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Streamkap charges based on data volume (in GB) using the maximum of inbound (from sources) and outbound (to destinations) data. The Billing page provides comprehensive usage tracking across multiple time ranges, helping you understand consumption patterns and optimize costs.

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Pricing Model

Streamkap uses a volume-based pricing model:
  • Charged based on: GB of data using the maximum of inbound (sources) and outbound (destinations)
  • Billing metric: Maximum of inbound and outbound data
  • Volume discounts: Available for larger consumption tiers
When migrating from a Monthly Active Rows model to a per-GB model, use an approximate conversion rate of 5 million rows per GB.
For the latest pricing details, visit the Streamkap Pricing Page.

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Usage Overview

The Billing page displays usage data across three key metrics: Billable Data, Inbound Data, and Outbound Data.
Billing usage overview showing Billable Data, Inbound Data, and Outbound Data tiles across multiple time ranges

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Billable Data

The primary billing metric represents the maximum of inbound and outbound data for each time period:
  • 1 HOUR: Real-time usage in the past hour
  • 1 DAY: Total usage in the past 24 hours
  • 7 DAYS: Weekly usage accumulation
  • 30 DAYS: Monthly usage (common billing cycle)
  • 90 DAYS: Quarterly usage trends
  • 365 DAYS: Annual usage for long-term planning
Calculation: Billable Data = MAX(Inbound Data, Outbound Data) for each time period.
The billable amount uses the maximum to ensure you’re charged for the actual data movement, whether it’s ingestion from sources or delivery to destinations.

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Inbound Data

Tracks data read from source connectors into Streamkap Kafka topics:
  • Measures raw data ingested from databases (MySQL, PostgreSQL, DynamoDB, etc.)
  • Includes change data capture (CDC) events and initial snapshots
  • Source of truth for data entering your Streamkap service

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Outbound Data

Tracks data written from Kafka topics to destination connectors:
  • Measures data delivered to data warehouses and lakes
  • Includes writes to BigQuery, Snowflake, ClickHouse, Databricks, etc.
  • May differ from inbound if using filtering, transformations, or partial replication
Outbound data can be lower than inbound if you filter data before writing to destinations, or higher if you replicate the same source data to multiple destinations.

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Service Selector

Use the dropdown in the top-right corner (e.g., “Streamkap Demo”) to switch between different Streamkap services and view usage for each service independently.

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Billing Metrics Chart

The interactive time series chart visualizes data flow over time:
Billing metrics chart displaying time series data for Sources Volume and Destinations Volume over the last 24 hours
Features:
  • Time Range Selector: Last 24 hours, Last 7 days, Last 30 days
  • Metric Toggle: Select which metrics to display (Destinations - Volume, Sources - Volume)
  • Hover Tooltips: View exact values at specific time points
  • Data Granularity: Hourly for 24h view, daily for longer periods
  • Legend: Click legend items to show/hide metric lines
Use Cases:
  • Identify usage spikes and patterns
  • Correlate usage with business events
  • Plan capacity and budget based on trends
  • Detect anomalies or unexpected data volumes

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Connector Usage Tables

Track usage per individual connector with detailed breakdowns.

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Sources - Inbound

Lists all source connectors with their inbound data usage:
Sources Inbound table showing source connector usage data across 1 Hour, 1 Day, and 7 Days time periods
Table Columns:
  • Source Connector: Connector name with icon (click to navigate to connector detail)
  • 1 Hour: Data ingested in the past hour
  • 1 Day: Data ingested in the past 24 hours
  • 7 Days: Weekly ingestion volume
  • Additional time ranges available via column selector
Features:
  • Search: Filter sources by name
  • Export Usage: Download usage data as CSV for reporting
  • Column Selector: Choose which time periods to display (1 Hour, 1 Day, 7 Days, 30 Days, 90 Days, 365 Days)
  • Show/Hide Table: Toggle table visibility with checkbox
  • Pagination: Navigate through large connector lists
Sorting:
  • Click column headers to sort by usage in that time period
  • Identify highest-consuming sources quickly

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Destinations - Outbound

Lists all destination connectors with their outbound data usage:
Destinations Outbound table showing destination connector usage data across 1 Hour, 1 Day, and 7 Days time periods
Table Columns:
  • Destination Connector: Connector name with icon (click to navigate to connector detail)
  • 1 Hour: Data written in the past hour
  • 1 Day: Data written in the past 24 hours
  • 7 Days: Weekly write volume
  • Additional time ranges available via column selector
Features:
  • Same search, export, column selector, and pagination capabilities as Sources table
  • Helps identify destinations consuming the most bandwidth
  • Useful for optimizing replication strategy
Use the Export Usage button to download CSV files for integration with your accounting systems or for detailed cost analysis.

