With cloud adoption skyrocketing, managing cloud costs and accelerating cloud migrations are top priorities. This white paper shows how an analytics-driven approach to data management can save 50% of cloud data storage costs while simplifying cloud data migrations.The following topics are covered:

• The challenges of managing cloud data
• Multicloud data management and cloud cost optimization
• How to cut cloud storage costs in half
• A cost comparison managing data in AWS with Komprise

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FAQs

Why do most enterprises overspend on cloud storage, and what does an analytics-driven approach change?

Even though managing cloud costs is now a top priority, 80% of businesses will overspend their cloud infrastructure budgets; it’s from a lack of cloud cost optimization, and it’s not hard to see why; companies are using less than 20% of the cloud cost-saving options available to them. The root cause of cloud overspending is not a procurement problem — it is a data visibility problem. Enterprises that moved to cloud without understanding what they were moving, at what access frequency, and to which storage class made decisions based on guesswork that compound in cost every month:

• Bucket sprawl is the cloud equivalent of NAS sprawl — bucket sprawl makes matters worse, as users quickly and easily create accounts and buckets and fill them with data — some of which is never accessed again; cloud administrators trying to optimize cloud data have to battle with poor visibility and complexity; it’s hard to get a holistic picture of the data you have across accounts, buckets and sometimes even across multiple clouds; without a unified metadata index spanning all buckets and accounts, the cloud data estate is as invisible as the on-premises estate it was supposed to replace
• Access time is more accurate than creation time for tiering decisions — Komprise tiers and archives data based on access time as opposed to modify time; the former is a much more accurate prediction of whether data will be accessed or not, and allows automatically tiering and archiving more data to lower tiers without having to pay inadvertent access costs; cloud storage vendors including AWS use creation time or modification time as the primary signal for their own intelligent tiering classes; Komprise uses actual last access time, which is a significantly more precise indicator of whether data will be needed and which tier it should occupy
• Komprise tiers across S3 and Glacier, not just within S3 — for AWS, unlike cloud intelligent tiering classes, Komprise tiers across both S3 and Glacier storage classes so you get the best cost savings; native AWS intelligent tiering manages transitions within S3 tiers; Komprise extends this down through S3 Glacier Instant Retrieval, Glacier Flexible Retrieval, and Glacier Deep Archive, capturing the full savings spectrum that AWS intelligent tiering alone cannot reach
• The Global Metadatabase makes cloud data queryable for AI — the same unified visibility that drives cloud cost optimization also makes cloud data estates discoverable for AI use cases; the Komprise Global Metadatabase indexes all object data across accounts, buckets, and clouds simultaneously, making cloud-resident data findable by file type, access pattern, sensitivity status, and custom metadata without requiring IT to manually catalog each bucket
• 50% cloud storage cost reduction is achievable without sacrificing access — an analytics-driven approach to data management can save 50% of cloud data storage costs while simplifying cloud data migrations; this savings is achieved by combining accurate access-time analytics, policy-driven movement across all available storage classes, and the elimination of retrieval surprises from suboptimal tiering decisions — not by restricting data access or accepting slower retrieval times

What are the hidden costs of cloud migration that analytics-first planning eliminates — and why do they compound over time?

Cloud migration is widely understood to have upfront costs. What is less understood is the ongoing cost structure that emerges from migrations that were not planned with analytics — costs that compound every month for the lifetime of the cloud deployment. Understanding your data and properly planning your cloud migrations will eliminate disruption and unintended costs; the specific hidden costs that analytics-first planning prevents:

