Analytics

The Analytics feature in Open WebUI provides administrators with comprehensive insights into usage patterns, token consumption, and model performance across their instance. This powerful tool helps you understand how your users are interacting with AI models and make data-driven decisions about resource allocation and model selection.

Admin-Only Feature

Analytics is only accessible to users with admin role. Access it via Admin Panel > Analytics.

Overview

The Analytics dashboard gives you a bird's-eye view of your Open WebUI instance's activity, including:

Message volume across different models and time periods
Token usage tracking for cost estimation and resource planning
User activity patterns to understand engagement
Time-series data showing trends over hours, days, or months

All analytics data is derived from the message history stored in your instance's database. When the Analytics feature is enabled, Open WebUI automatically tracks and indexes messages to provide fast, queryable insights.

Accessing Analytics

Log in with an admin account
Navigate to Admin Panel (click your profile icon → Admin Panel)
Click on the Analytics tab in the admin navigation

Dashboard Features

Time Period Selection

At the top right of the Analytics dashboard, you can filter all data by time period:

Last 24 hours - Hourly granularity for real-time monitoring
Last 7 days - Daily overview of the past week
Last 30 days - Monthly snapshot
Last 90 days - Quarterly trends
All time - Complete historical data

Your selected time period is automatically saved and persists across browser sessions.

Group Filtering

If you have user groups configured, the Analytics dashboard allows filtering by group:

Use the group dropdown next to the time period selector
Select a specific group to view analytics only for users in that group
Choose "All Users" to see instance-wide analytics

This is useful for:

Department-level reporting - Track usage for specific teams
Cost allocation - Attribute token consumption to business units
Pilot programs - Monitor adoption within test groups

All metrics on the page update automatically when you change the time period or group filter.

Summary Statistics

The dashboard header displays key metrics for the selected time period:

Total Messages - Number of assistant responses generated
Total Tokens - Sum of all input and output tokens processed
Total Chats - Number of unique conversations
Total Users - Number of users who sent messages

Message Counting

Analytics counts assistant responses rather than user messages. This provides a more accurate measure of AI model usage and token consumption.

Message Timeline Chart

The interactive timeline chart visualizes message volume over time, broken down by model. Key features:

Hourly or Daily granularity - Automatically adjusts based on selected time period
Multi-model visualization - Shows up to 8 models with distinct colors
Hover tooltips - Display exact counts and percentages for each model at any point in time
Trend identification - Quickly spot usage patterns, peak hours, and model adoption

This chart helps you:

Identify busy periods for capacity planning
Track model adoption after deployment
Detect unusual activity spikes
Monitor the impact of changes or announcements

Model Usage Table

A detailed breakdown of how each model is being used:

Column	Description
#	Rank by message count
Model	Model name with icon
Messages	Total assistant responses generated
Tokens	Total tokens (input + output) consumed
%	Percentage share of total messages

Features:

Sortable columns - Click column headers to sort by name or message count
Model icons - Visual identification with profile images
Token tracking - See which models consume the most resources
Clickable rows - Click any model to open the Model Details Modal

Use cases:

Identify your most popular models
Calculate cost per model (by multiplying tokens by provider rates)
Decide which models to keep or remove
Plan infrastructure upgrades based on usage

Clicking on any model row opens a detailed modal with two tabs:

Overview Tab

The Overview tab provides:

Feedback Activity Chart - Visual history of user feedback (thumbs up/down) over time
- Toggle between 30 days, 1 year, or All time views
- Weekly aggregation for longer time ranges
Tags - Most common chat tags associated with this model (top 10)

This helps you understand:

How users perceive model quality over time
Which topics/use cases the model is handling
Trends in user satisfaction

Chats Tab

Admin Chat Access Required

The Chats tab is only visible when Admin Chat Access is enabled in your instance settings.

