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You spend three hours debugging a model quantization issue. Your GPU utilization stays at 12%. Your hardware runs hot. Meanwhile, your teammate uses an API. Their code works fast. Nobody calls them at 2 AM about memory errors.
Local LLM setups look free. They feel empowering. But the math often fails when you move to production.
I spent six months running Ollama for solo projects and small teams. I tried to use it for a production pipeline. Here is what I learned.
Local inference is great for demos and research. It is a bad choice for production architecture for most teams.
The Good Side Ollama is useful for specific needs:
- Experimenting without API bills.
- Using data that cannot leave your servers.
- Testing models in a controlled environment.
- Accessing models like DeepSeek or Kimi.
The Hidden Costs The GitHub stars do not show the real price. You pay in ways that do not show up on an invoice:
- GPU memory is limited. A 70B model needs a high-end workstation.
- Maintenance is a full-time job. Library updates can break your pipeline.
- Engineering time is expensive. You spend hours on scaling and quantization instead of building product features.
When you choose local inference, you own these problems:
- GPU provisioning and scaling.
- Model versioning and rollbacks.
- Hardware failure recovery.
- Security patching.
When to use Local Inference:
- You have strict data privacy requirements.
- You need to run apps offline.
- Your usage is too unpredictable for cloud pricing.
- You are doing research to save on API costs.
If these do not apply to you, you are paying a heavy tax.
How to protect your team:
- Review your architecture every month. Compare your engineering hours against API costs.
- Document everything. Write down every workaround for model issues.
- Build a cloud fallback. Do not let local failure break your entire system.
- Benchmark against competitors. API prices change constantly.
Ollama is a great tool. Do not mistake a research tool for production infrastructure. Ask yourself: What are you not building because you are busy maintaining this setup?
What local inference scenario made sense for your team? What hidden cost surprised you?
Optional learning community: https://t.me/GyaanSetuAi