AI Cost Optimization

See and optimize the full cost of enterprise AI.

Connect provider spend, tokens, inference, training, accelerators, teams, features, and business outcomes in one cost view.

SmartC
AI Cost Portfolio
Review
AI Spend
$1.84M
Cost / Outcome
$0.42
Open Actions
28
Workload
Model
Unit Cost
Support Assistant
Model A
$0.18 / case
Document Processing
Model B
$0.07 / doc
Code Assistant
Model C
$4.20 / user
AI Cost Pressure

AI spend moves across providers, applications, models, tokens, and infrastructure.

Provider bills lack business context
Token and inference spend changes quickly
Shared models obscure ownership
GPU capacity can be underused
Forecasting is volatile
Low token price can hide weak unit economics
AI Cost Inventory

See every AI provider, model, workload, and cost component.

Model APIs and managed AI services
Self-hosted models and accelerator infrastructure
Training, fine-tuning, inference, and supporting data services
Applications, agents, features, and experiments consuming AI
Providers
Model APIs and managed services
Models
Hosted, partner, open, and private models
Infrastructure
GPU, TPU, compute, storage, and data
Workloads
Applications, agents, features, and experiments
AI Consumption

Track the meters that create AI cost.

Input and output tokens
Requests and inference events
Training and fine-tuning runs
GPU, TPU, and instance hours
AI Cost Allocation

Assign AI spend to the teams and business uses that generate it.

Team and cost owner
Product, feature, application, or agent
Model, provider, and environment
Customer, business unit, or business outcome
SmartC
AI Cost Allocation
96%
Business Use
Team
Monthly Cost
Support Assistant
Customer Ops
$420K
Code Assistant
Engineering
$310K
Document Processing
Operations
$184K
AI Unit Economics

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

Cost per token
Cost per request or inference
Cost per user or feature
Cost per business outcome
AI Budgets and Forecasts

Plan AI spend with consumption assumptions—not a single top-down number.

Provider, model, and workload budgets
Token, request, or infrastructure-volume assumptions
Committed and variable AI cost
Forecast revisions as adoption changes
SmartC
AI Cost Forecast
Illustrative
AI Cost Anomalies

Detect unexpected usage and spend before the monthly review.

Provider spike
Model or feature spike
Runaway agent or repeated call pattern
Unexpected infrastructure consumption
Training and Inference Costs

Separate development, training, deployment, and ongoing operating cost.

Experimentation and development
Training and fine-tuning
Inference APIs or serving infrastructure
Data, storage, retrieval, and supporting services
SmartC
AI Cost Composition
Review
Cost Component
Current
Share
Inference
$940K
51%
Training
$380K
21%
Data & Infrastructure
$520K
28%
Model Economics

Compare model and deployment choices with quality and performance context.

A lower-cost model is an opportunity only when the required business quality and performance are protected.

Cost
Token, request, instance, or committed rate.
Quality
Approved workload-specific evaluation.
Performance
Latency, throughput, and reliability requirements.
Operating model
API, managed service, or self-hosted deployment.

Token, request, instance, or committed rate.

Token Efficiency

Reduce unnecessary input, context, and output without degrading the required result.

Repeated or oversized context
Unnecessary input and output length
Repeated calls for the same work
Token cost by model, feature, and outcome
SmartC
Token Cost Review
Context
Feature
Input
Output
Support Summary
8.4K
1.2K
Document Extract
3.1K
420
Agent Workflow
14.2K
4.8K
Caching, Batch, and Rate Options

Match workload timing and repetition to the available consumption option.

Availability and economics vary by provider, model, region, workload, and commitment term.

Context caching
Reuse repeated content where supported.
Batch processing
Use asynchronous processing for suitable workloads.
Flexible consumption
Use lower-cost service levels where latency permits.
Committed capacity
Review commitments only for stable, justified demand.

Use asynchronous processing for suitable workloads.

Accelerator Utilization

Measure how effectively provisioned GPU and TPU capacity is used.

Provisioned versus utilized capacity
Idle and underused accelerator hours
Training and inference demand patterns
Scheduling, scaling, and deployment candidates
SmartC
Accelerator Utilization
Retain
Provisioned
18.4K h
Utilized
11.7K h
Efficiency
64%
Cluster
Utilization
Demand
Training A
82%
Stable
Inference B
41%
Variable
Human-Approved AI Cost Actions

Turn AI cost evidence into controlled optimization decisions.

SmartC calculates. People approve consequential AI cost actions.

Reduce waste
Remove unnecessary tokens, calls, or idle capacity.
Change consumption option
Review caching, batch, flexible, or committed modes.
Review model or deployment
Compare alternatives with workload-specific quality evidence.
Track outcome
Reconcile the observed cost and business result.

Remove unnecessary tokens, calls, or idle capacity.

Before You Act

What to know before optimizing AI costs.

What AI costs are included?
Provider APIs, managed AI services, self-hosted infrastructure, training, fine-tuning, inference, accelerators, data, and supporting services where evidence is available.
How is AI spend allocated?
Map token, inference, API, and infrastructure cost to the relevant team, application, feature, model, customer, or business outcome.
What is the right AI unit cost?
Use the unit that reflects the workload: token, request, inference, user, feature, case, document, transaction, or another approved business outcome.
Should we always choose the cheapest model?
No. Compare cost with workload-specific quality, latency, throughput, reliability, and operating requirements.
Is this the same as Ask AI?
No. AI Cost Optimization manages the cost of AI workloads. Ask AI uses AI to explain available IT cost data.
Does SmartC switch models or stop workloads automatically?
No. Authorized people review quality, performance, business impact, and operational safeguards before consequential changes.
Book a Demo

See AI spend, consumption, unit economics, and optimization opportunities.

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AI cost optimization assessment.

Review AI spend coverage, allocation, consumption, unit economics, budget volatility, model and infrastructure choices, and human-approved cost 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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