The Chats tab shows conversations that used this model:

User info - Who started each chat
Preview - First message of each conversation
Timestamp - When the chat was last updated
Click to open - Navigate directly to the shared chat view

This is useful for:

Understanding how users interact with specific models
Auditing model usage for quality assurance
Finding example conversations for training or documentation

User Activity Table

Track user engagement and token consumption per user:

Column	Description
#	Rank by activity
User	Username with profile picture
Messages	Total messages sent by this user
Tokens	Total tokens consumed by this user

Features:

Sortable columns - Organize by name or activity level
User identification - Profile pictures and display names
Token attribution - See resource consumption per user

Use cases:

Monitor power users and their token consumption
Identify inactive or low-usage accounts
Plan user quotas or rate limits
Calculate per-user costs for billing purposes

Token Usage Tracking

What Are Tokens?

Tokens are the units that language models use to process text. Both the input (your prompt) and output (the model's response) consume tokens. Most AI providers charge based on token usage, making token tracking essential for cost management.

How Token Tracking Works

Open WebUI automatically captures token usage from model responses and stores it with each message. The Analytics feature aggregates this data to show:

Input tokens - Tokens in user prompts and context
Output tokens - Tokens in model responses
Total tokens - Sum of input and output

Token data is normalized across different model providers (OpenAI, Ollama, llama.cpp, etc.) to provide consistent metrics regardless of which backend you're using.

Token Usage Metrics

The Token Usage section (accessible via the Tokens endpoint or dashboard) provides:

Per-model token breakdown - Input, output, and total tokens for each model
Total token consumption - Instance-wide token usage
Message count correlation - Tokens per message for efficiency analysis

Cost Estimation

To estimate costs, multiply the token counts by your provider's pricing:

Cost = (input_tokens × input_price) + (output_tokens × output_price)

Example for GPT-4:

Input: 1,000,000 tokens × $0.03/1K = $30
Output: 500,000 tokens × $0.06/1K = $30
Total: $60

Use Cases

1. Resource Planning

Scenario: You're running Open WebUI for a team and need to plan infrastructure capacity.

How Analytics helps:

View the Message Timeline to identify peak usage hours
Check Model Usage to see which models need more resources
Monitor Token Usage to estimate future costs
Track User Activity to plan for team growth

2. Model Evaluation

Scenario: You've deployed several models and want to know which ones your users prefer.

How Analytics helps:

Compare message counts across models to see adoption rates
Check token efficiency (tokens per message) to identify verbose models
Monitor trends in the timeline chart after introducing new models
Combine with the Evaluation feature for quality insights

3. Cost Management

Scenario: You're using paid API providers and need to control costs.

How Analytics helps:

Track total token consumption by model and user
Identify high-usage users for quota discussions
Compare token costs across different model providers
Set up regular reviews using time-period filters

4. User Engagement

Scenario: You want to understand how your team is using AI tools.

How Analytics helps:

Monitor active users vs. registered accounts
Identify power users who might need support or training
Track adoption trends over time
Correlate usage with team initiatives or training sessions

5. Compliance & Auditing

Scenario: Your organization requires usage reporting for compliance.

How Analytics helps:

Generate activity reports for specific time periods
Track user attribution for all AI interactions
Monitor model usage for approved vs. unapproved models
Export data via API for external reporting tools

Technical Details

Data Storage

Analytics data is stored in the chat_message table, which contains:

Message content - User and assistant messages
Metadata - Model ID, user ID, timestamps
Token usage - Input, output, and total tokens
Relationships - Links to parent messages and chats

When you enable Analytics (via migration), Open WebUI:

Creates the chat_message table with optimized indexes
Backfills existing messages from your chat history
Dual-writes new messages to both the chat JSON and the message table

This dual-write approach ensures:

Backward compatibility - Existing features continue working
Fast queries - Analytics doesn't impact chat performance
Data consistency - All messages are captured

Database Indexes

The following indexes optimize analytics queries:

chat_id - Fast lookup of all messages in a chat
user_id - Quick user activity reports
model_id - Efficient model usage queries
created_at - Time-range filtering
Composite indexes for common query patterns

API Endpoints

For advanced users and integrations, Analytics provides REST API endpoints:

Dashboard Endpoints:

GET /api/v1/analytics/summary
GET /api/v1/analytics/models
GET /api/v1/analytics/users
GET /api/v1/analytics/messages
GET /api/v1/analytics/daily
GET /api/v1/analytics/tokens

Model Detail Endpoints:

