#AI pricing#Open-source AI#DeepSeek#Gemma 4#On-premise AI#AI strategy
Frequently asked questions
- Why did AI prices drop 97% in two years?
- The main driver is open source. Meta's Llama, DeepSeek and Google's Gemma 4 broke the oligopoly in which OpenAI, Google and Anthropic controlled access to the best models. On top of that, the Mixture of Experts (MoE) architecture and Google's TurboQuant method reach the same accuracy with 6× less memory and 8× more speed. DeepSeek V3.2 today costs $0.28 per 1M tokens instead of the $30 GPT-4 cost two years ago - and that level is reachable on far cheaper infrastructure.
- Should my company deploy AI on its own servers instead of an API?
- Yes, if at least one of three conditions applies: (1) you work with sensitive data (legal, medical, financial) that can't be sent to third-party servers; (2) you need millisecond response times for real-time applications; (3) you have specialized datasets you want to fine-tune a model on so it beats a generic commercial solution in your context. The mid-size Gemma 4 today runs on a standard business laptop - the infrastructure barrier has dropped.
- Which AI model is best today?
- There's no single "best" model - there's the right model for the right purpose. Claude Opus 4.6 leads in code (1549 Elo). GPT-5.4 and Gemini 3.1 Pro run neck and neck on general intelligence. Gemini 3.1 Pro has no real competitor in image and video analysis. For high-volume routine work, DeepSeek V3.2 or Gemini 2.5 Flash give an 8× better price-quality balance. A hybrid stack - cheap model for routine, premium for precision - typically cuts AI spend 60-80% with no loss of quality.
