What is an Instance?
An instance represents a distinct product, deployment, or tenant within your AI application. All analytics in Thunder are scoped to instances - topics, signals, gaps, and metrics are tracked separately for each instance.When to Use Multiple Instances
Multiple Products
If you have separate AI products (support bot, sales assistant, etc.) - each product gets its own instance.
Environment Separation
Keep production and staging analytics separate by using different instances.
Single Instance
If you have one AI product with one deployment, you can use a single instance. Thunder creates a default instance for you when you first ingest data without specifying aninstanceId.
Creating Instances
Instances can be created in two ways:- From the dashboard - Create and manage instances through the Thunder dashboard
- Automatically on first ingest - If you don’t pass an
instanceId, Thunder creates one for you:
instanceId that was created - store this to use for future requests.
Querying by Instance
All query requests require aninstanceId:
Instance Isolation
Each instance has completely separate:| Data | Description |
|---|---|
| Topics | Topic detection learns from each instance’s conversations |
| Signals | Satisfaction signals are tracked per instance |
| Gaps | Knowledge and tool gaps are detected per instance |
| Users | End user tracking is scoped to each instance |
| Sessions | All sessions belong to exactly one instance |
Best Practices
Plan your instance structure early
Plan your instance structure early
Decide how you want to segment analytics before you start ingesting data. Changing instance structure later requires re-ingesting historical data.
Use meaningful instance IDs
Use meaningful instance IDs
While Thunder accepts any UUID, use IDs that map to entities in your system (clone IDs, product IDs) for easier correlation.
Don't over-segment
Don't over-segment
More instances means more fragmented data. Only create separate instances when you genuinely need isolated analytics.