- Elastic Cloud Serverless
- Elasticsearch
- Elastic Observability
- Get started
- Observability overview
- Elastic Observability Serverless billing dimensions
- Create an Observability project
- Quickstart: Monitor hosts with Elastic Agent
- Quickstart: Monitor your Kubernetes cluster with Elastic Agent
- Quickstart: Monitor hosts with OpenTelemetry
- Quickstart: Unified Kubernetes Observability with Elastic Distributions of OpenTelemetry (EDOT)
- Quickstart: Collect data with AWS Firehose
- Get started with dashboards
- Applications and services
- Application performance monitoring (APM)
- Get started with traces and APM
- Learn about data types
- Collect application data
- View and analyze data
- Act on data
- Use APM securely
- Reduce storage
- Managed intake service event API
- Troubleshooting
- Synthetic monitoring
- Get started
- Scripting browser monitors
- Configure lightweight monitors
- Manage monitors
- Work with params and secrets
- Analyze monitor data
- Monitor resources on private networks
- Use the CLI
- Configure a Synthetics project
- Multifactor Authentication for browser monitors
- Configure Synthetics settings
- Grant users access to secured resources
- Manage data retention
- Scale and architect a deployment
- Synthetics Encryption and Security
- Troubleshooting
- Application performance monitoring (APM)
- Infrastructure and hosts
- Logs
- Inventory
- Incident management
- Data set quality
- Observability AI Assistant
- Machine learning
- Reference
- Get started
- Elastic Security
- Elastic Security overview
- Security billing dimensions
- Create a Security project
- Elastic Security requirements
- Elastic Security UI
- AI for Security
- Ingest data
- Configure endpoint protection with Elastic Defend
- Manage Elastic Defend
- Endpoints
- Policies
- Trusted applications
- Event filters
- Host isolation exceptions
- Blocklist
- Optimize Elastic Defend
- Event capture and Elastic Defend
- Endpoint protection rules
- Identify antivirus software on your hosts
- Allowlist Elastic Endpoint in third-party antivirus apps
- Elastic Endpoint self-protection features
- Elastic Endpoint command reference
- Endpoint response actions
- Cloud Security
- Explore your data
- Dashboards
- Detection engine overview
- Rules
- Alerts
- Advanced Entity Analytics
- Investigation tools
- Asset management
- Manage settings
- Troubleshooting
- Manage your project
- Changelog
Elasticsearch billing dimensions
editElasticsearch billing dimensions
editElasticsearch is priced based on consumption of the underlying infrastructure that supports your use case, with the performance characteristics you need. Measurements are in Virtual Compute Units (VCUs). Each VCU represents a fraction of RAM, CPU, and local disk for caching.
The number of VCUs you need is determined by:
- Volume and ingestion rate of your data
- Data retention requirements
- Search query volume
- Search Power setting
- Machine learning usage
For detailed Elasticsearch Serverless project rates, see the Elasticsearch Serverless pricing page.
VCU types: Search, Indexing, and ML
editElasticsearch uses three VCU types:
- Indexing: The VCUs used to index incoming documents.
- Search: The VCUs used to return search results, with the latency and queries per second (QPS) you require.
- Machine learning: The VCUs used to perform inference, NLP tasks, and other ML activities.
Data storage and billing
editElasticsearch Serverless projects store data in the Search AI Lake. You are charged per GB of stored data at rest. Note that if you perform operations at ingest such as vectorization or enrichment, the size of your stored data will differ from the size of the original source data.
Managing Elasticsearch costs
editYou can control costs using the following strategies:
- Search Power setting: Search Power controls the speed of searches against your data. With Search Power, you can improve search performance by adding more resources for querying, or you can reduce provisioned resources to cut costs.
- Time series data retention: By limiting the number of days of time series data that are available for caching, you can reduce the number of search VCUs required.
-
Machine learning trained model autoscaling: Configure your trained model deployment to allow it to scale down to zero allocations when there are no active inference requests:
- When starting or updating a trained model deployment, Enable adaptive resources and set the VCU usage level to Low.
-
When using the inference API for Elasticsearch or ELSER, enable
adaptive_allocations.