Data Platform Cost Optimization

See and optimize the cost of every data workload.

Connect platform spend, compute, storage, queries, pipelines, capacity, teams, and business outcomes in one cost view.

SmartC
Data Platform Cost Portfolio
Review
Platform Cost
$4.82M
Cost / Workload
$286
Open Actions
41
Workload
Platform
Unit Cost
Sales Analytics
Warehouse A
$0.19 / query
Customer Pipeline
Compute B
$24 / run
Archive Store
Storage C
$8 / TB
Data Cost Pressure

Data cost moves across platforms, queries, pipelines, compute, and storage.

Billing records lack workload context
Queries consume more than expected
Compute stays active without useful work
Shared platforms obscure ownership
Storage and retention grow quietly
Capacity and pricing choices drift from demand
Data Platform Inventory

See the platforms, workloads, and cost components behind data delivery.

Databases, warehouses, lakehouses, and query engines
Clusters, serverless compute, slots, and reserved capacity
Pipelines, jobs, notebooks, dashboards, and queries
Tables, datasets, storage, transfer, and supporting services
Platforms
Databases, warehouses, lakehouses, engines
Compute
Clusters, slots, serverless, and capacity
Workloads
Queries, jobs, pipelines, dashboards
Data
Tables, datasets, storage, and transfer
Data Platform Consumption

Track the meters that create data-platform cost.

Compute time and platform units
Bytes scanned and query execution
Storage volume and retention
Data movement and supporting services
Data Cost Allocation

Assign data-platform usage to the teams and workloads that generate it.

Team, owner, user, and cost center
Application, product, project, or environment
Query, pipeline, job, cluster, or warehouse
Customer, business unit, or business outcome
SmartC
Data Cost Allocation
94%
Data Product
Owner
Monthly Cost
Customer Analytics
Data Products
$640K
Finance Reporting
Finance Data
$310K
Shared Exploration
Multiple
$188K
Data Workload Unit Economics

Measure cost in the unit that reflects the workload’s purpose.

Cost per query
Cost per pipeline or job run
Cost per table, dataset, or terabyte
Cost per report, user, or business transaction
Query Efficiency

Find expensive queries and the workload patterns behind them.

Bytes processed and execution duration
Repeated, scheduled, and high-frequency queries
Resource warnings and inefficient workload patterns
Query owner, application, and business purpose
SmartC
Query Cost Review
Repeated
Query Group
Runs
Processed
Daily Revenue
96
84 TB
Customer Extract
12
31 TB
Finance Close
8
7 TB
Idle Compute and Scheduling

Stop paying for inactive capacity where the workload permits.

Scheduling and suspend settings must match query arrival patterns and service requirements.

Inactive warehouses or clusters
Scheduled start and stop windows
Auto-suspend configuration
Serverless or consumption-based alternatives
Capacity and Commitments

Match reserved, provisioned, and autoscaled capacity to workload demand.

On-demand versus capacity-based consumption
Reserved slots, nodes, or platform commitments
Peak, average, and idle utilization
Workload isolation and shared idle capacity
SmartC
Capacity Utilization
Retain
Reserved
12.0K h
Consumed
8.1K h
Utilization
68%
Workload Pool
Peak
Average
Production
91%
74%
Development
48%
22%
Storage and Retention Cost

Review storage growth, access patterns, retention, and available pricing tiers.

SmartC identifies review candidates. Business and data owners approve retention, archival, or deletion decisions.

Active and older data
Retention and recovery windows
Temporary and intermediate data
Storage class and billing model
Pipeline and Job Cost

Trace recurring processing cost to the pipeline, job, schedule, and owner.

Cost per pipeline or job run
Schedule frequency and processing volume
Compute type, size, and runtime
Failed, repeated, or low-value processing
SmartC
Pipeline Cost Review
Expected
Pipeline
Runs
Cost / Run
Customer Daily
31
$42
Inventory Hourly
744
$18
Legacy Export
124
$29
Duplicate and Unused Data Cost

Find datasets and derived copies that continue to consume storage or processing cost.

Metadata indicates candidates. Authorized owners decide whether data or processing can be archived, consolidated, or retired.

Unused tables and datasets
Duplicate or derived copies
Temporary outputs retained beyond need
Processing maintained for inactive consumers
Budgets, Forecasts, and Anomalies

Track spend by platform and owner, then surface material changes.

Platform, team, project, and workspace budgets
Forecast based on usage and pricing assumptions
Query, pipeline, compute, and storage anomalies
Owner, evidence, and required review
SmartC
Data Platform Forecast
Illustrative
Human-Approved Data Cost Actions

Turn data-platform evidence into controlled optimization decisions.

SmartC calculates. People approve consequential data-platform actions.

Rightsize or reschedule
Align compute with actual workload demand.
Optimize query or pipeline
Reduce unnecessary processing while protecting the result.
Review storage and retention
Tier, archive, consolidate, or retire only with owner approval.
Track outcome
Reconcile observed cost and workload performance.

Align compute with actual workload demand.

Before You Act

What to know before optimizing data-platform costs.

What data-platform costs are included?
Databases, warehouses, lakehouses, query engines, compute, storage, pipelines, jobs, queries, transfer, and supporting services where evidence exists.
How is cost assigned to a workload?
Use billing records, resource metadata, tags, query or job identifiers, owners, and business dimensions.
What is the right data unit cost?
Use the workload’s meaningful unit: query, run, table, dataset, terabyte, report, user, transaction, or another approved outcome.
Should idle compute always be suspended?
No. Suspend and schedule settings must reflect arrival patterns, minimum billing, latency, and service requirements.
Is this the same as Cloud Cost Optimization?
No. Cloud Cost Optimization manages general public-cloud services and billing. Data Platform Cost Optimization focuses on data workloads, queries, pipelines, storage, and platform unit economics.
Does SmartC delete data or change queries automatically?
No. Authorized people review data value, quality, retention, performance, and business impact before consequential changes.
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See data-platform spend, workloads, unit economics, and optimization opportunities.

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Data platform cost optimization assessment.

Review cost coverage, allocation, workload consumption, query and pipeline economics, storage growth, capacity, budgets, anomalies, and human-approved actions.

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IT Cost Optimization software and expert-led services across the major areas of enterprise technology spend.

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