Local AI vs Online Services: Cost, Quality, and How to Choose ​

Written in September 2026. AI technology moves fast, so the model capabilities, prices, and hardware requirements mentioned here reflect the situation at that time and are for reference only.

AI tools keep multiplying, and almost every creator, developer, and student is using them. But there is a very practical question: is it better to use online services, or to deploy locally?

Online services are convenient and easy to use, but monthly subscriptions add up — image, voiceover, music, video, LLMs... subscribe to all of them and a few thousand yuan a year is completely normal.

Local deployment is free, private, and unlimited, but you have to wrestle with the environment yourself, and quality may take a hit.

This article looks at five areas and compares cost, quality, and use cases, so you can work out: which areas are already good enough locally, where online is still the safer bet, and which combination gives you the best value.

How much do online AI services really cost per month? ​

Let's start with what the common AI services charge. The following are public prices looked up in September 2026, for reference only — platforms may adjust them at any time.

International AI services ​

International services are priced in USD, converted at an exchange rate of 7.15, excluding payment processing fees.

China-based AI services ​

Most China-based services come with a free quota, so light usage may cost nothing at all. But if you are a heavy user with two or three subscriptions, ¥200-300 a month is normal — that's ¥2,000-3,000 a year. Add international services on top and the bill gets higher still.

Note: the prices above are public information looked up in September 2026. Different services bill differently (monthly, per generation, per credit), so a simple side-by-side comparison is for reference only. Always check the official source.

Five areas: which ones can be replaced locally? ​

Straight to the conclusion:

A short note on each below.

Image generation: local is already good enough ​

The flagship tool is ComfyUI, which can run models such as FLUX.2, Krea 2, Z-Image, and Qwen-Image. On image quality and prompt understanding, FLUX.2 Dev is already close to Midjourney, and models tuned for specific styles can even do better.

That said, ComfyUI's learning curve is steeper than Midjourney's or Jimeng's — you need to understand the idea of a "workflow". It suits people willing to spend time digging in.

Voice synthesis: Chinese TTS is mature ​

There are a dozen or so local TTS models to choose from, such as IndexTTS 2, GPT-SoVITS, Qwen3-TTS, and CosyVoice. For Chinese speech synthesis, local models are already very mature — both voice cloning and natural speech synthesis reach a genuinely usable level.

If all you need is voiceover for videos or spoken-word content, local TTS is more than enough, and there is no word-count limit.

Music generation: the lowest barrier ​

The standout is ACE-Step 1.5, an open source model jointly developed by StepFun and ACE Studio. Officially, its quality is described as sitting between Suno v4.5 and v5, with support for 1,000+ instruments and genres.

Beyond that there are several specialized models — Stable Audio 3 for background music, MOSS-SoundEffect-v2.0 for sound effects, SoulX-Singer for covers — each with its own strengths.

Hardware requirements are extremely low: 4GB of VRAM is enough to run it. This is the lowest barrier of the five areas.

Video generation: fastest progress, highest barrier ​

Video generation changed a lot in 2026. One landmark event was MiniMax H3 going open source — the same model is available both as a paid online service (Hailuo AI) and as a free download you can run locally.

Locally, ComfyUI + MiniMax H3 can produce high-quality video clips. But the learning barrier is high and so is the hardware requirement (12GB+ VRAM recommended). Online services (Kling, Jimeng, and others) still win on one-click results and interaction design.

Extra note: Seedream is ByteDance's image generation model and Seedance is its video generation model. Jimeng uses both, so it can generate images as well as video. Kling is Kuaishou's product and focuses more narrowly on video generation.

Large language models: local has a clear gap ​

This is the part where honesty is required. The gap between local LLMs and top online models is bigger than in any other area.

The reasons are very practical:

• Not enough VRAM: consumer GPUs have 8-24GB, which only runs models of 7B-32B parameters, or quantized versions of larger models. Quantization costs accuracy.
• Not enough bandwidth: consumer GPU memory bandwidth is far below data-center hardware, which caps inference speed.
• Not enough system RAM: loading large models needs a lot of memory — 32GB or more to run 14B+ models smoothly.

