The AI Gold Rush is Draining Your Cloud Budget, and Here’s the Real Culprit

The AI “gold rush” is here as companies compete for the advantages that AI agents and automated workflows can bring to their business. They’re pouring money into GPUs, expensive model licensing, and flashy tooling to squeeze every advantage that they can out of the technology.

However, the focus on compute causes many organizations to overlook the hidden costs of the AI race. AI models need massive amounts of unstructured data to function, both during the training phase and to perform inference. While the cloud is the logical place to store this data, moving and managing it causes many organizations to rack up huge and unexpected cloud bills.

AI is the future, and companies need to be able to access and process their data to extract critical insights and value. Achieving the organization’s AI goals requires AI cloud cost optimization.

Data-heavy AI workloads mean you're probably paying too much for storage

AI is famously data hungry. The LLMs in common use today were trained by scraping the public Internet and distilling the information that it provides into AI models that can answer queries and perform inference.

As organizations develop AI pipelines, their GenAI, multimodal workflows, and RAG architectures need access to vast amounts of data to provide real value. Worse, this data is largely unstructured, combining external data with an organization’s institutional knowledge and intellectual property to create a competitive edge.

Access to this data is essential at every stage of the AI lifecycle. AI models ingest massive amounts of data and use it for training, compressing it into the weights of the AI model. Access to these weights and various data sources is also important for model versioning and performing inference during daily use of the AI system.

Often, corporate AI budgets are focused on the compute side, ensuring that tools and workflows have the speed and capacity to perform training and inference. However, the need for consistent, high-performance access to these massive datasets can dramatically inflate the cost of AI and blow AI budgets.

The hidden cost centers you’re probably overlooking

Some costs of AI workloads are obvious, such as investment in GPUs, model licensing, tooling, and the base storage costs for AI datasets. However, many companies overlook hidden fees associated with data storage and management for AI solutions. Let's dig into some of the drivers of hidden costs.

Frequent data movement

Often, multicloud environments mean that storage and compute are not colocated as companies choose the best solution for each use case. As a result, organizations can incur significant egress fees as data moves between data lakes, archives, and GPU clusters.

Egress and API fees

AI systems make frequent data requests, especially when performing training and inference. If a data storage provider charges for data egress or API requests, these fees can multiply quickly to be an unpleasant surprise on monthly cloud bills.

Excess storage use

AI data is frequently unstructured, making it difficult for organizations to know what data they have and where it is located. This can result in excess storage usage and fees due to redundant copies of data.

Inefficient metadata

A lack of structured data and efficient metadata also means that an organization will struggle to locate the data it needs within its cloud storage. As a result, AI systems may be forced to perform full-volume scans and over-fetching. This may incur additional fees for data access and decreases the efficiency of AI workflows.

Many of these hidden fees relate to the core functionality of AI-enabled workflows, but can be difficult to predict and manage. They can silently eat into AI budgets, resulting in overspending or forcing cuts in other elements of a corporate AI strategy.

Rethink AI storage: simple, predictable, performance-driven

Managing the hidden costs of AI requires applying the same strategic mindset to data storage design as compute. Some best practices for managing the costs of AI storage include:

• Simple hot-style storage: It can be difficult to predict what data an AI system may need to access and how frequently it needs to do so. Hosting AI data on high-performance, always-available hot storage eliminates potential retrieval delays or the risk of pricing surprises due to unexpected access patterns.

• Flat-rate billing: API and egress fees are a common cause of AI overspend as companies pay for data access and movement between storage and compute environments. Cloud storage with a flat-rate billing model offers predictability and eliminates surprises on monthly cloud bills.

• Immutable storage: AI models depend on various high-value data, including model inputs, weights, provenance, and audit records, which are prime targets for ransomware. Immutable data storage helps to secure this data by eliminating the risk of malicious modifications.

• Metadata indexing and searchability: AI data is largely unstructured, which can make it difficult to locate the information needed for training or inference. Intelligent metadata indexing helps to locate the required data, reducing costs associated with redundant access and data discovery.

Many companies struggle with cloud costs that are difficult to predict, leading to overspend on AI storage budgets. Implementing intelligent storage design can help to both avoid hidden fees and enhance the operational efficiency of AI-powered workloads via more efficient data access.

The bottom line impacts of smarter AI storage

An intentional, intelligent design for cost-effective AI storage enables organizations to maximize the business impact of their AI investments. Key benefits of focusing on AI storage include the following real-world benefits.

Cash flow clarity

AI budgets are commonly drained by hidden and unpredictable fees, like those for API usage and data egress. AI systems make frequent data access requests, often of many small pieces of data. Optimized AI data storage enables companies to better forecast their spend on AI storage.

Operational efficiency

Unstructured, unindexed data slows data discovery and forces redundant data access. With only a vague idea of where data is located, a system needs to download all of it to search through, which takes time and can incur significant access fees. Metadata indexing can help AI tools to find the data that they need more quickly, enabling faster iterations and improved engineer productivity.

Strategic resilience

Immutable, indexed storage protects data against unauthorized modifications and simplifies data access. Without it, organizations may be vulnerable to ransomware or be unable to find the data required for regulatory compliance, audits, and retraining of AI models.

Conclusion

AI adoption has become a race, and there are definitely winners and losers. Some companies “strike gold” by upgrading their GPUs and investing in compute, taking advantage of faster data processing to gain a competitive advantage. Others find themselves held back by storage strategies that quietly drain their budget through hidden access and retrieval fees.

When designing or reviewing an AI strategy, it’s vital to audit data storage workflows, such as data movements, fees, and metadata design, to identify potential inefficiencies or hidden costs. When doing so, consider whether the storage layer of your ecosystem is providing value to the business or is pulling resources away from other elements of your AI strategy.