As the adoption of artificial intelligence (AI) accelerates across industries, financial services are at the forefront of this transformation. From fraud detection to personalized customer experiences and real-time risk assessments, AI is driving innovation like never before. But with great compute power comes great expense. Without a clear cost strategy, AI can quickly shift from being a transformative asset to a financial liability.

At Elyxor, we’ve had the privilege of helping financial institutions deploy AI at scale while keeping cloud costs under control. Here are three key lessons we’ve learned along the way:

1️⃣ Visibility is Key

It’s impossible to optimize what you can’t see. Financial services firms often run multiple AI workloads across diverse environments, from training machine learning models to real-time inference. Without tools that provide granular insights into cloud spending, inefficiencies can go unnoticed.

To address this, we recommend:

• Leveraging cloud cost management tools that break down expenses by workload, service, and time.

• Setting up dashboards to monitor spending trends and identify cost anomalies in real time.

• Conducting regular audits to align spending with organizational goals.

Visibility is the first step to uncovering savings and ensuring that every dollar spent drives value.

2️⃣ Automate Smarter, Not Harder

Automation is a powerful lever for cloud cost optimization, but it must be done thoughtfully. For example, right-sizing instances or shutting down unused resources can lead to significant savings without sacrificing performance. One of our clients, a financial institution with a high volume of AI workloads, reduced their cloud costs by over 30% by implementing automated cost management strategies.

Key automation strategies include:

• Automatically scaling resources up or down based on demand.

• Identifying and decommissioning unused or underutilized resources.

• Using spot instances or reserved capacity for predictable workloads to reduce costs.

When done right, automation enables organizations to focus on innovation while keeping costs in check.

3️⃣ Plan for Real-Time Needs

Many AI applications in finance, such as fraud detection or trading algorithms, require real-time processing. These systems must be designed for dynamic scaling to handle spikes in demand without over-provisioning resources.

Best practices for planning real-time AI systems include:

• Building architectures that can scale horizontally, ensuring seamless handling of increased workload.

• Using serverless computing or containerized environments for elastic scalability.

• Testing and refining scaling strategies to avoid bottlenecks during peak times.

By planning for real-time needs, financial institutions can deliver high-performance AI solutions while minimizing wasted resources.

Why Cost Optimization Matters

Cloud cost optimization isn’t just a technical exercise—it’s a strategic imperative. For financial services firms, managing costs effectively ensures that AI investments deliver maximum ROI. It also enables these firms to stay competitive in a rapidly evolving industry.

At Elyxor, we’re passionate about helping organizations strike the right balance between cost efficiency and performance. Whether it’s through advanced cloud cost management tools, automation, or architectural best practices, our mission is to empower clients to make the most of their AI investments.

Ready to Optimize Your AI Workloads?

Let’s work together to unlock the full potential of AI while keeping costs under control. Contact us to learn how we can help your organization achieve its goals.