In the AI gold rush era, where new large language models (LLMs) and other AI solutions emerge almost weekly, modern organisations are increasingly eager to employ machine learning.
With the recent appearance of DeepSeek-R1 and Alibaba’s Qwen2.5-Max models, which have shaken up the market, it’s easy for an enterprise to get lost in the decision-making process of which solution to use.
“The more models appear, the harder it becomes to keep track of them all — let alone experiment and deploy them effectively,” says Dr Mantas Lukauskas, AI evangelist at nexos.ai and AI engineer at Hostinger. “As a result of the diverse AI ecosystem, organizations often want to mix and match, quickly adopting the latest model to stay on the cutting edge.
“However, how do you effectively manage a complex, ever-changing array of AI models without tangling yourself in constant updates, skyrocketing costs, and compliance headaches?”
Challenges in the rise of AI models
“Organisations that simply rely on one big LLM will miss opportunities to optimize performance, reduce costs, or deliver unique capabilities to their customers. However, for those excited to try out each new AI release, the multi-model ambitions quickly become technically and logistically complex,” explains Lukauskas.
“Having, let’s say, five different third-party application programming interfaces (APIs) — each with its own usage rules, security considerations, pricing models, and performance trade-offs — can become unmanageable.”
He highlights the following challenges enterprises need to address:
- Complexity. Each model upgrade or usage policy change can require modifications across an organization’s codebase.
- Scaling costs. Relying on a single high-cost model for all tasks can unnecessarily inflate AI bills, whereas selectively deploying cheaper or specialized models might save money.
- Security and compliance. Sending sensitive data to multiple external services magnifies the risk of breaches or compliance violations.
- Performance variance. Different LLMs excel in different tasks. One might be superior for coding support, whereas another might shine at creative writing. Without an easy way to route specific tasks to the best-suited model, an organization may lose out on significant performance gains.
Solution: AI orchestration
AI orchestration might be the best solution for enterprises wanting to adapt in the AI gold rush times, according to Lukauskas.
By using a centralized platform that can speak to multiple AI providers via a single interface, businesses can maintain control over usage, ensure compliance, handle dynamic scaling, and adopt whichever new model emerges without overhauling their existing infrastructure.
The advantages of an AI orchestration platform are as follows:
- Flexibility to adopt or discard models quickly.
- Resilience through automatic load balancing and fallback mechanisms.
- Better ROI by caching repeated queries and choosing the most cost-effective model for each task.
- Stronger security and compliance controls in a single hub.
“The real competitive advantage is to stay nimble. AI orchestration platforms ensure that an organization can quickly integrate any new technology, test it, and scale it,” says Lukauskas. “The customer is never locked into a single model or provider because they can always switch to whichever solution offers the best outcome at any given moment.”
According to Lukauskas, the AI landscape will only get more crowded. Having an orchestration layer in place is one of the smartest investments an organization can make to stay agile, harness the best of every model, and remain competitive on this ever-evolving frontier.