How VirtueCloud Helped a Manufacturing Client Slash 60% of Their AI/ML Costs with Amazon SageMaker

Many industries are rapidly adopting to GenAI/ML competencies to be ahead in the competition. Though one of the biggest hurdles is not building the models, it’s managing the mounting infrastructure costs that come with scaling AI solutions.

One of our enterprise manufacturing client was struggling with ballooning AWS bills while running ML workloads for CRM and ERP optimisation. Their infrastructure, while technically functional, was oversized, underutilised and difficult to scale efficiently.

We helped them optimize their Amazon SageMaker setup, using a focused 5-point strategy that resulted in over 60% cost savings across their ML stack:

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1. Rightsizing SageMaker Instances

Analysed training and inference usage data to downsize from high-cost GPU instances to more efficient CPU-based alternatives for non-intensive models. This alone cut 20% of their compute costs.

2. Smarter Model Selection with JumpStart

Instead of defaulting to heavy LLMs, we guided the client toward pre-built models via SageMaker JumpStart that met their accuracy needs at a fraction of the cost. This saved an additional 15% in dev costs.

3. Leveraging Machine Learning Savings Plans (MLSP)

We committed to 1-year MLSPs, allowing us to optimize spend across training, batch transforms, and SageMaker Studio. These flexible plans helped the client reduce their long-term cloud commitment costs by up to 64%, especially as they scaled their AI operations.

4. Managed Spot Training

We shifted their model training to SageMaker Spot Instances This alone cut training compute expenses by nearly 90% compared to On-Demand. Combined with AWS Graviton processors, the result was significant — approximately $4,000/month saved during peak experimentation periods. Interruptions were handled via automated checkpoints to ensure progress wasn’t lost.

5. Smart Inference Strategy

Real-time inference was only used where absolutely necessary. For batch jobs, we used SageMaker Batch Transform and Asynchronous Inference, which scale to zero when idle, reducing waste and cost.

We applied a hybrid approach:

• Serverless Inference for intermittent CRM tasks (costs scale to zero when idle)
• Batch Transform for nightly ERP processing jobs
• Real-time Inference for high-priority customer interactions

This blended approach balanced performance and cost-effectiveness, slashing inferencing expenses by 40% on average.

📉 Results?

• 60% reduction in monthly AWS SageMaker costs
• Faster model iteration times
• Freedom to innovate without fearing the next billing cycle

This transformation was not just about saving money, it was about giving our client the freedom to scale AI across departments, from production forecasts to customer experience, without being held back by technical debt.

Are you a manufacturer or enterprise struggling with AI/ML costs? Let’s talk !

VirtueCloud brings you the blend of cloud-native efficiency, AWS expertise, and real-world business impact.