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

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

GradientHQ

Free

Distributed model-serving framework to build and run your own AI inference cluster across machines and cloud environments.

Distributed Model Serving: Routes inference requests across multiple machines and GPUs to serve models larger than a single device, improving throughput and enabling multi-node inference.
Cluster Deployment Anywhere: Designed to be deployed on cloud providers, on-premises servers, or hybrid environments so teams can run inference where they prefer.
Model Partitioning and Sharding: Supports partitioning or sharding of model computation across devices to handle very large models that do not fit on a single GPU.
Hardware-Aware Scheduling: Allocates workloads across available CPU/GPU resources to maximize utilization and reduce inference latency across the cluster.
Scalable Load Balancing: Balances traffic across worker nodes and can scale up or down to match inference demand, improving reliability under variable load.
Extensible Open-Source Architecture: Provides hooks for integrating custom model backends, user authentication, and monitoring integrations to adapt to different deployment needs.
Distributed model serving across a cluster
Ability to build and run AI clusters on arbitrary infrastructure
Scalable inference workload distribution
Open-source codebase hosted on GitHub

Serving Large LLMs: Host and serve large language models that exceed single-GPU memory by partitioning the model across multiple GPUs for low-latency inference.
Hybrid Cloud Deployment: Deploy inference clusters that span on-premises GPUs and cloud instances to keep sensitive data local while scaling compute in the cloud.
High-Throughput Inference for Applications: Provide reliable, load-balanced model endpoints for applications (chatbots, search, recommendation systems) that require consistent throughput.
Research and Model Evaluation: Run distributed inference experiments and benchmarks across different node configurations to evaluate performance and cost trade-offs.
Self-Managed ML Infrastructure: Replace or augment managed vendor services with a self-hosted inference cluster to retain control over data, costs, and deployment topology.
Deploying scalable model inference clusters for production ML workloads
Running model serving on private or on-premises infrastructure
Distributing inference load across multiple nodes to improve throughput and availability
Experimenting with custom cluster topologies for model deployment