects every tool call before it runs and can block, rewrite, or pause the action.

Pre-Execution Tool Call Checking: Every command, file edit, tool call, and outbound request is evaluated before it is allowed to run, not audited afterwards.
Intent-Aware Decisions: The check compares the proposed action against the task the developer actually requested plus session context, rather than matching a static rule list.
On-Device Model: A post-trained 8B-parameter model runs locally and is reported to beat frontier models on agent-security benchmarks while remaining fast enough to sit inline.
Graduated Responses: Actions can be allowed, blocked, redacted, paused for a human answer, or simply logged, and a blocked action does not stop the agent's other work.
Secret and Exfiltration Protection: Catches credential leaks, data exfiltration attempts, and destructive infrastructure operations before they execute.
Local Review Dashboard: Every decision is logged and reviewable through a dashboard that runs on the developer's own machine.
Broad Agent Compatibility: Works with Claude Code, Cursor, Codex, Hermes, OpenClaw, Kiro, and Antigravity.
Privacy by Design: Repository contents and tool output stay on the local machine because inference happens there.

Guarding Autonomous Coding Sessions: Let a coding agent run with fewer manual approvals while a local check still catches destructive commands.
Preventing Secret Leakage: Stop an agent from pasting API keys or credentials into an outbound request or a committed file.
Blocking Prompt-Injection Damage: Catch actions an agent was steered into by malicious content in a repository, issue, or web page it read.
Protecting Production Infrastructure: Intercept destructive infrastructure operations before they reach cloud resources or databases.
Agent Behaviour Auditing: Review a logged trail of what an agent tried to do and which actions were blocked or redacted.
Team Policy Enforcement: Apply consistent guardrails across developers using different coding agents on the same codebase.