---
layout: docs
page_title: Client count calculation
description: |-
  Technical overview of client count calculations in Vault
---

# Client count calculation

Vault provides usage telemetry for the number of clients based on the number of
unique entity assignments within a Vault cluster over a given billing period:

- Standard entity assignments based on authentication method for active entities.
- Constructed entity assignments for active non-entity tokens, including batch
  tokens created by performance standby nodes.
- Certificate entity assignments for ACME connections.
- Secrets being synced to at least one sync destination.

```markdown
CLIENT_COUNT_PER_CLUSTER = UNIQUE_STANDARD_ENTITIES +
                           UNIQUE_CONSTRUCTED_ENTITIES +
                           UNIQUE_CERTIFICATE_ENTITIES +
                           UNIQUE_SYNCED_SECRETS
```

Vault does not aggregate or de-duplicate clients across clusters, but all logs
and precomputed reports are included in DR replication.

<Note heading="Certificate entities currently reported as non-entity clients">

  Vault currently rolls certificate entities into the non-entity client count
  in the UI and API query requests. For more detailed information on certificate
  entities, use the
  [internal counter endpoint](/vault/api-docs/system/internal-counters) to query
  monthly data for the PKI mount path or export historic data and look for
  `client_type=pki-acme`.

</Note>

## How Vault tracks clients

Each time a client authenticates, Vault checks whether the corresponding entity
ID has already been recorded in the client log as active for the current month:

- **If no record exists**, Vault adds an entry for the entity ID.
- If a record exists but the entity was last active **prior to the current month**,
  Vault adds a new entry to the client record for the entity ID.
- If a record exists and the entity was last active **within the current month**,
  Vault does not add a new entry to the client record for the entity ID.

For example:

- Two non-entity tokens under the same namespace, with the same alias name and
  policy assignment receive the same entity assignment and are only counted
  **once**.
- Two authentication requests from a single ACME client for the same certificate
  identifiers from different mounts receive the same entity assignments and
  are counted **once**.
- An application authenticating with AppRole receive the same entity assignment
  every time and only counted **once**.

At the **end of each month**, Vault pre-computes reports for each cluster on the
number of active entities, per namespace, for each time period within the
configured retention period. By de-duplicating records from the current month
against records for the previous month, Vault ensures entities that remain
active within every calendar month are only counted once for the year.

The deduplication process has two additional consequences:

1. Detailed reporting lags by 1 month at the start of the billing period.
1. Billing period reports that include the current month must use an
   approximation for the number of new clients in the current month.

## How Vault approximates current-month client count

Vault approximates client count for the current month using a
[hyperloglog algorithm](https://en.wikipedia.org/wiki/HyperLogLog) that looks
at the difference between the cardinalities of:

- the number of clients across the **entire** billing period, and
- the number of clients across the billing period **excluding** clients from the current month.

The approximation algorithm uses the
[axiomhq](https://github.com/axiomhq/hyperloglog) library with fourteen
registers and sparse representations (when applicable). The multiset for the
calculation is the total number of clients within a billing period, and the
accuracy estimate for the approximation decreases as the difference between the
number of clients in the current month and the number of clients in the billing
period increases.

### Testing verification for client count approximations

Given `CM` as the number of clients for the current month and `BP` as the number
of clients in the billing period, we found that the approximation becomes
increasingly imprecise as:

- the difference between `BC` and `CM` increases
- the value of `CM` approaches zero.
- the number of months in the billing period increase.

The maximum observed error rate
(`ER = (FOUND_NEW_CLIENTS / EXPECTED_NEW_CLIENTS)`) was 30% for 10,000 clients
or less, with an error rate of 5 &ndash; 10% in the average case.

For the purposes of predictive analysis, the following tables list a random
sample the values we found during testing for `CM`, `BP`, and `ER`.

<Tabs>

<Tab heading="Single-month tests">

| Current month (`CM`) | Billing period (`BP`) | Error rate (`ER`) |
| :-----------------: | :------------------: | :---------------: |
| 7                   | 10                   | 0%                |
| 20                  | 600                  | 0%                |
| 20                  | 1000                 | 0%                |
| 20                  | 6000                 | 10%               |
| 20                  | 10000                | 10%               |
| 200                 | 600                  | 0%                |
| 200                 | 10000                | 7%                |
| 400                 | 6000                 | 5%                |
| 2000                | 10000                | 4%                |

</Tab>

<Tab heading="Multi-month / multi-segment tests">

| Current month (`CM`) | Billing period (`BP`) | Error rate (`ER`) |
| :-----------------: | :------------------: | :---------------: |
| 20                  | 15                   |      0%           |
| 20                  | 100                  |      0%           |
| 20                  | 1000                 |      0%           |
| 20                  | 10000                |      30%          |
| 200                 | 10000                |      6%           |
| 2000                | 10000                |      2%           |

</Tab>

</Tabs>

## Resource costs for client computation

In addition to the storage used for storing the pre-computed reports, each
active entity in the client log consumes a few bytes of storage. As a safety
measure against runaway storage growth, Vault limits the number of entity
records to 656,000 per month, but typical storage costs are much less.

On average, 1000 monthly active entities requires 3.0 MiB of storage capacity
over the default 48-month retention period.

@include "content-footer-title.mdx"

<Tabs>

<Tab heading="Related concepts">
<ul>
  <li>
    <a href="/vault/docs/concepts/client-count/">Clients and entities</a>
  </li>
  <li>
    <a href="/vault/docs/concepts/client-count/faq">Client count FAQ</a>
  </li>
</ul>
</Tab>

<Tab heading="Related API docs">
<ul>
  <li>
    <a href="/vault/api-docs/system/internal-counters#client-count">Client Count API</a>
  </li>
  <li>
    <a href="/vault/api-docs/system/internal-counters">Internal counters API</a>
  </li>
</ul>
</Tab>

<Tab heading="Related tutorials">
<ul>
  <li>
    <a href="/vault/tutorials/monitoring/usage-metrics">
      Vault Usage Metrics in Vault UI
    </a>
  </li>
  <li>
    <a href="/vault/tutorials/monitoring/usage-metrics">KMIP Client metrics</a>
  </li>
</ul>
</Tab>

<Tab heading="Other resources">
<ul>
  <li>
    <a href="https://github.com/axiomhq/hyperloglog#readme">Accuracy estimates for the axiomhq hyperloglog library</a>
  </li>
  <li>
    Blog post: <a href="https://www.hashicorp.com/blog/onboarding-applications-to-vault-using-terraform-a-practical-guide">
      Onboarding Applications to Vault Using Terraform: A Practical Guide
    </a>
  </li>
</ul>
</Tab>

</Tabs>