GET /api/v1/analytics/models/{model_id}/chats    # Get chats using this model
GET /api/v1/analytics/models/{model_id}/overview # Get feedback history and tags

Common Query Parameters:

Parameter	Type	Description
`start_date`	int	Unix timestamp (epoch seconds) - start of range
`end_date`	int	Unix timestamp (epoch seconds) - end of range
`group_id`	string	Filter to a specific user group (optional)

API Access

All Analytics endpoints require admin authentication. Include your admin bearer token:

curl -H "Authorization: Bearer YOUR_ADMIN_TOKEN" \
  "https://your-instance.com/api/v1/analytics/summary?group_id=abc123"

Privacy & Data Considerations

What Gets Tracked?

Analytics tracks:

✅ Message timestamps and counts
✅ Token usage per message
✅ Model IDs and user IDs
✅ Chat IDs and message relationships

Analytics does not track:

❌ Message content display in the dashboard (only metadata)
❌ External sharing or exports
❌ Individual message content outside the database

Data Retention

Analytics data follows your instance's chat retention policy. When you delete:

A chat - All associated messages are removed from analytics
A user - All their messages are disassociated
Message history - Analytics data is also cleared

Frequently Asked Questions

Why are message counts different from what I expected?

Analytics counts assistant responses, not user messages. If a chat has 10 user messages and 10 assistant responses, the count is 10. This provides a more accurate measure of AI usage and token consumption.

How accurate is token tracking?

Token accuracy depends on your model provider:

OpenAI/Anthropic - Exact counts from API responses
Ollama - Accurate for models with token reporting
llama.cpp - Reports tokens when available
Custom providers - Depends on implementation

Missing token data appears as 0 in analytics.

Can I export analytics data?

Yes, via the API endpoints. Use tools like curl, Python scripts, or BI tools to fetch and export data:

curl -H "Authorization: Bearer TOKEN" \
  "https://instance.com/api/v1/analytics/summary?start_date=1704067200&end_date=1706745600" \
  > analytics_export.json

Summary

Open WebUI's Analytics feature transforms your instance into a data-driven platform by providing:

📊 Real-time insights into model and user activity
💰 Token tracking for cost management and optimization
📈 Trend analysis to understand usage patterns over time
👥 User engagement metrics for community building
🔒 Privacy-focused design keeping all data on your instance

Whether you're managing a personal instance or a large organizational deployment, Analytics gives you the visibility needed to optimize performance, control costs, and better serve your users.

Evaluation - Measure model quality through user feedback
RBAC - Control access to models and features per user
Data Controls - Manage chat history and exports

Overview​

Accessing Analytics​

Dashboard Features​

Time Period Selection​

Group Filtering​

Summary Statistics​

Message Timeline Chart​

Model Usage Table​

Model Details Modal​

Overview Tab​

Chats Tab​

User Activity Table​

Token Usage Tracking​

What Are Tokens?​

How Token Tracking Works​

Token Usage Metrics​

Use Cases​

1. Resource Planning​

2. Model Evaluation​

3. Cost Management​

4. User Engagement​

5. Compliance & Auditing​

Technical Details​

Data Storage​

Database Indexes​

API Endpoints​

Privacy & Data Considerations​

What Gets Tracked?​

Data Retention​

Frequently Asked Questions​

Why are message counts different from what I expected?​

How accurate is token tracking?​

Can I export analytics data?​

Summary​

Related Features​

Overview

Accessing Analytics

Dashboard Features

Time Period Selection

Group Filtering

Summary Statistics

Message Timeline Chart

Model Usage Table

Model Details Modal

Overview Tab

Chats Tab

User Activity Table

Token Usage Tracking

What Are Tokens?

How Token Tracking Works

Token Usage Metrics

Use Cases

1. Resource Planning

2. Model Evaluation

3. Cost Management

4. User Engagement

5. Compliance & Auditing

Technical Details

Data Storage

Database Indexes

API Endpoints

Privacy & Data Considerations

What Gets Tracked?

Data Retention

Frequently Asked Questions

Why are message counts different from what I expected?

How accurate is token tracking?

Can I export analytics data?

Summary

Related Features