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

A side-by-side comparison of CleanSlop and Local — 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.

Base Compute

Free

A macOS app that runs chat, coding and meeting AI entirely on your own Mac, with no cloud, no account and no per-token cost.

BaseRT Chip-Tuned Engine: Base Compute's inference runtime compiles for your specific Apple silicon on first launch, claiming up to 5.4x more tokens per second than engines other local apps ship.
Privacy Mode: Every request runs on the Mac and no data leaves the device — memories are stored locally, and facts marked sensitive are pinned to the machine permanently.
In-Folder Agentic Coding: Point Local at a project and it reads, edits and runs code in place without ever uploading the codebase.
On-Device Meeting Transcription: Meetings are transcribed locally with speakers labelled, so recordings and transcripts never reach a third-party service.
Memory You Can Edit: A short, fully visible list of lasting facts about you that you can review, edit or delete, rather than an opaque profile.
Memory-Aware Model Recommendations: Local reads your chip and RAM (8-16 GB, 24 GB, or 32-128 GB tiers) and suggests which open models will actually run well.
Office Mode: Serve the largest model from your fastest machine — Mac Studio, AMD Strix Halo, NVIDIA DGX Spark or an on-prem server — and reach it from Local on every laptop in the office.
Cost and Speed Analytics: A dashboard showing tokens processed, per-model throughput, and the equivalent cloud API cost you avoided.

Confidential Document Review: Drop a contract or PDF into chat and get a summary without the file ever touching a cloud provider.
Regulated-Industry Coding: Work agentically inside a proprietary codebase at a firm whose policy forbids uploading source to external AI services.
Private Meeting Notes: Transcribe internal calls with speaker attribution while keeping the audio on the laptop.
Zero-Marginal-Cost Experimentation: Run heavy prompt iteration without watching a per-token meter, since inference happens on hardware you already own.
Small-Office AI Server: Host a large open model on the one powerful Mac in the office and let the whole team query it from their own laptops.
Offline Fieldwork: Keep a full chat and coding assistant available on a flight or at a site with no reliable connectivity.
Hybrid Frontier Access: Keep everyday work local and connect your own OpenAI or Anthropic key only for the rare job that needs a frontier model.