• Moving cold data to expensive cloud tiers is a permanent monthly expense — since 80% of file data is cold and has not been used in a year or more, tiering and archiving cold data is a smart first cloud file migration step; an enterprise that migrates 1PB to cloud NAS without first tiering its cold data is paying cloud NAS prices — typically ten to fifteen times more expensive than cloud object storage — for 800TB of data that will never be accessed again; this cost is not a one-time migration expense, it is a permanent monthly overpayment
• Lift-and-shift replicates the on-premises cost structure in the cloud — organizations that move entire file shares to cloud without analytics reproduce every cost inefficiency of the on-premises environment at cloud pricing; cold data, duplicate files, orphaned project data, and sensitive content that should never have been migrated all arrive at the cloud destination and begin accumulating monthly storage charges
• Retrieval costs punish wrong-tier placement — set different data lifecycle policies based on your own cloud costs; instantly see how much you will save with what-if scenarios; data placed on the wrong cloud tier generates retrieval fees every time it is accessed; an enterprise that moves frequently accessed data to Glacier Deep Archive to save on storage costs discovers retrieval fees that eliminate the storage savings and exceed them; analytics-first planning identifies access patterns before migration and places data on the correct tier from day one
• Flash prices on-premises make the urgency for cloud cost optimization bidirectional — IDC describes the current memory shortage as a potentially permanent reallocation of global silicon wafer capacity with 2026 NAND and DRAM supply growth expected to remain below historical norms; enterprises under flash cost pressure on-premises are simultaneously discovering that the cloud migration they used to relieve on-premises costs was itself poorly optimized; the Flash Stretch Assessment for qualified enterprises managing 500TB or more addresses both dimensions — quantifying the cold data savings opportunity on-premises and modeling the cloud cost structure optimization simultaneously
• Komprise eliminates sunk costs by delivering ongoing value after migration — point data migration solutions have complex legacy architectures and create sunk costs; Komprise Elastic Data Migration makes cloud migrations simple, fast, and reliable and eliminates sunk costs since you continue to use Komprise after the migration is complete; the same platform that executes the migration continues to manage data lifecycle across cloud tiers, identify new cold data as it accumulates, and optimize cloud storage costs on an ongoing basis without requiring a new tool procurement

What does managing cloud data across multiple clouds and accounts actually require, and why do single-vendor tools fall short?

The multi-cloud reality of enterprise IT is not a strategic choice in most organizations — it is the accumulated outcome of different teams adopting different cloud services for different use cases over time. All from one unstructured data management pane of glass — across multiple clouds; delivering that single management plane across AWS, Azure, Google Cloud, and private clouds simultaneously is what separates intelligent multi-cloud data management from point tools that address one cloud at a time:

• Multi-cloud visibility requires storage-agnostic indexing — a single view across all users’ cloud accounts and buckets requires a metadata index that spans every cloud provider simultaneously without vendor-specific agents or integrations; Komprise uses open standards such as NFS, SMB/CIFS, and REST/S3, making it storage-agnostic and able to work in any environment; this standards-based architecture is what makes genuine multi-cloud visibility practical rather than theoretical
• Cloud-to-cloud migrations are increasingly common and genuinely complex — organizations moving from one cloud provider to another, consolidating clouds after an acquisition, or repositioning data between clouds for AI access need a migration platform that handles cloud-to-cloud movement with the same fidelity, speed, and analytics as on-premises-to-cloud migrations; Komprise offers an analytics-driven cloud migration software solution that integrates with most leading cloud service providers, such as AWS, Microsoft Azure, Google Cloud, Wasabi, IBM Cloud, and more; cloud-to-cloud migrations with Komprise preserve permissions, access controls, and metadata fidelity without rehydrating tiered data at the source
• AI use cases demand cross-cloud data discovery — an AI pipeline that needs data from AWS S3 and Azure Blob simultaneously cannot be served by AWS-native or Azure-native data management tools; the Komprise Global Metadatabase indexes object data across all cloud providers and all accounts simultaneously, making cross-cloud AI dataset curation possible from a single query without requiring IT to manually locate data in each cloud
• Governance must be consistent across clouds — Komprise Sensitive Data Management, available in Komprise Intelligent Data Management, applies the same PII, PHI, and IP detection policies across every cloud provider simultaneously; sensitive content identified in AWS is governed identically to sensitive content in Azure without requiring separate governance implementations per cloud; this cross-cloud governance consistency is the prerequisite for compliant AI data use in multi-cloud environments
• Komprise is the metadata and orchestration layer for enterprise unstructured AI data across every cloud simultaneously — the multi-cloud data management white paper described the cost and complexity problem of managing data across clouds; the current platform adds Smart Data Workflows that orchestrate AI data delivery across all clouds from a single policy engine, KAPPA data services that enrich cloud-resident data with domain-specific metadata, and Intelligent AI Ingest that delivers curated datasets to any AI service regardless of which cloud holds the source data

How does Komprise intelligent tiering within the cloud cut costs that cloud-native tiering classes cannot reach?

