Local AI for ASO: Run RespectASO Pro's AI Features on Your Own Mac

TL;DR: RespectASO Pro now supports Local AI for ASO — you can run the AI Niche Researcher, AI Competitor Analyzer, and ASO Score Simulator on a model hosted on your own Mac via Ollama or LM Studio, instead of a cloud provider. The result: no per-query cost, no API key, and nothing leaves your machine. Because these analyses are demanding, performance depends on your Mac's hardware, and larger models give the most robust suggestions. This post explains what Local AI for ASO is, how it saves money, how to set it up, and when to use it.

Last updated: June 7, 2026

Local AI for ASO is here. From this release, RespectASO Pro can run its AI workflows on a model hosted on your own Mac — not just on OpenAI, Anthropic, or Google. If your machine can run a capable local model, you can do AI keyword research, competitor breakdowns, and metadata scoring with zero per-query cost and complete privacy. It's the same agentic engine you already know; you're simply choosing where the model lives.

This is the natural next step for a tool that has always tried to keep cost and data in your hands. The bring-your-own-key model already lets you pay cloud providers directly, with no platform markup. Local AI takes that to its logical conclusion: run the model yourself and pay nothing per query.

What is Local AI for ASO?

Local AI for ASO means running the large language model that powers RespectASO Pro's AI features on your own computer, rather than calling a hosted cloud API. You install a small local model server, point RespectASO at it, and the Pro AI workflows run on-device.

RespectASO works with two popular, free runtimes:

• Ollama — a lightweight model server you drive from the command line. Install it, pull a model, and RespectASO connects at http://localhost:11434.
• LM Studio — a friendly desktop app for downloading and serving models. Start its local server and RespectASO connects at http://localhost:1234.

Any other OpenAI-compatible local endpoint works too, through the Custom option. Whichever you choose, the experience inside RespectASO is identical: open Settings → AI, choose Local AI, detect your installed models, and confirm the setup with a one-click test.

Why running ASO AI locally saves money

Cloud AI is metered. Every niche research run, every competitor analysis, every metadata simulation calls a provider that charges for usage. With RespectASO's BYOK approach you pay that provider directly, with no platform markup — but you're still paying per query, and it adds up if you run a lot of analyses.

Local AI removes the meter entirely. The model runs on hardware you already own, so the per-query cost is zero. There's no API key to fund and no usage cap to watch. You can iterate on a tricky niche as many times as you like, re-run a competitor analysis whenever a rival updates their metadata, and stress-test a dozen metadata drafts in the ASO Score Simulator without thinking about cost.

How you run the AI	Per-query cost	API key	Where it runs
Cloud provider (OpenAI / Anthropic / Gemini)	Charged by your provider	Your own key	Provider's servers
Local AI (Ollama / LM Studio)	Nothing	None	Your Mac

To be clear about what's free and what isn't: you still need a Pro license to unlock the AI workflows. Local AI removes the per-query AI cost, not the license. You can compare editions on the pricing page.

What you can run locally: the three Pro workflows

Every AI feature in RespectASO Pro works with Local AI:

• AI Niche Researcher — expands a seed keyword into a researched niche with related terms, search intents, and country signals.
• AI Competitor Analyzer — breaks down a top-ranking competitor's positioning, metadata patterns, and likely keyword strategy.
• ASO Score Simulator — evaluates a draft title, subtitle, and keyword field against scoring and competitor context, and can generate localized metadata.

These aren't single-shot prompts. RespectASO's engine is agentic: it researches, scores, validates against App Store constraints, and refines over several rounds. That's what makes the output reliable — and it's also why a capable model matters, which we'll get to below. For a fuller tour of what each workflow produces, see the AI Features documentation.

Privacy: your research never leaves your Mac

RespectASO has always been privacy-first — no telemetry, no account, and a local database on your machine. The one part that previously involved an outside service was the AI call itself, which went to your chosen cloud provider. Local AI closes that gap: with a model running on your Mac, your prompts and results never leave the device, and there's no API key to store or rotate.

If keeping competitive research off third-party servers matters to you, Local AI is the strongest option RespectASO offers. It pairs naturally with the broader privacy-first design and with running ASO inside your own AI assistant via MCP.

Performance and hardware: the honest part

Here's the candid trade-off. Because RespectASO's analyses are demanding and multi-step, local models won't perform equally on every device. Speed depends on your Mac's hardware — the chip, and especially memory bandwidth. On lighter machines, larger models can run slowly.

Two practical rules follow from this:

1. Bigger models give better results. Larger models handle the agentic, multi-round workflows best and return more robust, more relevant suggestions. If a smaller model feels slow or its output feels thin, moving up a size is usually well worth it.
2. Test before you rely on it. The built-in Test Local AI check runs a quick, representative task so you know your setup is ready before you start a full analysis. And you can cancel any run at any time.

We deliberately don't water down the analysis to make it run on the smallest possible model — output quality is the priority. If your hardware isn't there yet, a cloud provider remains a fast fallback, and you can switch back and forth freely.

How to set it up in five steps

1. Install Ollama (ollama.com) or LM Studio (lmstudio.ai) and download a model.
2. In RespectASO, open Settings → AI and choose Local AI.
3. Pick your runtime — the local address fills in automatically.
4. Click Detect to list your installed models, and select one.
5. Click Test Local AI to confirm it's ready, then save and run any AI workflow.

The full walkthrough, including tips for each runtime, lives in the Local AI setup guide. Local AI runs in the native macOS app.

When to use Local AI vs the cloud

You don't have to pick one forever — switch any time from Settings → AI. A pattern that works well:

• Local AI for everyday, high-volume work: iterating on niches, re-running simulations, and anything where you value zero cost and full privacy.
• A cloud model (with your own key) for occasional heavy jobs, or when you want maximum speed on lighter hardware.

If you want the deeper rationale for keeping AI cost and data under your control, the Local AI ASO tool page lays out the full story, and the BYOK page covers the cloud side.

Get started

Local AI is part of RespectASO Pro. Download RespectASO, install a local model with Ollama or LM Studio, and point the app at it in Settings → AI. From there, your ASO keyword research, competitor analysis, and metadata scoring run on your own Mac — for nothing per query, and with nothing leaving your machine.

It's a big step for a privacy-first, cost-conscious ASO tool: the full agentic workflow, now entirely in your hands.