Usage Analytics

Real-time dashboard for AI model usage, costs, tokens, and latency. The most comprehensive observability for any LLM gateway

Requesty’s analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, tokens, cache savings, and latency, all in real-time.

View your analytics dashboard in the Requesty Console.

Dashboard Tabs

The analytics dashboard has three main views:

Tab	What it shows
General	Cost overview, request volume, token usage, latency, and cost savings
Savings	Cache hit rates, token cache rates, and savings per model
Advanced	Fully customizable analytics with flexible grouping, metrics, and filters

General Tab

The General tab gives you an at-a-glance overview with six charts:

• Cost Overview: Total cost of API calls over time, broken down by model or group. When using BYOK, shows Requesty cost vs provider cost separately.
• Request Volume: Total number of API requests over time.
• Cost Savings: Dollar amount saved through caching and optimization.
• Cost Savings %: Gauge showing your current savings percentage (e.g., 71.8% in the example above).
• Token Usage: Total tokens processed, input, output, and cached.
• Total Request Latency: Average, P50, or P90 latency in milliseconds.

Below the charts, a breakdown table shows per-model metrics: requests (with %), tokens (with %), cost, and average latency.

Time Range

Select from preset ranges or set a custom period:

• Quick: 7 Days, 30 Days, This Week, This Month, This Quarter, This Year
• Extended: 24 Hours, Last 3 Months, Last 6 Months, Last 12 Months
• Custom Date Range: Pick an exact start and end date/time for full control

Grouping & Filters

Use the toolbar to slice your data:

• Time Grouping: Hour, Day, Week, or Month
• Group By: Model, Provider, User, Member, API Key, or any custom metadata field
• Filters: Filter by any field, supports multiple values (value1,value2) and wildcards (*pattern*)

Savings Tab

The Savings tab shows how much you’re saving through caching:

• Cost Savings ($): Total dollar savings from cache hits and optimizations
• Cost Savings (%): Percentage of costs saved vs what you would have paid without caching
• Cache Hit Rate: Percentage of requests served from cache
• Token Cache Rate: Percentage of tokens served from cache

A Cache Performance by Model table ranks models by cache effectiveness, showing hit rate, token cache rate, and savings per model.

Enable Auto Caching to start seeing savings. A 30%+ cache hit rate typically translates to 25-40% cost reduction.

Advanced Tab

The Advanced tab is a fully flexible analytics workbench for deep analysis.

Controls

Control	Options
Group By	Model, Provider, User, Member, or any custom field, or “None” for totals
Metric	Cost, Requests, Input Tokens, Output Tokens, Cached Tokens, Total Tokens, Latency, Cost Savings, and more
Calculation	Sum, Average, Median, Count Distinct, P95, P99
Time Range	24h, 3d, 7d, 30d, 3m, 6m, 12m, or custom date range
Time Grouping	None (total), Minute, Hour, Day
Filters	Dynamic field-value filters with wildcard support

Data Summary Table

Below the chart, a pivot table shows the raw data with:

• Sortable columns (click any column header)
• Toggle individual series visibility
• Show values as percentages
• Hide zero-value rows
• Export to CSV or PDF, download the full dataset for external analysis or generate a branded PDF report

Learn more about exports →

Category Grouping

Select multiple series in the data table and group them into a custom category. Useful for combining related models (e.g., group all Claude variants into “Anthropic”) for high-level comparisons.

Filtering Examples

Filter by specific model

Add a filter: model = anthropic/claude-sonnet-4-5

Filter by user pattern

Add a filter: user = *@company.com

Filter by multiple models

Add a filter: model = openai/gpt-4.1,anthropic/claude-sonnet-4-5

Combine Group By with Filters

Group by user and filter by model = anthropic/* to see which users use Anthropic models the most.

Custom Metadata

Tag your requests with custom fields using Request Metadata, then filter and group by those fields in analytics. For example, tag requests with environment, feature, or customer_id and analyze usage per dimension.

Integration with Other Features

• Cost Tracking. Deeper cost analysis and optimization
• Performance Monitoring. Latency and reliability metrics
• Session Reconstruction. Drill into individual request sessions
• Spending Alerts. Get notified when spending exceeds thresholds
• Request Metadata. Add custom dimensions for analytics filtering