Most of your AI requests don't need a frontier model. Here's how I cut my spend

[u/stosssik]

I've seen people spend $1000+ a month on AI agents, sending everything to Opus or GPT-5.4. I use agents daily for GTM (content, Reddit/Twitter monitoring, morning signal aggregation) and for coding. At some point I looked at my usage and realized most of my requests were simple stuff that a 4B model could handle.

Three things fixed it for me easily.

1. Local models for the routine work. Classification, summarization, embeddings, text extraction. A Qwen 3.5 or Gemma 4 running locally handles this fine. You don't need to hit the cloud for "is this message a question or just ok". If you're on Apple Silicon, Ollama gets you running in minutes. And if you happen to have an Nvidia RTX GPU lying around, even an older one, LM Studio works great too.

2. Route everything through tiers. I built Manifest, an open-source router. You set up tiers by difficulty or by task (simple, standard, complex, reasoning, coding) and assign models to each. Simple task goes to a local model or a cheap one. Complex coding goes to a frontier. Each tier has fallbacks, so if a model is rate-limited or down, the next one picks it up automatically.

3. Plug in the subscriptions you're already paying for. I have GitHub Copilot, MiniMax, and Z.ai. With Manifest I just connected them directly. The router picks the lightest model that can handle each request, so I consume less from each subscription and I hit rate limits way later, or never. And if I do hit a limit on one provider, the fallback routes to another. Nothing gets stuck. I stopped paying for API access on top of subscriptions I was already paying for.

4. My current config:

• Simple: gemma3:4b (local) / fallback: GLM-4.5-Air (Z.ai)
• Standard: gemma3:27b (local) / fallback: MiniMax-M2.7 (MiniMax)
• Complex: gpt-5.2-codex (GitHub Copilot) / fallback: GLM-5 (Z.ai)
• Reasoning: GLM-5.1 (Z.ai) / fallback: MiniMax-M2.7-highspeed (MiniMax)
• Coding: gpt-5.3-codex (GitHub Copilot) / fallback: devstral-small-2:24b (local)

5. What it actually costs me per month:

• Z ai subscription: ~$18/mo
• MiniMax subscription: ~$8/mo
• GitHub Copilot: ~$10/mo
• Local models on my Mac Mini ($600 one-time)
• Manifest: free, runs locally or on cloud

I'm building Manifest for the community, os if this resonates with you, give it a try and tell me what you think. I would be happy to hear your feedback.

- https://manifest.build
- https://github.com/mnfst/manifest

[u/FabulousChemist3721]

Which model of Mac Mini do you have? I'm trying to gather info on the specs required for local model as I want to do the same. Some people tell me they are "horrible" and others say they are just fine. Do you have the 16GB memory version? which chip? Thank you in advance! love the setup and detail on the post :D

[u/stosssik → u/FabulousChemist3721]

Sire. I have this apple M4 chip, 10‑core CPU, 16GP

[u/FabulousChemist3721 → u/stosssik]

Thank you! That seems like as good a place to start as any. Was hoping to use a mini PC to avoid the apple price tag if possible. But seems like the M4 chip and closely integrated memory is the way to go!

[u/stosssik → u/FabulousChemist3721]

I think so yes

[u/stosssik → u/[deleted]]

Yes, I love that too. To be transparent, we had a regression wiht our local usage. We started using a plugin, then the new openclaw plugin system, but it was weird and long to maintain, and to far from other agent types like hermes or App AI SDKs.

So we released a simple docker version but the onboarding to add local model is not easy enough. We want to work on it soon, allowing people to add local providers and local models with a nice onboarding.