Cloud optimization trends are more than just a flash in the pan; they’re essential building blocks that help organizations leverage the cloud effectively. Optimization is the process of managing cloud resources to improve performance, reduce costs, and align cloud spend with key business values.
This is a tall order for many organizations, particularly those with small IT teams. According to Gartner®, “By 2024, 40% of organizations will reduce the pace of their cloud adoption due to challenges managing increasing public cloud expenditure.”
Despite recent reports, organizations don’t have to scale back their efforts. By following the crucial cloud optimization trends we discuss below, organizations can stay on pace and on budget.
While often temporary by nature, these four cloud optimization trends can make an enduring impact on cloud costs. Businesses that focus on integrating these trends in 2024 will build a solid foundation that they can continue fortifying and refining year after year.
Cloud Optimization Trends
This year, more organizations are opting out of single-cloud platform environments in favor of multi-cloud environments. With cloud costs on the rise and data governance regulations becoming increasingly strict, a multi-cloud strategy is one of the best ways for organizations to stay agile.
Multi-cloud environments offer a number of benefits compared to the single-cloud platform model.
| Multi-Cloud vs. Single-Cloud Platforms | |
| Multi-Cloud | Single Cloud |
| Pros: ✅ Select individual services or products that offer the best performance. ✅ Select the most cost-effective services. ✅ Select services that protect sensitive data, including those designed to meet specific regulations, like HIPAA and the GDPR. |
Pros: ✅ Easy to track all costs and resources from a single dashboard. ✅ Easy to integrate across the organization. |
| Cons: ❌ Can be more difficult to track costs and resources across environments. ❌ Can be challenging to integrate across the organization. |
Cons: ❌ Limited to one provider’s services and products, which may not meet all of an organization’s needs. ❌ Some services may cost more than those offered by another cloud platform. ❌ Limited to the provider’s data governance services, which may not meet all regulations. |
With greater flexibility over single cloud platforms, multi-cloud environments will continue to be an important cloud optimization trend well into the future. According to Gartner, “Through 2026, 70% of organizations will expand their cloud investments with specialty cloud providers to support specific business requirements including digital sovereignty.”
Since its creation in 2019, FinOps has become one of the top cloud optimization trends, with increasing numbers of organizations adopting its core principles every year. According to the State of FinOps 2024 report, 76% of organizations are actively training their engineers in FinOps, and FinOps practitioners expect this number to rise.
FinOps is a holistic set of best practices and principles that help organizations get the greatest business value from their cloud spend. Stakeholders follow six core principles when adopting a FinOps strategy:
| FinOps Principles | ||
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Adopting a FinOps strategy is a long-term solution to cloud cost challenges. Organizations with a strong FinOps culture can:
To adopt a FinOps strategy, practitioners recommend a “crawl, walk, run” approach.
With more organizations moving to the cloud than ever before, data optimization is now one of the most pressing cloud optimization trends. Additionally, organizations using machine learning (ML) and AI have a pressing need for data optimization. ML and AI are data-hungry, relying on training data sets that live in large-scale cloud data lakes. Optimizing this data is crucial to ensure the data is accessible, useful, storage and compute-efficient, organized, and protected.
While related to FinOps, data optimization differs in scope — it focuses almost entirely on managing cloud resources to reduce costs and protect sensitive information stored in cloud data lakes. The most successful organizations use the following methods to optimize their cloud data.
| Trending Data Optimization Methods | ||
| Efficient cloud migration | Rather than “forklifting” all data directly to the cloud, many organizations identify and move only the most useful data to cloud data lakes. | |
| Compression | Lossless and lossy compression of data stored in cloud data lakes reduces the cost of storage and improves performance for network-constrained workloads. | |
| Tiering and tagging | Data tagging enables IT teams to track resource utilization, costs, and other key business metrics. Data tiering allows IT teams to prioritize useful data to store in cloud data lakes and archive less useful data, which reduces overall storage costs. |
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| Resource right-sizing | To reduce cloud costs and improve performance, organizations can identify and delete data that isn’t useful, or delete unused block storage volumes. | |
| Automation | More organizations rely on data optimization tools that automate all of the processes above, which saves time and reduces the risk of human error. | |
| PII discovery and masking | To fully unlock value from data, organizations should identify and protect sensitive information stored in cloud data lakes so that it can be used safely in analytics, generative AI, and machine learning models. | |
There are a number of steps organizations can take immediately to leverage this cloud optimization trend.
One of the most powerful cloud optimization trends is the use of AI-powered tools for monitoring and optimizing cloud resources. More organizations are turning to AI solutions, because they can identify optimization opportunities faster and more efficiently than human IT teams. This trend frees up IT teams, allowing them to focus on more complex tasks such as new product innovations.
Below are just a few tasks AI is well-equipped to handle.
| AI Cloud Optimization Capabilities |
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These are just a few AI-driven capabilities. As AI adoption increases, organizations will likely have access to more comprehensive AI-based tools.
Granica is an ideal partner for organizations hoping to take advantage of the latest cloud optimization trends. Our AI-driven data compression, visibility, and privacy services reduce cloud costs, improve performance, and help organizations reach FinOps maturity.
Granica Crunch is a data compression service that uses the power of AI to automatically compress data stored in cloud data lakes, reducing storage costs costs by up to 80%. Its byte-granular compression algorithms are adaptive, meaning it continuously scans data based on usage patterns and types.
Granica Chronicle AI is a training data visibility service that for insights into the entire cloud data lake landscape. Its genAI-powered prompts allow users to ask questions and receive detailed responses in the form of data visualizations. The tool also supports multi-cloud environments for organizations using both Amazon and Google cloud platforms.
Granica Screen is a data privacy service that makes data safe for use with analytics and AI, from training to inference. It discovers sensitive information and PII in training data sets and inference prompts and then masks that data, ensuring that no sensitive information leaks out while also reducing bias. Granica Screen automatically detects and de-identifies data based on regulatory compliance standards and custom parameters, enabling organizations to use LLMs securely.
Using these tools, organizations can stay updated on the most important cloud optimization trends and harness the cloud’s full potential.
Explore an interactive demo for Crunch to unlock your cloud optimization potential
Gartner, Predicts 2024: Unraveling Tomorrow’s Cloud Computing Landscape, by Sid Nag, Yefim Natis, et al., February 7, 2024
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