Cloud AI Agent Economics & Drift Analyzer

Agent Simulation Parameters Config

Monthly Task Invocations: 50,000

Model Tier Benchmark

Input Token Cost / M: $2.50

Output Token Cost / M: $10.00

Always-On VPS / Container ($/mo): $40.00

SaaS Subscription Price ($/user/mo): $29.00

Active Subscribers (MAU): 500

Log File & Transcript Parser

Drag & Drop Execution Logs (CSV / JSON)

Or click to browse local transcript files

Serverless Mo. Cost $0.00 Tokens + Invocations

24/7 VPS Hosting $40.00 Fixed Server Overhead

Monthly SaaS Revenue $14,500 500 Users @ $29/mo

Net SaaS Margin 0.0% Optimal Architecture

Analyzing architecture efficiency...

Hosting Model Scaling Breakdown (Serverless vs VPS) X: Monthly Invocations | Y: Monthly Overhead ($)

NLP Topic Drift & Latency Scatter (Compromise NLP) High Drift (>0.3) Indicates Execution Risk

Execution Log Stream (0 Runs Analyzed)

Timestamp	Intent	Tokens	Latency	Drift Score	Status

Cloud AI Agent Unit Economics & Drift Breakdown

How does the tool compare serverless token pricing against persistent VPS hosting, and what drives its topic drift metric?

This interactive workbench models the recurring cost trade-off between pay-per-token serverless APIs and fixed virtual private server (VPS) hosting across varying monthly task volumes. It computes serverless cost by weighting user-configured prompt and completion token rates against an assumed 70/30 input-to-output split, while evaluating gross margins against subscription revenue. Additionally, it plots execution latency alongside an NLP topic drift score derived from tokenized part-of-speech ratios.

The workbench relies on hardcoded operational assumptions. For VPS hosting, it assumes a fixed monthly base plus an arbitrary 85% reduction in external token API fees, representing self-hosted open-weight inference without factoring in actual GPU hardware procurement, autoscaling overhead, or idle compute wastage. Furthermore, its 'topic drift' calculation is an uncalibrated heuristic measuring noun and verb structure ratios rather than semantic vector distance, meaning high drift scores do not indicate verified semantic hallucination.

Try a worked example

Select 'Claude 3.5 Sonnet ($3.00 / $15.00 per M tokens)' from the Model Tier Benchmark menu while retaining the default 50,000 monthly invocations and default 6-log fixture. The input token cost updates to $3.00/M, output token cost updates to $15.00/M, Serverless Mo. Cost recalculates to $711.91, and the Net SaaS Margin updates to 99.0% against $14,500 in monthly subscription revenue.

Token Pricing Split & Serverless Mechanics

The engine computes average token count across active logs (averaging 2,073 tokens across the default 6 fixture items). It allocates 70% of those tokens to prompts ($inputCost per million) and 30% to completions ($outputCost per million), adding a constant $0.000002 invocation surcharge per task.

VPS Heuristics vs. Real Infrastructure

The tool models VPS expense as state.vpsCost + (monthlyTokenCost * 0.15). In production architectures, hosting open-weight models on dedicated virtual servers eliminates third-party token API fees but incurs persistent GPU slice reservations, cooling, networking, engineering maintenance, and capacity bottlenecks during traffic spikes.

Drift Score Heuristic

The scatter plot marks tasks exceeding 0.3 as critical drift. Rather than calculating semantic divergence via embedding cosine distance against initial system prompts, the script evaluates the Compromise NLP library's ratio of nouns and verbs relative to total word count, adding synthetic jitter.

Cloud economics arithmetic and the chart discrepancy

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Read the explanation

The six supplied logs contain twelve thousand nine hundred forty total tokens. Dividing by six and rounding gives two thousand one hundred fifty seven per task. The economics formula assumes seventy percent input and thirty percent output, so these become fifteen hundred nine point nine input and six hundred forty seven point one output tokens. They are estimated splits, not separately measured log fields. The preset token prices are source constants, not verified current provider prices. At fifty thousand invocations, source prices two point five and ten dollars per million produce token cost five hundred twelve point two eight dollars. Invocation fees add ten cents. The VPS metric adds forty dollars plus fifteen percent of token cost, about one hundred sixteen point eight four dollars. The fifteen percent assumption is not demonstrated equivalent inference performance or an actual hosting bill. The chart draws VPS as a flat forty dollars, omitting that variable fraction, so its line disagrees with the metric. Revenue is five hundred subscribers times twenty nine dollars, or fourteen thousand five hundred. Subtracting the cheaper modeled infrastructure yields about ninety nine point two percent margin. This excludes staffing, support, payment fees, acquisition and many other costs, so it is not total business profitability. The drift helper uses noun and verb ratios plus randomness, or a random fallback without the NLP library. A synthetic benchmark generates random logs rather than production evidence. This page audits local assumptions; it does not validate economics or semantic safety.