One of the most important decisions when adopting AI is choosing between open source models (Llama 4, Mistral, DeepSeek) or proprietary ones (GPT-5, Claude 4, Gemini 3). Each option has advantages depending on your use case.
Advantages of open source models
Total control: You can download the model, host it on your infrastructure, and customize it without restrictions.
Privacy: Data never leaves your servers. Essential for regulated industries like healthcare, finance, or defense.
Long-term cost: No per-token costs. Once invested in infrastructure, marginal inference cost is minimal.
Customization: You can fine-tune with your data without API costs or worrying about limits.
Advantages of proprietary models
Superior quality: GPT-5 and Claude 4 typically outperform open source models on general benchmarks.
Zero infrastructure: No need for GPUs, MLOps teams, or scaling servers.
Maintenance: The provider updates the model, improves security, and optimizes performance.
Support: Access to documentation, examples, and professional support teams.
When to choose open source
Large inference volumes (over 1M queries/day), sensitive data that cannot leave your infrastructure, deep model customization, and teams with technical capacity to maintain infrastructure.
When to choose proprietary
Small volumes, rapid prototyping, teams without ML infrastructure, and when model quality is the most critical factor.
Hybrid model
Many companies use both: proprietary models for development and prototyping, and fine-tuned open source models for production with large volumes.
Cost comparison
For 100K queries/day: Proprietary API can cost $3000-10000/month. Open source on own infrastructure: $1000-3000/month (amortizing GPUs).
There is no one-size-fits-all answer. At Vynta we analyze your volume, privacy requirements, and technical capacity to recommend the best strategy. Contact us for AI model selection consulting.