Statistics & Graphs

Statistics and graphs in Aimogen give you a visual, aggregated view of how AI is actually used over time. While logs show individual events, statistics turn that raw data into patterns you can understand at a glance. They exist to help you monitor trends, detect anomalies, and make informed decisions, not to overwhelm you with metrics.

They are operational dashboards, not vanity charts.

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What Statistics Represent #

Aimogen statistics summarize AI execution activity, including:

• volume of AI usage
• distribution across features
• provider and model usage
• time-based trends
• limit interactions
• success vs blocked executions

They are derived from usage and execution data, not from assumptions.

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Where Statistics & Graphs Are Available #

Statistics and graphs are available in the Aimogen admin area.

Path:
Aimogen → Statistics

The interface focuses on clarity and trend visibility rather than dense tables.

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Types of Graphs You Will See #

Statistics are typically presented as:

• time-series graphs (usage over time)
• distribution charts (usage by feature)
• provider usage breakdowns
• model usage comparisons
• limit hit frequency
• chatbot vs content usage ratios

Each graph answers a specific operational question.

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Time-Based Usage Trends #

Time-based graphs help you understand:

• daily and monthly usage patterns
• peak activity periods
• sudden spikes or drops
• gradual growth over time

These trends are essential for capacity planning and cost control.

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Feature-Level Insights #

Statistics show how usage is distributed across features such as:

• chatbots
• content generation
• bulk generators
• image generation
• Playground usage
• forms and workflows

This helps identify which features drive most AI activity and cost.

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Provider & Model Distribution #

Graphs can show:

• which providers are used most
• which models dominate usage
• whether expensive models are overused
• how fallback affects provider distribution

This visibility is critical when multiple providers are enabled.

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Limits & Blocked Execution Visibility #

Statistics also reflect:

• how often limits are reached
• which limits trigger most blocks
• whether limits are too strict or too loose

This allows you to tune limits based on real behavior instead of guesswork.

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Detecting Anomalies Early #

Graphs make anomalies obvious.

Examples:

• sudden spikes in chatbot usage
• unexpected Playground activity
• bulk generation running more often than planned
• guest usage approaching admin levels

Catching these visually is faster than reading logs.

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Statistics vs Logs #

Statistics:

• show patterns
• show trends
• show proportions
• answer “how much” and “how often”

Logs:

• show individual events
• show exact causes
• answer “what” and “why”

They are complementary, not interchangeable.

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Using Statistics for Cost Awareness #

While statistics are not billing tools, they help you:

• anticipate provider costs
• correlate usage with billing periods
• identify heavy usage windows
• decide where to tighten limits
• justify provider or model changes

They support proactive cost management.

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Multi-User and Role Analysis #

In multi-user environments, statistics help you see:

• which roles consume most AI
• how public usage compares to internal usage
• whether role-based limits make sense

This is especially important for membership and SaaS-style setups.

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What Statistics Do Not Show #

Statistics do not:

• show full prompts or content
• expose sensitive user data
• replace provider billing dashboards
• diagnose individual execution failures
• explain internal execution paths

For those, logs are required.

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Common Mistakes #

• ignoring statistics until costs spike
• relying only on provider dashboards
• misinterpreting short-term spikes
• adjusting limits without trend context
• expecting real-time billing accuracy

Statistics are directional tools, not invoices.

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Best Practices #

Review statistics regularly, especially after enabling new features or exposing AI publicly. Use trends to adjust limits, provider selection, and workflows. Pair statistics with logs when investigating anomalies. Treat graphs as early warning signals, not as post-mortems.

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Summary #

Statistics & graphs in Aimogen provide a high-level, visual overview of AI usage patterns, helping you understand trends, feature distribution, provider behavior, and limit effectiveness. They turn raw execution data into actionable insight, enabling proactive cost control, safer scaling, and informed operational decisions without drowning you in raw logs.