The honest summary, up top
- Privacy and offline: local wins.
- Cost at scale: local wins.
- Cost for occasional use: cloud wins.
- Latency on M-series: roughly tied for short prompts.
- Frontier quality: cloud still wins for the hardest 20% of tasks.
- Multimodal: cloud wins; local vision-capable models are catching up but not there yet.
- Operational simplicity: cloud wins. Local needs you to babysit a model server.
What local AI actually delivers in 2026
Two things have changed since 2024:
- Apple Silicon. Unified memory + Metal Performance Shaders + MLX make 32B-class models practical on a real-world laptop.
- Open weights closed the capability gap. Qwen 2.5, DeepSeek-Coder 3, and Llama 3.3 are genuinely useful, not toys.
On an M4 Pro with 36 GB, a 14B coder model streams at ~30 tok/sec. That feels like a fast cloud call. A 32B is closer to 12–18 tok/sec — usable, slower than the frontier.
Where cloud still wins
The hardest tasks — novel algorithm design, long-context multi-file reasoning, deep multimodal work, agentic orchestration with many tools — still benefit from the GPT-5 / Claude 4.5 / Gemini 3 tier. Local closes 80% of the gap for 80% of tasks; the last 20% is what frontier models charge for.
The cost math
Cloud LLMs are cheap per call and expensive at volume. A team doing 50k tokens per developer per day across 100 developers is paying ~$3–6k/month on a frontier model. Local hardware pays for itself in 12–18 months at that volume — and your code never leaves the building.
For an individual using AI casually, the math is reversed: a few dollars a month on a hosted API is far cheaper than an M-series upgrade you'd buy anyway for other reasons.
The privacy math
Cloud providers have improved their data-handling policies dramatically since 2023 — most enterprise tiers will sign DPAs, don't train on your data, and offer region pinning. That doesn't change the fundamental answer for sensitive code: if it can't leave the building, it can't go to a cloud API.
Local AI removes the question. The data never moves. For compliance-bound work (HIPAA, GDPR with hard borders, defense), this is decisive.
The hybrid pattern most people land on
Pure local feels purist; pure cloud feels lazy. Most production setups blend:
- Local STT (Whisper) for transcription.
- Local 7–14B for completion, chat, "explain this stack trace".
- Cloud frontier for the few-times-a-day heavy tasks, with explicit opt-in per request.
Cloak supports exactly this pattern. Settings → STT picks local Whisper. Settings → Models → Custom Provider points at a local Ollama / LM Studio server. Settings → Models → Cloud Provider keeps a hosted key around for hard tasks. You see in the UI which one served each turn.
How to decide for your work
Answer three questions:
- Can the source leave my machine? If no — local.
- Am I paying for AI more than I spend on coffee? If no — cloud is cheaper. If yes — local is breaking even.
- Does my hardest task need frontier capability? If yes — keep a cloud key around for that task and run local for the rest.
Try the hybrid
Download Cloak from the home page. The hybrid local+cloud setup takes about ten minutes to configure and is the most flexible AI workstation you can run on a Mac.