Billing & Usage Explained
This page explains how billing and usage work in SynapticWorks so teams can plan confidently, monitor activity, and avoid surprises at the end of the month.
Overview of billing
SynapticWorks billing is based on how much platform usage your workspace generates during a billing period. Instead of charging by feature clicks, usage is measured from real activity such as AI tokens, job processing, and storage.
At a high level, your monthly invoice combines:
- Base plan charges (if included in your plan)
- Variable usage charges from platform activity
- Any agreed add-ons or overage items
Token usage
Token usage tracks text processed by AI models.
- Input tokens are the text sent to a model (prompts, instructions, context).
- Output tokens are the generated response.
- Total token usage is input + output.
Think of tokens as pieces of text, not full words. Short words may be one token. Longer strings or punctuation-heavy content may use more.
### Example
If a prompt uses 700 input tokens and the model returns 300 output tokens, the request consumed 1,000 total tokens.
Jobs and processing usage
A job is a unit of work handled by SynapticWorks, such as processing an inbound event, running a transformation step, or delivering an outbound action.
Processing usage can include:
- Number of jobs executed
- Retry attempts when temporary failures occur
- Additional processing stages in larger workflows
More jobs and more retries generally increase usage.
Storage usage
Storage usage represents data retained by the platform for normal operations, reporting, and recent history.
Typical storage contributors include:
- Message payloads and metadata
- Workflow execution details
- Logs retained within your plan window
Longer retention and higher traffic usually increase storage usage.
Usage preview vs monthly rollups
SynapticWorks provides two views of usage:
- Usage preview: Near-real-time estimates so teams can monitor current activity.
- Monthly rollups: Finalized totals for invoicing and period reporting.
Use preview data for day-to-day awareness. Use rollups for financial reconciliation.
Soft vs hard limits
Limits help teams control spend and platform behavior.
- Soft limits warn you when usage approaches a threshold.
- Hard limits stop or restrict new usage once a threshold is reached.
Soft limits are useful for proactive monitoring. Hard limits are useful when strict spending controls are required.
How usage is reported
Usage is reported in two primary ways:
- UI dashboards for quick visibility into trends and totals
- Operational logs for detailed activity records
Dashboards help with high-level decisions. Logs help explain specific spikes, retries, or workflow changes.
Avoiding unexpected charges
Best practices:
- Set soft alerts before expected peak traffic windows.
- Review high-token prompts and shorten unnecessary context.
- Monitor retry patterns and fix recurring transient errors.
- Archive or prune non-essential historical data where possible.
- Check preview usage weekly, not only at month-end.
Example cost scenarios
### Scenario 1: Support automation month
A support team launches new AI-assisted responses. Token usage rises, but job volume stays stable. Most of the cost increase comes from model activity.
### Scenario 2: Integration incident month
A downstream service becomes unstable for several hours. Retries increase job processing usage. Costs rise even though customer traffic is unchanged.
### Scenario 3: Seasonal campaign month
A campaign drives more inbound events and longer conversations. Token usage, jobs, and storage all increase together. Preview tracking helps the team react early.