AI Answer · Model Choice

Should I use OpenAI or an open-source LLM?

Quick facts

• Hosted APIs win on time-to-market, quality, and zero ops — start there.
• Open-source models win on data control, cost at high volume, and customization.
• "Free" open-source still costs GPUs, ops, and engineering time to run.
• You can abstract the model behind your own interface and switch later.
• Most products should ship on a hosted API first and revisit only with real data.
• Privacy and regulatory needs are the strongest reason to self-host, not cost alone.

When a hosted API (OpenAI / Anthropic / Google) wins

• You need the highest quality reasoning with the least effort, today.
• Your volume is low to moderate and per-token pricing still pencils out.
• You do not want to run GPUs, handle scaling, or staff ML operations.
• You want automatic access to newer, better models as they ship.
• Your data terms are satisfied by the provider's DPA and retention settings.

When a self-hosted open-source model wins

• Data cannot leave your environment for privacy or regulatory reasons.
• Volume is high enough that token costs dwarf the cost of running your own.
• You need deep customization or fine-tuning the hosted option does not allow.
• You require full control over model versioning and behavior over time.
• Latency or offline/on-prem requirements rule out a remote API.

The total-cost reality

Open-source models are often described as the cheaper option, and at high volume they can be. But the bill changes shape rather than disappearing. Instead of paying per token, you pay for GPU capacity (often idle between requests), the engineering to deploy and scale inference, monitoring, security patching, and the ongoing work of keeping up with better models. For low and moderate volume, a hosted API is almost always cheaper once you count engineering time. Self-hosting typically wins on cost only past a meaningful traffic threshold — and you should model that threshold before committing.

Don't marry one model

The field moves monthly. The durable architecture is one where your application talks to a thin internal interface, and the model behind it is swappable. That lets you start on a hosted API, A/B a cheaper or open-source model later, or split traffic — without rewriting features. Building that abstraction up front costs little and protects you from both price changes and the next better model.

How QUANT LAB USA approaches it

QUANT LAB USA defaults to a hosted API for speed and quality, abstracts the model so it stays swappable, and recommends self-hosting only when privacy, volume, or customization genuinely justify the operational burden. The decision is paired with the data-safety and cost questions — see is my data safe with an AI vendor, the cost to build an AI chatbot, and the overall approach to adding AI to a product.

Sources and methodology

This comparison reflects QUANT LAB USA's engineering practice for US clients. For service detail see quantlabusa.dev/services, and the glossary defines inference, fine-tuning, and tokens. Model names are referenced neutrally; no placement is paid.