Local AI vs. API: When to Use Ollama Instead of OpenAI
A cost, latency, and privacy comparison of running local LLMs with Ollama versus calling the OpenAI API — based on real client projects.
“Should we use the OpenAI API or run a model locally?” is the question we field most often. The honest answer is: it depends on three variables — cost, latency, and privacy. Here is the framework we use to decide, drawn from shipping both architectures in production.
Cost
Hosted APIs like OpenAI charge per token. That is wonderful at low volume and brutal at scale. Ollama running an open model such as LLaMA or Mistral flips the equation: a fixed hardware cost, then effectively free inference. If you process millions of tokens a day, local inference often pays for the GPU within weeks.
Latency
Local models remove network round-trips and rate limits — great for tight, interactive loops. But a frontier hosted model can still be faster per token than a large local model on modest hardware. Match the model size to your GPU; a quantized 8B model on a consumer card is snappy, a 70B model is not.
Privacy and compliance
This is frequently the deciding factor. When data cannot leave your infrastructure — patient records, legal documents, proprietary code — local inference is not an optimization, it is a requirement. We covered how this shapes a private knowledge assistant in our RAG pipeline guide.
A note on quality
Frontier hosted models still lead on the hardest reasoning tasks. Open models have closed the gap dramatically for summarization, extraction, classification, and RAG — which is the majority of real product work. Benchmark on your task, not a leaderboard.
The decision matrix
- Choose a hosted API for low volume, fast iteration, or when you need top-tier reasoning today.
- Choose local (Ollama) for high volume, strict privacy, predictable cost, or offline deployment.
- Go hybrid — route easy requests locally and escalate hard ones to an API. Best of both worlds.
Key takeaways
- Cost favors local at scale; APIs win for low volume.
- Privacy requirements can make local the only option.
- Benchmark on your real task before committing.
We design AI systems around your actual constraints, not hype. Tell us about your workload and we will recommend the right architecture.