Cost-Saving Strategies for AWS OpenSearch(FinOps): Optimize Performance without Breaking the Bank

11 min readApr 14, 2023

AWS OpenSearch, formerly known as Amazon Elasticsearch, is a powerful search and analytics engine that organizations leverage for a wide range of applications, such as log and event data analysis, full-text search, and more. However, when not optimized, costs can spiral out of control. In this blog post, we’ll discuss several cost-saving strategies to help you get the most out of AWS OpenSearch without breaking the bank.

Choose the Right Instance Type and Size

Selecting the right instance type and size is critical to optimizing costs. AWS OpenSearch offers various instance types with different CPU, memory, and storage configurations. Before you select an instance, consider the following factors:

The nature of your workload
The volume of data you need to store and index
Query performance requirements

Carefully evaluate your needs and select an instance type that offers the best balance between performance and cost.

Utilize Reserved Instances

Reserved Instances (RIs) are a cost-saving option for long-term workloads. By committing to a one or three-year term, you can save up to 66% compared to on-demand pricing. Analyze your usage patterns and consider reserving instances if you have a consistent, long-term workload.

Use Index Lifecycle Management

Index Lifecycle Management (ILM) policies help you manage your data over time by automatically transitioning indices through different stages (hot, warm, cold, and delete). Implementing ILM policies can reduce storage costs by moving less-frequently accessed data to less expensive storage tiers or deleting old, unused data.

Optimize Data Storage

Compressing data and using the right storage type can significantly reduce storage costs. AWS OpenSearch offers several ways to optimize data storage:

Source Compression: Compress your source data before sending it to OpenSearch to reduce the storage footprint and data transfer costs.
Index Compression: Enable index-level compression to save storage space and reduce I/O costs.
Use Amazon S3 as a data store: For infrequently accessed or archived data, consider using Amazon S3 as a cost-effective storage option.

Implement Cross-Account Access for AWS OpenSearch

Cross-account access allows you to share your OpenSearch cluster across multiple AWS accounts, enabling centralized management and cost optimization. By sharing a single OpenSearch cluster with multiple accounts, you can reduce the overall number of instances and resources required, resulting in cost savings.

To implement cross-account access for AWS OpenSearch, follow these steps:

Create an Amazon Resource Name (ARN) for each account that requires access to the OpenSearch cluster.
Define an OpenSearch access policy that grants the necessary permissions to the specified ARNs.
Apply the access policy to your OpenSearch cluster, allowing the listed accounts to access the cluster.

By implementing cross-account access, you can consolidate your OpenSearch resources and minimize the costs associated with running multiple separate clusters. Additionally, centralizing your OpenSearch management can simplify administration and improve visibility into your costs and usage patterns, making it easier to identify and address cost optimization opportunities.

Configure Snapshots and Retention Policies

Snapshots provide a point-in-time backup of your OpenSearch cluster. By default, AWS OpenSearch takes daily snapshots and retains them for 14 days. To save on storage costs, consider adjusting the snapshot frequency and retention period based on your business requirements and data recovery needs.

Monitor and Optimize Performance

Regularly monitoring your OpenSearch cluster’s performance can help you identify and resolve issues that may lead to unnecessary costs. Utilize Amazon CloudWatch metrics, OpenSearch Dashboards, and performance analyzers to keep an eye on performance and optimize query efficiency.

Scale Dynamically with Auto Scaling

Auto Scaling automatically adjusts the number of instances in your OpenSearch cluster based on real-time demand. By enabling Auto Scaling, you can reduce costs by only using the resources you need, when you need them.

Optimize Index Sharding and Replication

Optimizing the number of shards and replicas for your indices can help balance the trade-off between performance, resilience, and cost. Consider these factors when configuring sharding and replication:

Shard count: Distributing data across multiple shards can improve query performance, but too many shards can increase cluster overhead and costs. Aim to find the right balance based on your workload requirements.
Replica count: Increasing the number of replicas can improve search performance and fault tolerance, but it also increases storage and resource costs. Analyze your needs and determine the appropriate replica count for each index.

Leverage Caching

Caching can significantly improve query performance and reduce resource consumption, leading to cost savings. AWS OpenSearch provides several caching mechanisms:

Field Data Cache: Cache the results of aggregations to speed up subsequent requests for the same data.
Query Cache: Cache the results of frequently executed queries to minimize the resources needed to serve them.
Request Cache: Cache the responses of search requests to reduce load on the cluster.

Implement Security Best Practices

Securing your OpenSearch cluster can prevent unauthorized access and data breaches, which could lead to costly remediation efforts. Implement security best practices, such as:

Enabling encryption at rest and in transit
Using fine-grained access control
Configuring strong authentication mechanisms
Regularly auditing and monitoring your cluster for security threats

Optimize Data Ingestion

Efficiently ingesting data into OpenSearch can reduce costs associated with data transfer and processing. Consider the following best practices:

Bulk indexing: Group multiple documents into a single request to reduce the overhead of individual indexing operations.
Use Logstash, Amazon Kinesis Data Firehose, or AWS Lambda for ingestion: These services can help you efficiently ingest data into OpenSearch while also providing features such as data transformation, filtering, and buffering.

