AI Interaction Cost Calculator
Compare recurring cloud AI spend with running private AI interactions locally through InfoDump and InfoDump Models.
Context
Why AI Interaction Cost Needs a CEO View
Most AI cost conversations focus on token prices, subscriptions, or cloud invoices. Executives need a simpler question: what does normal AI usage cost every month as employees interact with models, agents, and governed workflows?
Tokens per watt is still useful, but only as part of a larger operating view. The point is not to count every token by hand. The point is to estimate average interactions, then compare recurring cloud AI spend with the cost of running private AI workflows locally through InfoDump.
Scenario
How Much Does AI Usage Cost Per Interaction?
Imagine AI usage spreading across leadership, finance, operations, sales, support, and product teams. Some interactions are short summaries. Some are document questions. Some are heavier business analysis or governed workflow actions.
That broader interaction mix is a better executive planning unit than a single finance scenario. A CEO can estimate the number of people using AI, how often they interact with it, and what the average interaction looks like.
Cloud AI interactions
Each interaction is priced by cloud token usage. Costs can scale quickly as more employees use AI more often.
InfoDump local AI interactions
Interactions run on private local infrastructure, with InfoDump Models available for managed model processing when the workflow calls for it.
Executive Cost Model
Estimate Monthly AI Interaction Cost
Enter how many people use AI, how often they interact with it, and the average interaction type. Token volume, cloud cost, local electricity, and InfoDump Models estimates are calculated automatically.
Company usage
These are the only volume inputs. The calculator turns usage into estimated monthly interactions and tokens.
Primary Insight
Estimated monthly savings with InfoDump local AI: $2,070.98.
For 13,200 monthly interactions, this compares a cloud model workflow with an InfoDump local deployment and infodump local + managed models. The estimate emphasizes recurring AI usage, not one-off experimentation.
Monthly interactions
13,200
People x daily interactions x workdays
Cloud monthly cost
$2,164.80
Frontier cloud model, 314.2M monthly tokens
InfoDump estimated monthly cost
$93.82
Local electricity plus selected InfoDump model mode
Estimated savings
95.7%
Monthly cloud cost compared with InfoDump local estimate
Cloud cost / interaction
$0.1640
23,800 calculated tokens per interaction
InfoDump cost / interaction
$0.0071
7,800 model tokens per interaction
Local electricity / month
$42.34
Department local deployment, always-on profile
InfoDump Models / month
$51.48
InfoDump local + managed models
Interaction profile
Business analysis mix
A blended average across business analysis, search, spreadsheet questions, and document analysis.
Cloud usage avoided
67.2% less metered cloud model usage
Derived from the average interaction type and local execution profile.
Local efficiency signal
2,292 tokens / watt-hour
Estimated local model tokens per watt-hour for this interaction profile.
These are planning estimates, not a quote. Actual economics depend on workload shape, model selection, utilization, deployment design, electricity rates, and commercial terms. The calculator does not upload files, store inputs, or send assumptions to a backend.
Comparison
Cloud AI vs Local AI
Cloud AI cost is driven by input tokens, output tokens, model pricing, and usage volume. Local AI cost is driven by hardware, power draw, workload duration, utilization, and operations overhead. Neither number tells the whole story on its own.
A better comparison starts at the interaction level. How many people use AI? How many interactions happen per person per day? What is the average interaction type? The calculator uses those business inputs to estimate token volume, cloud cost, local electricity, and potential InfoDump Models processing cost.
InfoDump
Why Local AI Changes the Cost Model
Running AI locally changes the economics of repeatable internal interactions. Instead of treating every prompt, follow-up, search, summary, or agent step as a metered cloud request, InfoDump gives companies a local execution layer for private AI work and controlled access to InfoDump Models when managed model processing is useful.
- Recurring interactions can run closer to company systems and sensitive business data.
- Local infrastructure cost can be compared against monthly cloud AI usage.
- InfoDump Models can support controlled workflows without making every interaction a frontier-model call.
- Executives can reason about cost per interaction, monthly volume, and data control in one view.
Offerings
Three ways InfoDump supports private AI economics
The calculator estimates interaction cost. The right product path depends on where AI should run, how much governance the team needs, and whether the organization wants role-specific models.
Local Infrastructure
InfoDump Hardware
Dedicated private AI hardware for teams that want interactions, document work, and governed model access to run closer to company data.
- Personal and corporate setups
- Office-grade local AI access
- Private deployment control
Governance Layer
InfoDump Platform
A private AI workspace for model access, source boundaries, agent permissions, auditability, and controlled business workflows.
- Controlled source access
- Agent and model governance
- Privacy and audit controls
Role Models
InfoDump SLM Studio
A local workflow for creating role-specific small models that learn behavior, answer structure, and boundaries while company facts stay governed.
- Role definition
- Example generation and evals
- Local testing and packaging
InfoDump
Choose the Right InfoDump Setup
InfoDump helps companies reduce unnecessary cloud AI usage through private AI hardware, a governed platform layer, and SLM Studio for role-specific models. Teams can combine these paths based on data sensitivity, interaction volume, and deployment control.
Related reading: InfoDump Platform, SLM Studio launch and role-specific small models.
FAQ
Can InfoDump run AI workflows on-prem?
Yes. InfoDump is designed to support private AI workflows that can run closer to company data through local hardware, company infrastructure, Banix hardware, or an InfoDump-governed deployment path.
Why would a company run AI on-prem instead of only using cloud models?
On-prem AI can help reduce unnecessary metered cloud usage, keep sensitive interactions closer to approved systems, and give teams more control over source access, model routing, auditability, and data ownership.
What role does InfoDump hardware play?
InfoDump hardware gives teams a dedicated local place to run private AI interactions, document workflows, and governed model access. It is useful when an organization wants local control without turning every interaction into a cloud request.
What role does the InfoDump Platform play?
The InfoDump Platform is the governance layer. It helps teams manage model access, agent permissions, controlled source access, privacy guardrails, auditability, and policy expectations across local and hosted AI workflows.
Where does InfoDump SLM Studio fit?
InfoDump SLM Studio helps teams create role-specific small models through a local workflow. Studio is for shaping behavior, answer structure, role boundaries, and evaluation before models are used in governed workflows.
Does on-prem AI replace cloud AI completely?
Not always. Many organizations will use a hybrid pattern: run repeatable and sensitive workflows locally, use InfoDump Models where they fit, and route selected work to larger external models when the task requires it.
How should executives evaluate on-prem AI cost?
Executives should look at interaction volume, cost per interaction, local infrastructure cost, model routing, data sensitivity, and governance requirements. This is more useful than comparing token prices in isolation.