Backgrind vs Parallax: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Backgrind and Parallax — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Backgrind
Backgrind
Always-on-top desktop overlay for macOS and Windows that runs your AI coding agent and pings you only when it needs approval or input.
Key features
- Always-On-Top Overlay: Floats your coding agent over any app, editor, browser or fullscreen game so it stays in view.
- Bring Your Own Agent: Works as a thin frontend over Claude Code, Cursor or a Backgrind-hosted model using your existing login and history.
- Attention-Only Alerts: Stays quiet while the agent works and flashes or chimes only when it needs approval or input.
- Inline Approvals: Surfaces command-run and dependency-install requests so you can approve or reject them in place.
- Customizable Window: Drag, stretch, recolor and fade the floating window to fit your workspace.
- Cross-Platform: Available for both macOS and Windows.
Best for
- Background Coding: Kick off a refactor or build and keep working elsewhere until the agent needs you.
- Supervising Multiple Agents: Keep several agent sessions visible in floating windows at once.
- Vibe Coding: Let casual builders run an agent without learning a full IDE workflow.
- Long-Running Tasks: Monitor test runs and multi-step builds without staring at a terminal.
- Approval Gating: Review and authorize potentially risky commands before they execute.
Parallax
GradientHQ
Distributed model-serving framework to build and run your own AI inference cluster across machines and cloud environments.
Key features
- 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