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Using [AutoOps for self-managed clusters](/deploy-manage/monitor/autoops/cc-autoops-as-cloud-connected.md) through Cloud Connect is included with your [self-managed Enterprise license](https://www.elastic.co/subscriptions).
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Cloud Connect enables you to use {{ecloud}} services in your self-managed cluster without having to install and maintain their infrastructure yourself. In this way, you can get faster access to new features while significantly reducing your operational overhead.
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Cloud Connect is included with your [self-managed Enterprise license](https://www.elastic.co/subscriptions) and works with self-managed free trials for the duration of the trial period.
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AutoOps is the first service available for use with Cloud Connect. More services are coming soon.
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### AutoOps
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Learn how to set up and use [](/deploy-manage/monitor/autoops/cc-autoops-as-cloud-connected.md).
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## FAQs
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## FAQ
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Find answers to your questions about Cloud Connect.
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:::{dropdown} Does using Cloud Connect require additional payment?
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$$$cc-payment$$$
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No. Cloud Connect is included with your [self-managed Enterprise license](https://www.elastic.co/subscriptions)and works with self-managed free trials for the duration of the trial period.
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Each cloud connected service has its own licensing and payment requirements.
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However, you may incur additional costs for specific functions within a cloud connected service. For example, when sending metrics data from your cluster in a CSP region to AutoOps in {{ecloud}}, shipping costs will be determined by your agreement with that CSP.
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# Elastic for Observability billing dimensions [observability-billing]
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{{obs-serverless}} projects provide you with all the capabilities of Elastic Observability to monitor critical applications. Projects are provided using a Software as a Service (SaaS) model, and pricing is entirely consumption-based.
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{{obs-serverless}} projects provide you with all the capabilities of Elastic Observability to monitor critical applications. Projects are provided using a Software as a Service (SaaS) model, and pricing is entirely consumption-based. {{obs-serverless}} projects are available in the following tiers of carefully selected features to enable common observability operations:
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***Observability Logs Essentials**: Includes everything you need to store and analyze logs at scale.
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***Observability Complete**: Adds full-stack observability capabilities to monitor cloud-native and hybrid environments.
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Your monthly bill is based on the capabilities you use. When you use {{obs-serverless}}, your bill is calculated based on data volume, which has these components:
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Data volumes for ingest and retention are based on the fully enriched normalized data size at the end of the ingest pipeline, before {{es}} compression is performed, and will be higher than the volumes traditionally reported by {{es}} index size. In addition, these volumes might be larger than those reported by cloud provider proxy logs for data going into {{es}}. This allows you to have flexibility in choosing your preferred ingest architecture for enrichment, whether it’s through Elastic Agent, Logstash, OpenTelemetry, or collectors — with no impact on the cost.
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Refer to [Serverless billing dimensions](serverless-project-billing-dimensions.md) and the [{{ecloud}} pricing table](https://cloud.elastic.co/cloud-pricing-table?productType=serverless&project=observability) for more details about {{obs-serverless}} billing dimensions and rates.
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## Synthetics [synthetics-billing]
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[Synthetic monitoring](/solutions/observability/synthetics/index.md) is an optional add-on to Observability Serverless projects that allows you to periodically check the status of your services and applications. In addition to the core ingest and retention dimensions, there is a charge to execute synthetic monitors on our testing infrastructure. Browser (journey) based tests are charged per-test-run, and ping (lightweight) tests have an all-you-can-use model per location used.
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[Synthetic monitoring](/solutions/observability/synthetics/index.md) is an optional add-on to Observability Serverless projects that allows you to periodically check the status of your services and applications as a part of your "Complete" tier subscription. In addition to the core ingest and retention dimensions, there is a charge to execute synthetic monitors on our testing infrastructure. Browser (journey) based tests are charged per-test-run, and ping (lightweight) tests have an all-you-can-use model per location used.
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Refer to [Serverless billing dimensions](serverless-project-billing-dimensions.md) and the [{{ecloud}} pricing table](https://cloud.elastic.co/cloud-pricing-table?productType=serverless&project=observability) for more details about {{obs-serverless}} billing dimensions and rates.
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## Elastic Managed LLM
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The default [Elastic Managed LLM](kibana://reference/connectors-kibana/elastic-managed-llm.md) enables you to leverage AI-powered search as a service without deploying a model in your serverless project. It's configured by default to use with the Security AI Assistant, Attack Discovery, and other applicable AI features as a part of your "Complete" tier subscription. Using the default LLM will use tokens and incur related token-based add-on billing for your serverless project.
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* When starting or updating a trained model deployment, [Enable adaptive resources](../../autoscaling/trained-model-autoscaling.md#enabling-autoscaling-in-kibana-adaptive-resources) and set the VCU usage level to **Low**.
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* When using the inference API for {{es}} or ELSER, [enable `adaptive_allocations`](../../autoscaling/trained-model-autoscaling.md#enabling-autoscaling-through-apis-adaptive-allocations).
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***Indexing Strategies:** Consider your indexing strategies and how they might impact overall VCU usage and costs:
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* To ensure optimal performance and cost-effectiveness for your project, it’s important to consider how you structure your data.
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* Consolidate small indices for better efficiency. We recommend avoiding a design where your project contains hundreds of very small indices, specifically those under 1GB each.
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* Why is this important?
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* Every index in Elasticsearch has a certain amount of resource overhead. This is because Elasticsearch needs to maintain metadata for each index to keep it running smoothly. When you have a very large number of small indices, the combined overhead from all of them can consume more CPU resources than if the same data were stored in fewer, larger indices. This can lead to higher resource consumption and hence higher costs and potentially impact the overall performance of your project.
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* Recommended Approach
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* If your use case naturally generates many small, separate streams of data, we advise implementing a process to consolidate them into fewer, larger indices. This practice leads to more efficient resource utilization. By grouping your data into larger indices, you can ensure a more performant and cost-efficient experience with Elasticsearch Serverless.
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Versions of the {{stack}}, containing {{es}}, {{kib}}, and other products, are available as downloadable Docker images.
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The first table contains the stack versions that shipped with the 4.0 version of {{ece}}. You can also check the [most recent stack packs and Docker images](#ece-recent-download-list), which might have released after the 4.0 version of ECE, as well as the [full list of available stack packs and Docker images](#ece-full-download-list).
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The first table contains the stack versions shipped with the current {{version.ece}} version of {{ece}}. You can also check the [most recent stack packs and Docker images](#ece-recent-download-list), which might have released after the current version of ECE, as well as the [full list of available stack packs and Docker images](#ece-full-download-list).
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| Docker images included with {{ece}} {{version.ece}} |
$$$ece-all-stacks$$$Additional {{stack}} versions are available as Docker images that you can use with ECE. For offline installations, you need to download both the {{stack}} pack and the Docker images for the same version.
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| Required downloads | Minimum required ECE version |
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