Choose Ollama if
- · You live in the terminal and want CLI as a first-class interface
- · You need Linux or Windows (we're Mac-only)
- · You want fully open-source code you can fork
- · Free matters more than polish, agents, or memory
Honest comparison
AiLocally vs Ollama
Both run AI on your Mac without the cloud. We respect Ollama — it's a fantastic open-source runtime and a big part of why local AI took off in 2024–2026. This page is the comparison we wish existed when we started: where we win, where they win, and how to choose.
Choose AiLocally if
- · You want a polished Mac app, not a terminal + chat wrapper
- · You need agents and pipelines, not just one-shot prompts
- · Cross-conversation memory is part of your workflow
- · MLX-native performance on Apple Silicon matters to you
- · You'd pay $49 once to skip the setup yak-shaving
Side-by-side
Both projects ship fast — we'll keep this page current. If you spot something wrong or stale, tell us.
| Feature | AiLocally | Ollama |
|---|---|---|
| Price | $29–129 lifetime | Free, open source |
| Platforms | macOS 26.1+ (Apple Silicon) | macOS, Linux, Windows |
| Runtime | Apple MLX (native) | llama.cpp / ggml |
| Interface | Native SwiftUI app | CLI + Ollama for Mac (Electron) |
| Built-in agents | 22 (coding, writing, research, …) | None — DIY |
| Visual pipelines | Yes (flow editor + multi-agent) | No |
| Persistent memory | Yes, cross-conversation | Per-context only |
| OpenAI-compatible API | Yes (built-in server) | Yes (built-in server) |
| Hugging Face browser | Yes, native UI + filters | CLI pull only |
| Model library size | Curated (HF MLX community) | Larger (Modelfile ecosystem) |
| Quantisation formats | MLX (4/6/8-bit, mixed) | GGUF (Q2_K → Q8_0, K-quants) |
| Auto-updates | Sparkle (planned post-notarisation) | Homebrew / manual |
| Open source | No (proprietary) | Yes (MIT) |
| Support | Email + Discord (paid) | GitHub issues + community |
Performance
MLX vs llama.cpp on Apple Silicon
Ollama uses llama.cpp under the hood — a brilliant C/C++ inference engine that runs everywhere. On Apple Silicon it's very good. But Apple wrote MLX specifically for their unified memory + Neural Engine pipeline, and the difference shows on bigger models.
In our internal benchmarks on M3 Pro 36 GB running Llama 3.3 70B 4-bit, MLX delivers 20–35% more tokens/second than llama.cpp Q4_K_M at comparable quality. On smaller models the gap closes, but on a 70B class model that's the difference between "usable" and "lol no thanks".
Caveat: benchmarks lie. We'll publish reproducible numbers + scripts in a follow-up blog post. If your favourite model isn't in MLX yet, Ollama's broader format coverage genuinely wins.
UX philosophy
Why we built a real Mac app
Ollama's "Ollama for Mac" is great if you want a frontend over the runtime. Under the hood it's Electron, which means it renders Chromium for every chat bubble. On a low-spec Mac that adds ~400 MB of resident memory before you've loaded a single model.
AiLocally is written in Swift + SwiftUI, signed for macOS 26+, and renders natively. Window snapping, native menus, Quick Look on outputs, drag-and-drop into chats, keyboard shortcuts that match the rest of the OS. It doesn't matter until you've used a CLI for two hours straight — and then it matters a lot.
Agents + pipelines
The thing Ollama leaves to you
In Ollama, an "agent" is a Modelfile with a system prompt. Want to chain three models that pass output between each other? You write a Python script that talks to the local server. Want one of them to call a tool? That's also on you.
AiLocally ships 22 first-party agents (Bug Hunter, SQL Wizard, Translator, Researcher, Doc Writer, …) and a visual flow editor where you drag boxes to compose them. Plus tool execution (web search, code run, file read) gated by your approval, not a hardcoded permission file. It's not magic — but if your weekend project is shipping ML, not building agent orchestration plumbing, the time saved is real.
Memory
Conversations that remember
Every Ollama conversation starts blank. Local Markdown files give you no concept of cross-session memory — that's a deliberate decision, and it keeps the runtime simple.
AiLocally writes structured memory entries as Markdown into your Application Support folder, indexed and injected into the system prompt on demand. The agent can call recall as a tool. You stay in full control: open the folder, edit the files, delete what you want.
Try them both. Pick the one that fits.
We mean it — Ollama is great. If you decide it suits your workflow better, that's a win for local AI. If you want native UX, agents, and memory baked in, we're here.