Halo by Scam AI vs oMLX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Halo by Scam AI and oMLX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
oMLX
jundot
An open-source LLM inference server for Apple Silicon with continuous batching and tiered KV caching, managed from the macOS menu bar.
Key features
- Tiered KV Caching: Persists past context across a hot in-memory tier and a cold SSD tier, so cached context stays reusable across requests even when the conversation context changes mid-session.
- Continuous Batching: Serves concurrent requests through a batched scheduler rather than one-at-a-time, keeping throughput up when several clients or agent loops hit the server together.
- Menu Bar Management: Controls the server, pinned models, on-demand model swapping and context limits from a native macOS menu bar app with in-app auto-update.
- Native Metal Custom Kernels: Ships precompiled kernels in the official DMG that give large speedups on affected model families — roughly 30x faster fused DSA prefill for GLM 5.2 (845 vs ~29 tok/s measured on an M3 Ultra) with lower memory use.
- OpenAI-Compatible Endpoint: Exposes every discovered model at http://localhost:8000/v1 so existing OpenAI clients, coding agents and SDKs connect without modification.
- Multi-Modality Model Support: Auto-discovers and serves text LLMs, vision-language models, OCR models, embedding models and rerankers from subdirectories of the model directory.
- Admin Dashboard: Provides a web UI at /admin for real-time monitoring, model management, chat, benchmarking and per-model settings in eight languages, with all CDN dependencies vendored for fully offline operation.
- Experimental Multi-Mac Inference: Source builds can split one model across unequal-memory Macs using MLX pipeline ranks over Ring or Thunderbolt RDMA, with a cluster dashboard for peer discovery and SSH/runtime verification.
Best for
- Local Coding Agents: Back Claude Code, OpenCode, Codex or Copilot with an on-device model where cached context makes repeated agent turns fast enough to be usable.
- Private Inference: Keep prompts, code and documents entirely on the Mac with no cloud provider in the path and no per-token billing.
- Serving a Team from One Mac: Run the OpenAI-compatible endpoint on a high-memory Mac so other machines on the network can use larger models than they could host themselves.
- Model Benchmarking: Compare throughput and per-model settings across quantizations and families from the built-in benchmark tools in the admin dashboard.
- Multi-Modal Local Pipelines: Serve embeddings, rerankers and OCR alongside chat models from a single endpoint to build local RAG without extra infrastructure.
- Running Oversized Models: Use experimental cluster mode to split a model that will not fit on one machine across several Apple Silicon Macs.