BaseRT vs Screencap: Features, Pricing & Which Is Better (2026)

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

BaseRT

Base Compute

Free

BaseRT is a high-performance LLM runtime for Apple Silicon that runs open-source models locally, faster than llama.cpp and MLX.

Apple Silicon Optimized Runtime: A native inference engine tuned for M-series chips that outperforms llama.cpp and MLX on decode and prefill benchmarks.
One-Line Install: BaseRT ships as a single curl-piped install script, so users can go from download to serving a model in seconds.
Broad Open-Source Model Support: Runs Qwen3, Llama 3.1/3.2, Gemma 3/4, Mistral, Phi-3, and Nomic BERT out of the box, with quantized (Q4/Q8) weights.
Local Serving for Coding Agents: `basert serve <model>` exposes a local endpoint that pairs with the pi plugin so coding agents run fully on-device with no API keys.
Privacy by Default: All inference happens on the user's machine, so prompts, code, and outputs never leave the device.
Benchmark-Driven Performance: Publishes tokens/sec comparisons on Apple M5 Pro against MLX and llama.cpp for reproducibility.

On-Device Coding Assistant: Engineers pair BaseRT with a local coding agent to get autocomplete and refactoring without sending source code to a cloud API.
Private Model Evaluation: ML practitioners benchmark open-source models on their own laptop without renting GPUs or exposing test data.
Offline LLM Applications: Developers ship desktop apps that call a locally served model, avoiding rate limits and per-token costs.
Prototyping on Apple Silicon: Researchers experiment with new quantizations and open-weight models on M-series Macs at high throughput.
Enterprise On-Prem Inference: Teams with data-residency constraints run production inference on employee devices instead of external APIs.

Proteus Computer Use

Freemium

Local-first macOS screen recorder that captures, labels, and indexes team workflows so knowledge stays searchable and private.

Local-First Capture: Recordings live in ~/.screencap on your Mac and never leave unless you explicitly share them.
On-Device Task Segmentation: An on-device model breaks long recordings into labeled tasks like payroll runs, expense approvals, or CRM data entry.
Full-Text Search of Workflows: Every spoken word and on-screen moment is indexed so any past workflow can be surfaced months later by search.
Privacy-Enforced Recording: Password managers and banking apps are cut before a frame is written; email and chat are masked in real time.
MCP Context Snapshots: While recording, Screencap queries connected MCP servers to capture the exact Gusto/Attio/Linear/Notion record on screen inside the video.
Deliberate Sharing With PII Scrubbing: Every shared copy is scrubbed of names, secrets, and PII, and only the recordings you pick ever leave the machine.
Source-Available Codebase: The full capture engine, encryption, agent, and anonymizer are public on GitHub under PolyForm Noncommercial 1.0.0.

Team Onboarding: Assemble ordered collections of real workflow recordings so new teammates learn exactly how work is actually done.
Institutional Knowledge Capture: Preserve the tacit steps behind payroll runs, reconciliations, and quarterly reports as searchable video.
Ops Documentation: Replace stale wikis by pointing teammates at labeled task recordings that stay current with the real system.
Compliance-Sensitive Recording: Capture back-office work in banking, finance, and HR without leaking passwords, account balances, or PII.
Computer-Use Dataset Contribution: Optionally donate reviewed, scrubbed recordings to a public dataset for training open computer-use models.