Today it is rare to find a company with AI in production that uses a single model. One team tries Claude for one task, GPT for another, and an in-house model for whatever has to run on-premise. Each decision makes sense on its own. Answering for the whole picture —what it costs, how fast it responds and who decided what— requires a different perspective.
What happens when each model is managed separately?
Every standalone model means a different bill, a different response time and a different blind spot for whoever has to answer for the governance of those systems to a regulator or a customer. Multiply that by the number of models that coexist in any organization with AI in production today, and the need for a central layer to bring order to all of it grows quickly, even for experienced engineering teams.
The three factors that decide whether it scales
Cost: a central criterion steers the choice toward the most efficient model for each task, regardless of which one is easiest to integrate. Latency: each model responds at a different pace, and that affects the end-user experience if nobody manages it explicitly. Governance: a centralized log makes it possible to state precisely which model made which decision and based on what information.
What does an orchestration layer do?
An orchestration platform such as OrchestAI, from Xcapit, centralizes those three decisions: it routes each task to the right model based on cost and need, keeps an audit log of what happened, and lets you require that data be processed inside the company's own data center.
It is designed especially for regulated industries —finance, energy, government—, where the question of which model decided what is a core requirement in front of regulators, customers and auditors.
Before adding one more model
If your organization already has more than one AI model in production, it is worth solving the governance layer before adding a third or a fourth. It is considerably simpler to set the criteria from the start than to rebuild them later, once several teams are already making decisions independently.
How are you handling governance and costs today when several AI models coexist in production?
Fernando Boiero
CTO & Co-Founder
Over 20 years in the tech industry. Founder and director of Blockchain Lab, university professor, and certified PMP. Expert and thought leader in cybersecurity, blockchain, and artificial intelligence.
Ready to leverage AI & Machine Learning?
From predictive models to MLOps — we make AI work for you.
You Might Also Like
Shadow AI Is Already in Your Company: How Enterprise IT Can Take Back Control Without Saying No
Your employees are already using ChatGPT from their phones — without policy, without audit, without governance. This article lays out the three tensions enterprises face with AI adoption, why current solutions fall short, and the architecture that lets IT say yes to AI without losing control: multi-LLM orchestration, signed audit chains, and on-premise deployment.
If everyone has Artificial Intelligence, where will the competitive advantage be?
AI is no longer a barrier to entry: today it is within reach of companies of every size. Why the difference is no longer about using AI, but about how it is used and the quality of the architecture that integrates it.
Will Artificial Intelligence replace developers? Why software engineering matters more than ever
AI automates code generation, testing and documentation, but it does not replace engineering. Why, as writing code becomes easier, the value of those who design architectures and understand the business goes up.