Even with open source agent frameworks, most people still choose a hybrid "local framework + online API" setup, for example using a Qwen API or DeepSeek API. Running an agent entirely locally only works on a high-spec machine.

Recommendation: for everyday writing, translation, and simple Q&A, a local model is enough. For complex reasoning and deep analysis, stick with an online service — Qwen and DeepSeek are both good options.

Payback period: how long until you earn it back? ​

Take a heavy user as an example, assuming roughly ¥600 per month spent on AI tools (a mix of international and China-based services; your mileage will vary), and work through three typical scenarios.

Important disclaimer: the hardware prices below are market reference estimates as of September 2026. Prices vary enormously between e-commerce platforms, channels, and time periods, so they should not be treated as a buying guide. Always go by the actual market price when you purchase.

Scenario A: you already have a PC, and add one GPU ​

Good for people who already have a desktop and just want to try things out. Add one GPU and you can run TTS, music, and basic image generation.

Scenario B: build a brand-new AI creation machine ​

Good for people who plan to do AI creation long term and don't want to be limited by online service quotas. Year one roughly breaks even; from year two on it's pure savings.

Scenario C: you already have a Mac with an M-series chip ​

But there's a catch: you must not need AI video generation. Video generation is the Mac's biggest weakness — many video models only support NVIDIA's CUDA ecosystem, so on a Mac they either won't run or run very slowly. If you want to do video, Mac users still need an NVIDIA-based machine.

On top of that, local LLMs cannot replace online services, so that part of the spending stays.

What about schools and training institutions? ​

For schools and training institutions, the math works a bit differently:

• Per-seat accounts are expensive: dozens of students each needing an account means tens of thousands of yuan per semester, and you may keep paying over the winter and summer breaks
• Usage is uneven: everyone uses it during class, nobody uses it afterwards — a subscription model wastes a lot
• Local deployment is better value: build one local AI lab shared by the whole school or institution — a one-time investment, used for years
• There's teaching value too: students can get hands-on and run experiments instead of merely "using" AI

Of course, hardware investment is also larger for schools and institutions — you may need server-grade equipment or multiple workstations. But over the long term, especially with a large student body, the cost advantage of local deployment keeps growing.

The combination strategy we recommend most ​

Local and online are not an either/or choice — use them together.

Best combination: online China-based services for LLMs, local for image, voice, music, and video generation.

Why:

• Local LLMs have the biggest gap, while online services (Qwen, DeepSeek, and others) are cheap and good — a few dozen yuan a month, not worth trying to save
• Image, voice, and music are creation tools you use heavily and burn through quotas fast; running them locally has no limits and saves more
• Choose video generation as needed: online for convenience (Kling, Jimeng), local for volume and freedom (ComfyUI + H3)

Put together this way, you keep the few dozen yuan of LLM subscription fees, run the high-frequency creation scenarios — images, voiceover, music — locally, and save several hundred yuan a month in quota costs.

Which area should you start with? ​

If you want to try local AI but don't know where to begin, we suggest this order:

1. Start with music generation — the lowest hardware requirement (runs on 4GB of VRAM), ACE-Step 1.5 sounds good, and it's easy to get positive feedback
2. Then try TTS voice synthesis — equally low barrier, very practical, useful for voiceover and audio content
3. Next, image generation — ComfyUI's learning curve is steep, but once you're up to speed the productivity gain is the biggest
4. Save video generation for last — the highest hardware requirement; consider it once you're comfortable with the others
5. Large language models — just use online services day to day, and set up local once you have high-spec hardware

If you're also paying continuously for AI tools, why not start with a low-barrier area like music or TTS and try local deployment. With 魔当 you can install all the local AI apps mentioned above in one click, without wrestling with the command line or environment configuration.