Every AI model is good at something different. That isn’t a marketing cliché — it’s the reality companies collide with three months into using AI.
Claude Sonnet handles long documents and dense legal language better. GPT is faster and cheaper for simple tasks. Gemini is great at data analysis and tables. Open-source models via Ollama can drop costs to zero on high-volume tasks — but require your own infrastructure.
The problem with most AI platforms
Most AI platforms on the market pick the model for you. “We use model X. Don’t ask why.” You pay, even if the price has soared, even if something better appeared, even if your task would run on a model ten times cheaper.
Worse — when a newer, better model appears (and they do every few months), you wait for the vendor to integrate it. Sometimes forever. Vendor lock-in in a new form: locked to one AI architecture.
How we solve it
In Ragen you have all the main models available from one place: OpenAI (GPT-5.4, GPT-5.4 nano, GPT-5.3 chat), Anthropic (Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5), Google (Gemini 3 Flash, Gemini 2.5 Flash), and open-source models via Ollama that you can host yourself or with us.
You decide:
- A customer-facing website chatbot? You turn on a cheaper model, because most questions are simple and typical. You save a large chunk of the cost.
- A contract-analysis assistant in a law firm? You set Claude Opus, because the quality is worth every euro. Legal interpretation accuracy decides everything.
- An internal company assistant with heavy usage? You put an open-source model on your own server and pay zero per conversation. With 50 employees using AI daily, that’s real money.
- Financial report analysis with tables? Gemini, because it handles structured data best.
The administrator sets which models are available for which organisation, for which assistants, with which limits. The user switches with one click — or doesn’t have to at all, because the admin set a default for their role.
Three things this changes in the business
End of vendor lock-in. A new, better model arrives? You add it in the panel. No waiting for a platform update, no contract renegotiation, no data migration. OpenAI raises prices? You switch high-volume workloads to a cheaper competitor.
Continuity of access. When one provider has an outage, the system switches to another automatically. Zero downtime, zero intervention from you. One vendor = one risk. Multiple vendors = stability.
Cost optimisation stops being fiction. Instead of paying one averaged price for everything, you pay for each task what it actually costs. Simple questions go to cheap models. Complex ones — to expensive ones. Savings measured in tens of thousands of euros per year on mid-sized deployments.
Who this matters most to
Every CTO who remembers how cloud vendor lock-in cost their company a six-figure sum. Every CFO who noticed the OpenAI invoice is growing faster than revenue. Every company that wants to use AI for years, not just the next quarter.
This is the difference between “I’m buying an AI platform” and “I’m buying flexibility for years”.