Optimize Query Performance

Efficient queries can significantly reduce the resources required to process them, lowering your costs. Implement these best practices to optimize your OpenSearch queries:

Use filters instead of queries when possible: Filters don’t affect the relevancy score and are faster to execute, as they can be cached.
Paginate results with caution: Avoid deep pagination, as it can consume considerable resources. Instead, use scrolling or search_after for deep paginations.
Utilize Search Templates: Search templates help you reuse query structures, reducing the complexity of crafting and parsing queries.

Consolidate Multiple Clusters

Running multiple OpenSearch clusters can lead to increased costs. Consider consolidating multiple clusters into one, using index prefixes and separate namespaces to differentiate data from different applications. This can help you save costs by reducing the number of instances and the resources required to manage multiple clusters.

Implement Cost Allocation Tags

Use cost allocation tags to track and analyze the costs associated with your OpenSearch resources. By categorizing resources based on project, environment, or other relevant factors, you can identify areas where cost optimization efforts are needed and allocate resources more efficiently.

Leverage Spot Instances

AWS Spot Instances allow you to bid on unused Amazon EC2 capacity at a discount. You can use Spot Instances for stateless, fault-tolerant workloads, such as OpenSearch data ingestion and processing. Be prepared to handle interruptions, as Spot Instances can be terminated with short notice when the capacity is no longer available.

Use OpenSearch Curator

OpenSearch Curator is a powerful tool that enables you to perform various management tasks on your OpenSearch indices, such as deleting, closing, or optimizing indices based on specified criteria. By automating these tasks, you can maintain a lean and efficient OpenSearch cluster, reducing storage and processing costs.

Monitor Costs with AWS Cost Explorer

AWS Cost Explorer is a tool that allows you to visualize, understand, and manage your AWS costs and usage. By monitoring your OpenSearch costs using Cost Explorer, you can identify cost trends, detect anomalies, and take action to optimize your spending.

Fine-tune JVM Heap Size

JVM (Java Virtual Machine) heap size directly impacts OpenSearch’s performance and resource usage. By fine-tuning the JVM heap size, you can strike the right balance between memory consumption and garbage collection efficiency. Monitor your OpenSearch cluster’s memory usage and adjust the heap size based on your application’s requirements to optimize resource utilization and minimize costs.

Utilize OpenSearch Rollups

OpenSearch Rollups are a powerful feature for summarizing and compressing historical data. By aggregating and storing data in a more compact form, you can reduce storage costs while maintaining query performance for long-term analytics use cases. Implement rollup jobs to aggregate and store data at various intervals (e.g., hourly, daily, or monthly) to optimize storage costs based on your analytical needs.

Optimize Mapping and Index Settings

Optimizing your OpenSearch index mappings and settings can help reduce storage requirements and improve query performance. Consider the following best practices:

Disable _all and _source fields when not needed: Disabling these fields can save storage space if you don’t require them for your use case.
Use the appropriate field data types: Choosing the correct data types for your fields can optimize storage and query performance.
Use index templates: Index templates enable you to define mappings and settings that are automatically applied to new indices, ensuring consistency and efficiency across your OpenSearch deployment.

Implement Throttling and Rate Limiting

Throttling and rate limiting can help you control resource usage and costs by limiting the number of requests to your OpenSearch cluster. By implementing these techniques, you can ensure that resources are allocated fairly among users and prevent excessive resource consumption, ultimately reducing costs.

Monitor and Optimize Client Libraries

Client libraries play a crucial role in the efficiency of your OpenSearch deployment. Monitor and optimize your client libraries to reduce the overhead associated with connecting and querying your OpenSearch cluster. Regularly update your client libraries to the latest versions, and use connection pooling and bulk operations to minimize resource usage.

Optimize Data Ingestion with AWS Data Pipeline

AWS Data Pipeline is a data integration service that enables you to automate data movement and transformation between AWS services. By using Data Pipeline to ingest data into your OpenSearch cluster, you can reduce data transfer and processing costs while ensuring that data is processed efficiently and accurately.

Monitor and Optimize Network Costs

OpenSearch network usage can contribute to your overall costs, especially when transferring large amounts of data between AWS services or across regions. Monitor your network usage and optimize data transfer to minimize costs by:

Compressing data before transmission
Reducing cross-region data transfers
Using Amazon VPC endpoints for private connectivity to OpenSearch

Periodically Review Instance Types and Storage Options

AWS continuously introduces new instance types and storage options that may provide better performance and cost savings for your OpenSearch deployment. Regularly review and compare available options to identify opportunities to upgrade or switch to more cost-effective resources.

Optimize Data Retention Policies

Regularly review and adjust your data retention policies to ensure that you are only storing data that is necessary for your business requirements. By deleting or archiving data that is no longer needed, you can reduce storage costs and improve the overall efficiency of your OpenSearch deployment.

