Gain deep visibility into the costs associated with training, inference, and data processing for AI workloads.
- Break down costs by model, dataset, and cloud region
- Identify high-cost operations and resource inefficiencies
Maximize the performance of AI models while minimizing costs through intelligent resource management.
- Optimize GPU and CPU utilization
- Auto-scale resources to match workload demand
Leverage advanced techniques to streamline AI model training and inference processes.
- Identify redundant training iterations and eliminate waste
- Optimize hyperparameters and model architectures for efficiency
Reduce storage costs for large AI datasets without compromising data availability.
- Tiered storage options for cost-effective data management
- Automated data pruning to eliminate unused or outdated datasets
- Predictive AI Workload Scaling
Use AI-powered analytics to predict workload demands and dynamically adjust resources.
- Cost and Performance Balance
Optimize resource allocation to reduce costs without impacting model accuracy or performance.
- Real-Time Insights for AI Teams
Enable data scientists and engineers to monitor costs and performance metrics directly, ensuring alignment with business goals.
Challenge:
A fintech company faced skyrocketing cloud costs due to the training of multiple large language models.
Solution:
By using Cloudfit’s FinOps for AI, they optimized GPU usage, reduced idle compute time, and streamlined data storage practices.
Result:
Achieved a 40% reduction in training costs while maintaining model performance and reducing time-to-deployment.
Cloudfit offers tailored services to ensure your AI workloads run efficiently and cost-effectively:
AI FinOps Consulting:
Create a customized strategy for managing AI workloads in the cloud.
Managed FinOps for AI:
Continuous monitoring, cost optimization, and AI-specific insights.
Join global innovators who trust Cloudfit to transform their cloud management.