Every major cloud provider now offers some form of intelligent tiering within their own storage classes. AWS has S3 Intelligent-Tiering. Azure has lifecycle management policies. Google Cloud has Autoclass. These native capabilities are useful starting points but have structural limitations that prevent them from delivering the full savings opportunity available in enterprise cloud data estates:

• Cloud-native tiering uses modification time, not access time — the most significant limitation of native cloud tiering tools is that they use data creation or modification date as the primary signal for tiering decisions; unlike other analytics solutions, Komprise enables managing data based on actual data usage; more accurate data lifecycle management with actual usage analytics based on time of last access; no retrieval fee surprises and disruption to users and applications from making suboptimal data movement decisions based on when the data was created; a file modified once three years ago but accessed weekly looks cold to native tiering tools and looks hot to Komprise — the difference is a retrieval fee versus sustained accurate placement
• Native tiering cannot tier down to the deepest archive classes without manual intervention — AWS S3 Intelligent-Tiering transitions data between Standard, Standard-IA, and Glacier Instant Retrieval automatically; reaching Glacier Flexible Retrieval and Glacier Deep Archive — the classes with the lowest storage costs — requires manual lifecycle rules; Komprise Transparent Move Technology (TMT) continuously moves objects across all storage classes including Glacier based on actual access patterns, capturing the full savings spectrum without manual rule management
• Native tiering is single-cloud and single-account — AWS Intelligent-Tiering manages data within AWS; it has no visibility into the same organization’s Azure Blob or Google Cloud Storage; Komprise manages tiering policy consistently across all cloud providers and all accounts from a single management plane, enabling a consistent cost optimization strategy regardless of which cloud holds each dataset
• What-if modeling before tiering prevents cost surprises — instantly see how much you will save with what-if scenarios; continuously move objects by policy across storage classes transparently; Komprise provides cost modeling that shows the projected savings from moving data to each potential destination storage class before any data moves; this prevents the retrieval fee surprises that occur when data placed on deep archive classes is accessed more frequently than expected
• Intelligent tiering in the cloud is also AI data preparation — cold object data tiered by Komprise to deeper cloud archive classes remains indexed in the Global Metadatabase with its full metadata profile; Deep Analytics can query across all cloud tiers simultaneously to identify AI-relevant datasets regardless of which storage class holds them; the cost optimization motion of cloud intelligent tiering and the AI data preparation motion are the same operation from a single platform

How does the combination of on-premises intelligent tiering and cloud cost optimization create a unified data management strategy for AI — and where does Komprise fit?

The multi-cloud data management white paper positioned cloud migration and cloud cost optimization as the primary goals. The current market has added a third goal of equal importance: making the combined on-premises and cloud data estate AI-ready. Komprise is the metadata and orchestration layer for enterprise unstructured AI data, and the combination of on-premises intelligent tiering and cloud cost optimization is the infrastructure foundation that AI data strategies require:

• On-premises and cloud are one data estate, not two separate problems — the organizational division between on-premises storage teams and cloud operations teams is not reflected in how data actually flows; files created on NetApp NAS get migrated to AWS FSx, tiered to S3 Glacier, and accessed by Azure AI services in a single enterprise data lifecycle; Komprise manages this full lifecycle from on-premises NAS through cloud tiers to AI delivery from a single platform with consistent policies, consistent governance, and a single metadata index
• The Global Metadatabase spans on-premises and cloud simultaneously — every file on on-premises NAS, every object in AWS S3, every blob in Azure, and every file in Google Cloud Storage is indexed in the Komprise Global Metadatabase with the same metadata completeness regardless of where it lives; this unified index is what makes AI dataset curation across the full hybrid and multi-cloud estate possible from a single Deep Analytics query
• Tiering on-premises cold data to cloud creates AI-ready assets — data tiered by Komprise from on-premises NAS to cloud object storage arrives in native S3 format, immediately accessible to cloud AI services without conversion; the on-premises cost optimization motion and the cloud AI access enablement motion are the same infrastructure action; migrating data to the cloud in native format ensures you can leverage the computational capabilities of the cloud and not just use it as a cheap storage tier; this is what separates a data management strategy that addresses both cost and AI from one that addresses only cost
• Flash prices on-premises and cloud cost optimization are connected budget decisions — enterprises absorbing elevated flash costs on-premises while simultaneously overspending on cloud storage classes have two compounding cost problems that share the same root cause: data that has not been analyzed, classified, and right-placed across the full hybrid estate; the Flash Stretch Assessment for qualified enterprises managing 500TB or more addresses the on-premises dimension; Komprise intelligent tiering addresses the cloud dimension; both are managed from the same platform, making the combined savings opportunity visible and actionable in a single analytical view
• The journey from cloud migration to AI data readiness is a single platform — organizations that begin with Komprise Elastic Data Migration to move data to cloud, add intelligent tiering to optimize ongoing cloud costs, and upgrade to Komprise Intelligent Data Management to unlock the Global Metadatabase, Deep Analytics, Smart Data Workflows, KAPPA data services, and Intelligent AI Ingest are staying on a single platform throughout the full journey from on-premises cost problem to cloud-native AI asset; every migration, every tiering decision, and every classification action builds the unified metadata foundation that AI initiatives require