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Understanding Your Bill

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How Billing is Calculated

  1. Measure Period: Typically 30 days (monthly billing cycle)
  2. Calculate Billable Data: MAX(Inbound Data, Outbound Data) for the period
  3. Apply Pricing Tier: Based on total volume and any volume discounts
  4. Generate Invoice: Charges based on actual consumption

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Example Calculation

If in a 30-day period you have:
  • Inbound Data: 331.39 MB
  • Outbound Data: 404.41 MB
Your Billable Data would be 404.41 MB (the maximum), and you’d be charged based on that volume according to your pricing tier.

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Cost Optimization Strategies

  1. Filter Early: Use table/column filters in source connectors to reduce inbound volume
  2. Optimize Replication: Only replicate to destinations that need the data
  3. Use Transforms: Apply data transformations to reduce payload sizes
  4. Monitor Trends: Watch the metrics chart for unexpected spikes
  5. Review Per-Connector: Identify high-volume connectors and optimize their configuration
  6. Compress Data: Use compression settings where available
  7. Schedule Snapshots: Run full snapshots during off-peak times if possible

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Monitoring Usage

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Daily Monitoring

Check the 1 DAY cards daily to:
  • Track daily consumption
  • Detect anomalies early
  • Stay within budget expectations

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Weekly Reviews

Review the 7 DAYS metrics weekly to:
  • Identify weekly patterns
  • Adjust connector settings if needed
  • Plan for upcoming billing cycles

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Monthly Planning

Analyze the 30 DAYS metrics at month-end to:
  • Reconcile with billing statements
  • Forecast next month’s costs
  • Make strategic optimization decisions
Use 365 DAYS data for:
  • Long-term capacity planning
  • Budget forecasting for next fiscal year
  • Negotiating volume-based discounts

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Best Practices

  1. Set Up Alerts: Monitor usage thresholds and get notified of spikes (see Alerts)
  2. Regular Exports: Download connector usage reports monthly for record-keeping
  3. Optimize Sources: Start with source-side filtering to reduce overall data movement
  4. Review Inactive Connectors: Pause or delete connectors that aren’t actively used
  5. Test in Non-Production: Use separate services for testing to avoid unexpected charges
  6. Understand Your Data: Know which tables/topics generate the most volume
  7. Plan for Growth: Use historical data to forecast usage as your business scales
  8. Leverage Volume Discounts: Contact sales if you’re approaching higher usage tiers

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Troubleshooting

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Usage Doesn’t Match Expectations

If usage numbers seem incorrect:
  1. Check Time Zone: Usage metrics use UTC timestamps
  2. Verify Connectors: Ensure all connectors are accounted for
  3. Review Recent Changes: Check if new sources/destinations were added
  4. Inspect High-Volume Sources: Sort connector tables by usage to find outliers
  5. Check for Snapshots: Full table snapshots can cause large usage spikes
  6. Review Logs: Check connector logs for unusual activity (Logs)

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Unexpected Billing Spike

If you see a sudden increase in usage:
  1. Check Metrics Chart: Identify exactly when the spike occurred
  2. Review Connector Tables: Find which connector(s) caused the spike
  3. Inspect Connector Logs: Look for error patterns or configuration changes
  4. Verify Data Sources: Check if source databases had bulk inserts or updates
  5. Contact Support: Reach out to Streamkap support for billing questions

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Zero Usage Showing

If connectors show 0 bytes but should have data:
  1. Check Connector Status: Ensure connectors are running (Pipelines)
  2. Verify Time Range: Some time periods may not have data yet (e.g., past hour)
  3. Refresh Page: Reload to fetch latest usage data
  4. Check Date Range: Ensure you’re looking at the correct time window
  5. Review Pipeline Logs: Confirm data is actually flowing
  • Sources - Configure source connectors that generate inbound data
  • Destinations - Configure destination connectors that consume outbound data
  • Pipelines - Manage data pipelines between sources and destinations
  • Topics - View Kafka topic-level usage and message flow
  • Alerts - Set up usage threshold alerts
  • Services - Manage multiple Streamkap services
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