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The cloud has become the go-to platform for building and deploying Artificial Intelligence (AI) solutions. Companies like Tesla, with its self-driving car technology, and Palo Alto Networks, with its AI-powered cybersecurity tools, are just a few examples of businesses leveraging cloud AI to transform their products and services. This rapid adoption has driven a surge in demand for cloud AI services. This growth has also brought a renewed focus to managing the associated cloud spending, which means paying attention to how to manage workloads with FinOps for AI.
Cloud AI deployments can be complex. The dynamic nature of AI training and inference tasks can lead to unpredictable resource consumption. Coupled with the need for granular cost visibility, this can result in escalating cloud costs and inefficient resource utilization. To avoid these pitfalls, consider adopting a FinOps framework for your Cloud AI deployments, just as you would with any other cloud workload. The ultimate goal is to ensure accountability and efficient resource usage.
How can FinOps help manage AI cloud costs?
FinOps provides a framework for establishing clear cost ownership and accountability within your organization. This involves defining cloud cost segmentation and allocation models and assigning ownership of cloud spending to specific teams and services. FinOps encourages teams to understand the value proposition of their AI workloads and optimize resource utilization.
FinOps emphasizes the importance of cost monitoring and reporting tools. These tools provide real-time insights into cloud spending, allowing you to identify and proactively address potential cost anomalies and identify cost-saving opportunities. This is particularly crucial when exploring and adopting new machine learning and cloud AI services, enabling cost-conscious experimentation and innovation.
Applying the FinOps Framework to AI workloads
There are practical ways to understand, manage, and predict your AI workloads, the first being to understand AI workload pricing.
AI workload pricing typically falls into two main categories:
- Pay-as-you-go (Pay-per-token): This serverless option charges for the number of characters, images, audio files, or datasets processed. You often pay for the output as well. It’s crucial to understand the equivalence between items processed and tokens used and to test your forecasts against actuals to understand any variance.
- Self-hosting: This route offers more freedom and privacy. You pay for the infrastructure used regardless of the amount of data processed. Therefore, you need to monitor how efficiently the resources are used.
When possible, one suggestion is to evaluate your proof-of-concept under the Pay-as-you-go and Self-hosting models to see which model makes more financial and engineering sense.
Take control of your AI cloud costs: 5 tips to get started
1. Start small and experiment
Before scaling up projects, begin with pilot cloud AI deployments to better understand cost drivers and resource utilization patterns. This will allow for early identification and mitigation of potential issues around cloud spend.
2. Leverage cost segmentation
Implement clear labeling and tagging practices to categorize cloud AI costs. This enables granular analysis of spending patterns and pinpoints areas of high expenditure for further optimization. Every dollar spent should be traceable to a responsible individual who can act on optimizations or recommendations.
3. Set up budget alerts and cost anomaly detectors
Ensure that you have a budget alert for your project and a cost anomaly detector. Budget alerts are your friend in preventing unintended runaway costs at the end of the month. Some tools monitor costs daily by SKU, allowing you to identify misusage quickly.
4. Conduct regular reviews and optimizations
Schedule regular reviews of cloud AI workloads and associated costs with the responsible parties. This allows teams to identify optimization opportunities based on actual usage patterns and adjust resource allocation accordingly.
5. Embrace continuous improvement
Establish a refinement process for cloud AI deployments. This involves reviewing project outcomes and adapting resource allocation strategies based on performance data and unforeseen challenges.
The TLDR
Cost visibility and control are essential for successful entry into the AI field. While understanding the cost of AI workloads can be complex, applying proven FinOps frameworks can help. Ensure thorough labeling and tagging. Follow up to ensure that your spending forecast for a proof of concept aligns closely with actuals. Set up budget alerts and cost anomaly detection systems. Finally, embrace workload reviews and look for ways to improve efficiency.
Developing a solid FinOps strategy with SADA
SADA FinOps experts put your priorities at the forefront of your strategy to keep costs low while maximizing the return on your cloud spend. As AI continues to drive innovation, making sure that your investments in this high growth area are leading to strong returns is key. Feel free to reach out to schedule a discovery call with SADA FinOps experts today and get started on your streamlined FinOps strategy.
