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Infrastructure Economics

Compute & Storage Cost Control

Cost efficiency in xLake is an architectural commitment, not an operational practice. It's built into
the platform's structure—and the economics strengthen as deployments scale and mature.

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TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Infrastructure cost that compounds in your favour—not against you.

xLake connects your sources through a governed query layer — no pipelines, no data movement, no proprietary formats.

35–45%

Lower infrastructure TCO

Storage growth stops dragging compute spend with it. Burst workloads stop justifying permanent over-provisioning.

Compute

Storage

15–25%

YoY Compute Reduction

Workload-specific clusters eliminate idle capacity. Cluster rebuilds that took weeks complete in hours.

Annual

Kubernetes

50–65%

Lower Storage Costs

Object storage durability matches triple-replication resilience. Unified residency controls remove 10–30% of duplicated data.

Day 1

Object Storage

Real-time cost attribution

Spend is attributed across every pipeline, cluster, and environment as it happens—catching Spark data skew, retry storms, and hanging pipelines before they reach the bill.

Pipeline-level

Storage

98% Workload Reuse

The same Spark workloads run across on-prem, private cloud, public cloud, and sovereign deployments without refactoring—eliminating 10–15% OpEx inflation from re-engineering cycles.

On-prem

Private Cloud

Public Cloud

How It Works

Four mechanisms that fix cost at the foundation

Legacy platforms couple compute and storage. Data grows, your compute bill grows. Workloads spike, you over-provision for peak and pay for it permanently.

Decoupled compute and storage

each scales independently on its own economics

Workload-specific Kubernetes clusters

Right-sized for ETL, SQL, ML, and inference workloads rather than generalised for all of them

Object storage durability

Replaces expensive triple replication without sacrificing resilience

Runtime cost intelligence

Detects waste during execution, not after the bill arrives

The Compounding Effect Savings that don't stack linearly—they compound

The economics improve every year. Lower storage costs, right-sized clusters, unified attribution, and workload portability.

A sample enterprise running 200TB across hybrid Spark workloads

Savings Driver

Typical Baseline

Year-One xLake

Three-Year Trajectory

Storage (object vs. triple replication)

$480K

$192K

$168K (continued dedup gains)

Compute over-provisioning

$600K

$450–$510K

$380–$430K

Cross-environment data duplication

10–30% of storage spend

Eliminated

Migration & re-engineering overhead

$120–$180K per cycle

Near zero

Combined TCO reduction

Baseline

35–45%

50%+

At a Glance

Legacy platforms vs. xLake — head to head

Every cost driver that legacy platforms obscure or ignore — surfaced and resolved.

Compute-storage coupling

Storage replication

Cluster allocation

Cross-environment duplication

Migration overhead

Cost attribution

Year-one TCO reduction

Legacy Platform

xLake

Forced co-scaling

Independent scaling

3× multiplier

Object storage durability

Generalised

Workload-specific Kubernetes

10–30% redundant

Unified residency controls

Weeks; 10–15% OpEx inflate

Hours; 98% workload reuse

Fragmented, post-hoc

Unified, real-time

Baseline

35–45% minimum

Got Questions? Get Clarity

How quickly do organisations typically see cost savings after adopting xLake?

Most enterprises see measurable storage cost reductions from day one, as object storage economics apply immediately at deployment. Compute savings compound over the first 12–24 months as workload-specific cluster configurations are refined and idle capacity is eliminated.

Does xLake require us to refactor our existing Spark workloads?

No. xLake is designed for 98% workload reuse without refactoring. The same Spark workloads run across on-prem, private cloud, public cloud, and sovereign environments as-is—meaning migration and re-engineering costs are not added to the transition.

How does object storage maintain resilience without triple replication?

xLake uses object storage durability models that achieve equivalent resilience to triple-replication through erasure coding and distributed architecture—at 50–65% lower cost. The durability guarantees are the same; the underlying storage economics are fundamentally different.

What does real-time cost attribution actually detect, and how does it help?

xLake monitors spend at the pipeline, cluster, and environment level as workloads execute. It surfaces specific waste patterns—Spark data skew, retry storms, hanging pipelines—during the run, before they accumulate into the monthly bill. This allows engineering teams to intervene in hours rather than discovering the cost impact weeks later.

Can xLake model savings against our specific infrastructure before we commit?

Yes. The TCO analysis takes your current Spark cluster spend, storage volumes, and environment topology and models year-one savings alongside a three-year trajectory. It also surfaces costs your current platform typically does not attribute—giving a more complete picture of the actual baseline.

Are the published savings figures applicable to smaller deployments, or only large enterprises?

The headline figures are modelled against enterprise-scale deployments of 200TB and above. The relative savings percentages—particularly on storage and compute—hold across a wide range of deployment sizes, though the absolute numbers and compounding trajectory are most pronounced at scale. The TCO analysis scopes this to your specific volumes and topology.