oMLX vs siift: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of oMLX and siift — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
siift
siift
An agentic AI operating system that helps founders map, validate and execute business strategy on one intelligent canvas.
Key features
- Intelligent Business Canvas: A visual workspace that maps ideas, assumptions, actions and results into decision-ready filters so the whole business can be seen at once.
- Living Memory System: A scalable agentic memory that learns as the business evolves and keeps context aligned across tools, data and teammates.
- AI-Scored Validation: Automated, continuous research that grades assumptions into evidence so founders know what is validated and what is still risky.
- Five-Stage Execution Loop: Guided progression through Ideate, Validate, Build, Go To Market and Scale, each with its own AI-driven workflow.
- Safe Stack Automations: Human-in-the-loop actions across 80+ popular applications so approved work executes without leaving the canvas.
- Shareable Workspaces: Collaborative views that let teammates, advisors and other stakeholders work from the same strategy context.
- Proactive Next-Step Guidance: Personalized, iterative advice that surfaces the highest-leverage action rather than a generic checklist.
- Credit-Based AI Usage: Monthly request credits scaled by plan and weighted by task complexity, with unlimited projects even on the free tier.
Best for
- Idea Validation: De-risking a new business concept by turning founder assumptions into automatically researched, scored evidence before building.
- Strategy Mapping: Turning a cloud of unstructured ideas into a visual mind-map that exposes blindspots across the business model.
- Go-To-Market Planning: Iterating on sales and marketing with an AI-native loop that tests which channels actually produce revenue.
- Product Prioritization: Helping product leaders decide what to build next based on verified market opportunities rather than intuition.
- Scaling Diagnostics: Systematizing an existing business to surface its current growth constraints and reverse-engineer fixes.
- Advisor Collaboration: Sharing a single live strategy workspace with co-founders, advisors and investors instead of static decks.