Explain VCU consumption better #2602
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Explain Ingest, Search, ML VCU consumption better. I see this confusion on - Are search VCUs directly related to number of searches? Are ingest VCUs directly related to number of ingest operations? I have added info to break down what search/ingest/ML VCU consumption is driven by.
🔍 Preview links for changed docs |
kilfoyle
reviewed
Aug 18, 2025
deploy-manage/cloud-organization/billing/elasticsearch-billing-dimensions.md
Outdated
Show resolved
Hide resolved
…-dimensions.md Co-authored-by: David Kilfoyle <[email protected]>
ppf2
reviewed
Aug 27, 2025
| * **Machine learning:** The VCUs used to perform inference, NLP tasks, and other ML activities. | ||
| * **Indexing:** The VCUs used to index incoming documents. Indexing VCUs account for compute resources consumed for ingestion. This is based on ingestion rate, and amount of data ingested at any given time. Transforms and ingest pipelines also contribute to ingest VCU consumption. | ||
| * **Search:** The VCUs used to return search results, with the latency and queries per second (QPS) you require. Search VCUs are calculated as a factor of the compute resources needed to run search queries, search throughput and latency. Search VCUs are not charged per search request, but instead are a factor of the compute resources that scale up and down based on amount of searchable data, search load (QPS) and performance (latency and availability). | ||
| * **Machine learning:** The VCUs used to perform inference, NLP tasks, and other ML activities. ML VCUs are a factor of the models deployed, and number of ML operations such as inference for search and ingest. ML VCUs are typically consumed for generating embeddings during ingestion, and during semantic search or reranking. |
There was a problem hiding this comment.
@shubhaat Catching up on GH notifications :D I wonder if we want to mention that ML VCUs only apply if they use models deployed on ML nodes, i.e. if they use Elastic Managed LLMs, there is no ML VCU costs, only token charges.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Explain Ingest, Search, ML VCU consumption better. I see this confusion on -
Are search VCUs directly related to number of searches? Are ingest VCUs directly related to number of ingest operations?
I have added info to break down what search/ingest/ML VCU consumption is driven by.