Set monthly spend alerts
You can set up email alerts to monitor your organization’s monthly spending. These alerts notify designated recipients when spending reaches specified thresholds. The alerts automatically reset at the start of each monthly billing cycle.
To set a spend alert:
- Go to Settings > Spend alerts in the Pinecone console
- Click + Add Alert.
- Enter the dollar amount for the spend alert.
- Enter the email addresses to send the alert to. Organization owners are listed by default.
- Click Create.
To edit a spend alert:
- In the row of the spend alert you want to edit, click ellipsis (…) menu > Edit.
- Change the dollar amount and/or email addresses for the spend alert.
- Click Update.
Auto-spend spike alert: To protect from unexpected cost increases, Pinecone sends an alert when spending exceeds double your previous month’s invoice amount. While the alert threshold is fixed and the alert cannot be deleted, you can modify which email addresses receive the alert and enable or disable the alert notifications.
Monitor organization-level usage
You must be the organization owner to view usage across your Pinecone organization. Also, this feature is available only to organizations on the Standard or Enterprise plans. Monitor token usage
Requests to the chat, context retrieval, and evaluation API endpoints return a usage parameter with prompt_tokens, completion_tokens, and total_tokens generated.
Chat
Context retrieval
Response evaluation
For chat, tokens are defined as follows:
-
prompt_tokens are based on the messages sent to the assistant and the context snippets retrieved from the assistant and sent to a model. Messages sent to the assistant can include messages from the chat history in addition to the newest message.
prompt_tokens appear as Assistants Input Tokens on invoices.
-
completion_tokens are based on the answer from the model.
completion_tokens appear as Assistants Output Tokens on invoices.
-
total_tokens is the sum of prompt_tokens and completion_tokens.
{
"finish_reason": "stop",
"message": {
"role": "assistant",
"content": "The Chief Financial Officer (CFO) of Netflix is Spencer Neumann."
},
"id": "000000000000000030513193ccc52814",
"model": "gpt-4o-2024-11-20",
"usage": {
"prompt_tokens": 23626,
"completion_tokens": 21,
"total_tokens": 23647
},
"citations": [
{
"position": 63,
"references": [
{
"file": {
"status": "Available",
"id": "99305805-3844-41b5-af56-ee693ab80527",
"name": "Netflix-10-K-01262024.pdf",
"size": 1073470,
"metadata": null,
"updated_on": "2025-07-29T20:07:53.171752661Z",
"created_on": "2025-07-29T20:07:36.361322699Z",
"percent_done": 1,
"signed_url": "https://storage.googleapis.com/...",
"error_message": null
},
"pages": [
78,
79,
80
],
"highlight": null
},
{
"file": {
"status": "Available",
"id": "99305805-3844-41b5-af56-ee693ab80527",
"name": "Netflix-10-K-01262024.pdf",
"size": 1073470,
"metadata": null,
"updated_on": "2025-07-29T20:07:53.171752661Z",
"created_on": "2025-07-29T20:07:36.361322699Z",
"percent_done": 1,
"signed_url": "https://storage.googleapis.com/...",
"error_message": null
},
"pages": [
77,
78
],
"highlight": null
}
]
}
]
}
For context retrieval, tokens are defined as follows:
-
prompt_tokens are based on the messages sent to the assistant and the context snippets retrieved from the assistant. Messages sent to the assistant can include messages from the chat history in addition to the newest message.
prompt_tokens appear as Assistants Context Tokens Processed on invoices.
-
completion_tokens do not apply for context retrieval because, unlike for chat, there is no answer from a model. completion_tokens will always be 0.
-
total_tokens is the sum of prompt_tokens and completion_tokens.
{
"snippets": [
{
"type": "text",
"content": "edures, or caused such disclosure controls and procedures to be designed under our supervision, to\r\nensure that material information relating to the registrant, including its consolidated subsidiaries, ...",
"score": 0.86632514,
"reference": {
"type": "pdf",
"file": {
"status": "Available",
"id": "99305805-3844-41b5-af56-ee693ab80527",
"name": "Netflix-10-K-01262024.pdf",
"size": 1073470,
"metadata": null,
"updated_on": "2025-07-29T20:07:53.171752661Z",
"created_on": "2025-07-29T20:07:36.361322699Z",
"percent_done": 1,
"signed_url": "https://storage.googleapis.com/...",
"error_message": null
},
"pages": [
78,
79,
80
]
}
},
...
],
"usage": {
"prompt_tokens": 22914,
"completion_tokens": 0,
"total_tokens": 22914
},
"id": "00000000000000007b6ad859184a31b3"
}
For response evaluation, tokens are defined as follows:
-
prompt_tokens are based on two requests to a model: The first request contains a question, answer, and ground truth answer, and the second request contains the same details plus generated facts returned by the model for the first request.
prompt_tokens appear as Assistants Evaluation Tokens Processed on invoices.
-
completion_tokens are based on two responses from a model: The first response contains generated facts, and the second response contains evaluation metrics.
completion_tokens appear as Assistants Evaluation Tokens Out on invoices.
-
total_tokens is the sum of prompt_tokens and completion_tokens.
Response evaluation response
{
"metrics": {
"correctness": 123,
"completeness": 123,
"alignment": 123
},
"reasoning": {
"evaluated_facts": [
{
"fact": {
"content": "<string>"
},
"entailment": "entailed"
}
]
},
"usage": {
"prompt_tokens": 123,
"completion_tokens": 123,
"total_tokens": 123
}
}
