BaseRT vs Halo by Scam AI: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of BaseRT and Halo by Scam AI — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
BaseRT
Base Compute
BaseRT is a high-performance LLM runtime for Apple Silicon that runs open-source models locally, faster than llama.cpp and MLX.
Key features
- 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.
Best for
- 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.
Halo by Scam AI
Reality Inc
AI trust platform that detects deepfakes, voice clones, and GenAI content across images, video, audio, IDs, and live video calls.
Key features
- Deepfake Detection: Catches face swaps, lip-sync, reenactment, and cloned voices that impersonate real people in image, video, and audio.
- GenAI Content Detection: Identifies fully synthetic images, video, and audio from Stable Diffusion, DALL·E, Midjourney, Sora, and ElevenLabs.
- Halo On-Device Call Protection: Runs 100% on-device to flag synthetic faces and face swaps live on Zoom, Teams, and Meet without recording or uploading anything.
- Eva-v1 Model Family: Eva-v1-Fast returns verdicts on images in under 2 seconds; Eva-v1-Pro delivers forensic-grade accuracy in under 4 seconds.
- Unified REST API: One integration handles both deepfake and GenAI detection across image, video, and audio via a single authenticated endpoint.
- Identity & Document Verification: Detects forged IDs, manipulated selfies, and AI-generated documents before onboarding completes.
- Add-on Defenses: Adaptive Defense, Active Liveness, and Express Lane low-latency mode extend detection with injection-attack protection and 3s response SLAs.
- Enterprise Compliance: GDPR compliant, SOC 2 Type II attested, and no media retention by default, with configurable retention for audit needs.
Best for
- KYC & Onboarding: Financial services and marketplaces block AI-generated selfies and forged IDs before an account is opened.
- Contact Center Fraud Prevention: Detect voice clones in real time to stop synthetic-caller attacks against call-center authentication.
- Executive Call Protection: Halo alerts staff to face-swap impersonations on video calls before authorizing wire transfers.
- Hiring & Remote Interviews: Recruiters catch deepfake candidates impersonating real engineers during video interviews.
- Content Moderation: Media platforms scan uploads at scale to flag GenAI images, video, and audio before they reach users.
- Insurance Claim Review: Detect manipulated photos and forged supporting documents submitted with claims.