The choice between open models (Gemma, Llama, Mistral) and API models (GPT-4o, Claude, Gemini) is not about which is 'better.' It is about which fits your specific constraints — budget, privacy requirements, quality needs, and operational capacity.
This guide provides a practical decision framework with clear criteria for each option.
Quick answer
Use API models when you need the highest quality, fastest time-to-market, and can accept the per-token costs and data handling terms. Use open models when privacy is non-negotiable, you have high-volume workloads, or you need full control over the model. Many production systems use both.
- You are starting a new AI project and need to choose a model strategy.
- You are considering moving from API models to self-hosted or vice versa.
- You want to build a hybrid system that uses both open and API models.
Decision criteria
Five factors drive this decision: quality requirements, privacy constraints, cost at your expected volume, operational capacity, and time-to-market.
| Criterion | Favours API Models | Favours Open Models |
|---|---|---|
| Quality | Need the best available model | Good enough at smaller sizes |
| Privacy | Data handling terms are acceptable | Data must never leave your infrastructure |
| Cost (low volume) | Pay-per-use is cheaper than GPU hosting | Still cheaper to use API |
| Cost (high volume) | Costs grow linearly with volume | Fixed GPU cost amortised over many requests |
| Operations | No infrastructure to manage | You have ML ops capacity |
| Time-to-market | API call works immediately | Need to set up hosting, monitoring, scaling |
When API models win
API models win when quality is the top priority and your volume is moderate. The best API models (GPT-4o, Claude Opus, Gemini Ultra) are still significantly better than open models for complex reasoning, nuanced writing, and multi-step tasks.
They also win when you want to move fast. Calling an API is one line of code. Hosting a model is an infrastructure project.
When open models win
Open models win when privacy is non-negotiable, when your volume makes per-token pricing expensive, or when you need to customise the model (fine-tuning, specific quantisation, custom inference logic).
They also win for specific, focused tasks. A fine-tuned 7B model can outperform GPT-4o on a narrow task while being 100x cheaper to run.
The hybrid approach
Many production systems use both. Route simple queries to a local open model, send complex queries to an API model, and use open models for batch processing where latency is not critical.
This gives you the best of both worlds: low cost for most requests, high quality for the hard ones, and privacy for sensitive data.
Cost comparison at different scales
At 1,000 requests/day, API models are usually cheaper — the cost of hosting a GPU exceeds the API bill. At 100,000 requests/day, self-hosting becomes dramatically cheaper — the GPU cost is fixed while API costs scale linearly.
Run the numbers for your specific case. Include not just GPU costs but also engineering time for setup, monitoring, and maintenance.
Worked example: choosing for a document processing startup
A startup processes legal documents. They start with Claude API for development speed. At 500 docs/day, the API costs $150/day. When they reach 5,000 docs/day, they move simple extraction tasks to a local Gemma model ($50/day GPU) and keep Claude for complex analysis ($200/day). Total: $250/day instead of the $1,500/day it would cost on API alone.
Common mistakes
- Choosing based on philosophy ('open is better') instead of practical criteria.
- Not accounting for operational costs of self-hosting.
- Using the most expensive API model for every query when a cheaper one would work.
When to use something else
For running open models locally, see running Gemma 4 on your own machine. For reducing API costs without switching models, see cutting AI API costs.
Frequently asked questions
When should I use API (closed) models?
When you need top quality and fast time-to-market and can accept per-token cost and the provider's data terms. They still lead on complex reasoning and nuanced writing.
When do open models win?
When privacy is non-negotiable, volume makes per-token pricing expensive, or you need control such as fine-tuning, quantisation or custom inference. You trade some quality and ops effort for control and unit economics.
What factors should drive the decision?
Your quality bar, privacy and compliance needs, cost at your real volume, operational capacity to run inference, and time-to-market. Weight them for your app rather than following a trend.
At what volume do open models pay off?
When the fixed cost of hosting and GPUs amortises below your per-token API bill — typically high, steady volume. For spiky or low volume, APIs are usually cheaper all-in.
Can I use both?
Yes, and many production systems do: open models for high-volume or sensitive paths, API models for the hardest queries. Route by sensitivity and difficulty.
What is the hidden cost of open models?
Operations. Serving, scaling, GPU availability, evals and updates are now yours, so budget engineering time, not just hardware, before committing.
Related guides on this site
These guides cover local model setup, cost reduction, and model comparisons.