Understand Your Pipeline Usage
You can monitor your current pipeline usage, pipeline hours, and document storage units (DSU) anytime on the Usage dashboard.
You must be an Organization Admin to view the Usage dashboard.
Navigating the Usage Dashboard
All your usage information is visible on the Usage dashboard, which you can access from organization settings:
- Click your profile icon and choose Settings.
- Go to Organization>Usage.
The top of the dashboard shows a summary of all the information. To see how the data breaks down by pipeline, check the table at the bottom of the dashboard.
In the summary, you can check your organization, your credits, and your document storage units. The numbers in the image match the sections that follow:
1. Your Organization
Your organization name and time frame for the usage data.
2. Credits
Credits are units that represent the number of hours your pipelines were deployed or your indexes were processing files. This includes pipelines that were deleted but contributed to credits usage during this usage cycle.
One credit is one hour of your pipelines being deployed or your indexes processing files.
You consume credits when:
- Your pipeline is deployed and active.
- Your index is actively processing files.
No credits are consumed when:
- Your pipeline is deployed but inactive, on standby.
- Your index is enabled but is not indexing any files as indexing is complete. Note that every time you upload a file, the index processes it, which counts toward credit consumption.
Production and Development Pipelines
Your pipelines can be either production or development. This affects the number of replicas they use and the number of credits they consume. You can configure the number of replicas and the idle timeout in pipeline settings.
- Production pipelines have at least one replica running by default and scale up to 10 with heavy traffic. The more replicas running, the more credits you use.
- Development pipelines have no replicas by default. For every hour a development pipeline is deployed and active, you use one credit.
For details on service levels and how to change them, see Pipelines.
What Changed: Credits vs Production and Development Hours
Previously, we tracked deployment hours separately for production and development pipelines. This included indexing time, as indexes were tied to query pipelines.
Now, we've simplified things. We no longer distinguish between development and production hours. Instead, we measure all active deployment time and all indexing activity in credits:
- One credit per hour per active replica
- One credit per hour of indexing
Estimating Your Credit Usage
When planning your credit usage, it's helpful to understand how credits are consumed across different parts of the system.
Query Pipelines
Credits for query pipelines are based on how long your pipeline is actively deployed and how many replicas it uses.
- Development pipeline: 1 credit per hour (no replicas by default)
- Production pipeline: 1 replica by default = 1 credit per hour minimum
If your production pipeline scales due to high traffic, credit usage increases proportionally with the number of active replicas (up to a maximum of 10). You can configure the minimum and maximum number of replicas in the pipeline's Settings tab.
Indexing
Credits for indexes are only used when files are being processed. You don’t need to index the same data more than once. After indexing, you can reuse that data across different query pipelines without additional credit costs.
Several factors can affect how many credits indexing will use:
- Size and number of files: Larger and more files take longer to process.
- Index configuration: Preprocessing steps like OCR or generating metadata with a language model increase processing time.
- Upload pattern: Uploading files in a single batch or in several large batches is more efficient. Uploading files one-by-one with long gaps in between may add warm-up time.
- Embedding model: The dimensionality of the embedding model, the number of models used, and whether they're hosted locally or accessed via API can impact speed and credit usage.
Services
Credits for services are based on how long the service has active replicas running. The minimum replica count you configure sets the baseline cost.
- 1 credit = 1 active replica-hour
- Standby and inactive services consume no credits
A service with 2 minimum replicas uses 2 credits per hour while active. You can track per-service credit usage on the service's Metrics tab.
Example Credit Usage
Here are some rough estimates for how many credits are used by our standard index templates. This scenario assumes:
- 100 files in a mix of formats (TXT, HTML, XLSX, PDF, DOCX, CSV)
- All files uploaded together in one batch
| Index Template | Approximate Credits Used |
|---|---|
| Standard Index (English) | 0.03 |
| AI-Generated Metadata | 0.06 |
These figures can vary depending on your specific setup, but they offer a starting point for estimating costs.
3. Document Storage Units
Document Storage Units (DSU) show how much vector storage you're using for your indexed documents. The DSU count on the Usage page reflect the current state. They can go up or down as you add or remove documents.
When you upload and index your files, we split them into smaller pieces called documents. These documents are stored and your DSU count depends on two key factors:
- How many documents your files are split into.
- The size (dimensionality) of the embedding model used to process them.
The more documents or the larger the model, the more storage units you use.
Calculating DSUs
To calculate DSUs, we multiply the number of stored documents by a multiplier that reflects the embedding model's dimensionality:
- A standard embedding model with 768 dimensions uses a multiplier of 1.
- A larger model with 1,536 dimensions uses a multiplier of 2, meaning it needs twice as much storage.
- If you use keyword-based search or don't use vector storage, the multiplier is just 0.2, so it uses much less storage.
Example
Say you upload 5,000 files which are split into 83,697 documents:
- With the standard embedding model (768 dimensions):
83,697 documents × 1 = 83,697 storage units - With a larger model (1,536 dimensions):
83,697 documents × 2 = 167,394 storage units - With keyword search only or no vector storage:
83,697 documents × 0.2 = 16,739.4 storage units
Optimizing Storage Usage
As you near your storage capacity, you can reduce storage consumption by:
- Deleting duplicate files.
- Reducing irrelevant files, starting from the largest ones. Detecting irrelevant files is specific to your use case. You can use the Search History endpoint to check user queries, files, and documents used.
- Adjusting settings in DocumentSplitter:
- If your
split_overlapsetting is high, consider decreasing it. - If your
split_lengthsetting is low, try increasing it. Remember that it's important to avoid too large values to prevent Retriever and Generator components from cutting the documents.
- If your
Credits per Service
The Services tab at the bottom of the Usage page opens by default and shows how many credits each service used in the current usage cycle. Use it to find out which services use the most credits and to compare them across workspaces.
- Service: The name of the service. If a service was deleted but used credits in this cycle, the section shows its deployment ID instead of a name.
- Credits used: The total credits the service used in the current usage cycle. If a service used credits under more than one credit type, the section shows the combined total.
- Workspace: The workspace the service belongs to. The section includes services from every workspace that used credits in this cycle.
Usage on shared serverless capacity, which isn't tied to a single service, appears in its own Serverless row.
If a service's credits on its Metrics tab don't match the Services tab, check the time range. Both use the same data, but the Metrics tab shows credits for the time window you select (1 hour, 1 day, 7 days, or 30 days) rather than for the usage cycle. When both cover the same period, the numbers match.
4. Usage Per Pipeline or Index
The detailed usage per pipeline or index is split into two tabs next to the Services tab: Indexes and Pipelines, tagged Legacy. Each tab loads its list when you open it and shows the credits used and the workspace for each index or pipeline. Document storage units appear only on the Indexes tab.
Pipeline credits include time run on services. The Pipelines tab has a Last ran on column that links each pipeline to the service it last ran on.
At the bottom of the page, you can check the detailed usage per pipeline or index. You can check the number of credits used and the workspace it's in.
Pipeline credits include time the pipeline ran on services, as a note above the table reminds you. If a pipeline's credits look higher than its own deployment time suggests, it may also have run on a service.
To check whether a pipeline ran on a service, for example when its credits look high, check the Last ran on column:
- Service: The pipeline last ran on a service. Click the link to open that service.
- Serverless: The pipeline last ran on serverless compute and not on a service.
- N/A: We don't have information about where the pipeline last ran.
Indexes and pipelines that were deleted but contributed to usage in the current cycle are greyed out.
To track estimated LLM spend and token usage, see View Cost and Token Usage.
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