Leverage AWS Budgets

AWS Budgets enable you to set custom cost and usage budgets for your AWS resources, including OpenSearch. By creating budgets for your OpenSearch deployment, you can receive alerts when your spending approaches or exceeds your predefined thresholds. This proactive approach allows you to identify and address cost overruns early, helping you optimize your OpenSearch costs more effectively.

Monitor and Analyze Slow Logs

Slow logs provide insights into poorly performing queries and indexing operations that can consume significant resources and drive up costs. By regularly monitoring and analyzing slow logs, you can identify performance bottlenecks and take corrective action to optimize your OpenSearch cluster.

Evaluate Third-Party OpenSearch Plugins

There are several third-party plugins available for OpenSearch that can help you optimize costs and performance. Evaluate these plugins and choose those that align with your use case and requirements, as they can provide valuable tools and features to enhance your OpenSearch deployment.

Use AWS Savings Plans

AWS Savings Plans are another option to save on OpenSearch costs. By committing to a consistent amount of compute usage (measured in dollars per hour) for a one or three-year term, you can save up to 72% compared to on-demand pricing. Savings Plans provide flexibility, as they can be applied across various AWS services, including OpenSearch.

Exploit AWS Cost and Usage Reports

AWS Cost and Usage Reports provide comprehensive information about your AWS costs and resource usage. These reports can help you better understand your OpenSearch spending, allowing you to identify trends and potential areas for cost optimization. Regularly analyze these reports to make data-driven decisions about your OpenSearch deployment and cost management strategies.

Use AWS Compute Optimizer

AWS Compute Optimizer is a service that analyzes your resource usage and recommends optimal configurations for your OpenSearch instances. By leveraging machine learning, Compute Optimizer identifies opportunities for cost savings and improved performance. Utilize the recommendations provided by this service to optimize your OpenSearch instance types and sizes.

Leverage AWS Trusted Advisor

AWS Trusted Advisor is a service that provides real-time guidance to help you follow AWS best practices. Trusted Advisor checks your OpenSearch configuration and recommends improvements for cost optimization, performance, security, and fault tolerance. Periodically review the recommendations provided by Trusted Advisor to identify areas for cost savings and performance enhancements in your OpenSearch deployment.

Utilize AWS Organizations and Consolidated Billing

AWS Organizations allows you to consolidate multiple AWS accounts into a single organization, simplifying billing and management. By leveraging AWS Organizations and consolidated billing, you can aggregate your OpenSearch costs across accounts, making it easier to track and analyze spending patterns. This, in turn, can help you identify potential areas for cost optimization.

Implement Custom Alerting with Amazon SNS

Create custom alerts using Amazon Simple Notification Service (SNS) to monitor your OpenSearch cluster’s resource usage and costs. By setting up alerts for specific metrics, such as high CPU utilization, you can proactively identify and address issues that may lead to increased costs.

Use AWS Cost Categories

AWS Cost Categories allow you to create custom categories for your AWS costs, providing a more granular view of your spending. By organizing your OpenSearch costs into categories such as environment, team, or project, you can more easily identify areas where cost optimization efforts are needed and allocate resources accordingly.

Utilize AWS Cost Anomaly Detection

AWS Cost Anomaly Detection is a feature that leverages machine learning to automatically detect anomalies in your AWS spending. By enabling this feature for your OpenSearch costs, you can identify unexpected cost spikes or unusual patterns that may require further investigation and optimization.

Implement Resource Tagging Best Practices

Following resource tagging best practices can help you gain better visibility into your OpenSearch costs and usage. By tagging resources consistently and meaningfully, you can generate more accurate cost allocation reports, making it easier to identify cost optimization opportunities.

Use AWS Pricing Calculator

The AWS Pricing Calculator is a tool that helps you estimate the costs of AWS services, including OpenSearch. Use this calculator to model different deployment scenarios, compare costs between instance types, and evaluate the potential impact of cost-saving strategies such as Reserved Instances and Savings Plans.

Leverage AWS Well-Architected Framework

The AWS Well-Architected Framework is a set of best practices and principles designed to help you build efficient, secure, and cost-effective cloud applications. By following the recommendations outlined in the Cost Optimization pillar, you can ensure that your OpenSearch deployment adheres to AWS best practices, maximizing cost savings and performance.

Use AWS Instance Scheduler

AWS Instance Scheduler is a solution that automates the starting and stopping of Amazon EC2 and Amazon RDS instances based on a schedule. While OpenSearch does not support stopping and starting, you can use the Instance Scheduler for ancillary resources like EC2 instances used for data ingestion or other related processes. By running these instances only when required, you can reduce your overall AWS costs.

Continuously Review and Update Cost Optimization Strategies

Finally, keep in mind that cost optimization is an ongoing process. Regularly review your OpenSearch deployment and update your cost optimization strategies as your application’s requirements and AWS offerings evolve. Staying up-to-date with the latest features, best practices, and pricing options will ensure that your OpenSearch deployment remains efficient and cost-effective.