AWS Knowledge
Understanding Amazon OpenSearch Pricing 2024

Piyush Kalra
Oct 5, 2024

(Image Source: AWS)
How do you think the Amazon OpenSearch Pricing looks in the year 2024? Pricing of cloud services is one of the most strenuous tasks, let alone understanding the pricing model, which is 100% essential for any technical minded individual, business owner, developer or any other AWS and e-commerce users. This document makes you aware of the aspects affecting your OpenSearch application and the budgeting allowing you to use this great service to the maximum.
What is Amazon OpenSearch?
Amazon OpenSearch is one of the most widely used and managed distributed, collaborative service succeeding the underlying structure of Elasticsearch with the purpose of simplifying the delivery, operations and growth of OpenSearch domains on AWS. Considered then as a 100% open-source search and analytics that retains the licensing of the Apache 2.0, OpenSearch is especially relevant in the context of complex real-time system application performance monitoring, log assessment, and website searching tasks. This platform offers a flexible architecture to integrate OpenSearch Dashboards which enhances the users experience in using the system by allowing faster data querying and interactive data presentation to users. The OpenSearch platform is based on the Apache Lucene search engine and provides different search and analytics capabilities such as KNN search, SQL search, Anomaly Detection, Machine Learning, and Full Text Search among others. It has introduced state of the art features, enhanced compatibility and versatile billing structures, making it a must have business resource for any size of enterprise.
Pricing Structure Overview
Amazon OpenSearch pricing forms some sorts of complexity, and in order to be able to control the expenditures, it is necessary to comprehend the aspects of such pricing. There are three main aspects of pricing:
Instance Hours
The hours when your OpenSearch instances are up and functional, also known as instance hours are taken into consideration. There is an hourly rate for instance types, for example, $0.50 per hour, and these figures like other expenses may snowball even to roughly 40% of your total expenditure per month if care is not taken. Appreciating how these hours will affect your pocket is very important.
Storage Costs
The storage costs are variable due to the category of storage used as well as the tier of storage chosen. These include Amazon Elastic Block Store (EBS) and UltraWarm or Cold storage when data is accessed less frequently. For instance, EBS may charge $0.10 per GB-month which is likely to constitute about 30% of the total cost. The choice of the storage configuration can make a real difference in the one’s overall cost.
Data Transfer Fees
Basically, data transfer costs are incurred whenever there is the moving of the data in and out of your OpenSearch services. These fees can differ depending on the amount of data movement and the regions involved. Normally, the cost is $0.09 per GB, and this may account for about 30% of your budget. Scrutinizing as well as optimizing data transfer so as to shield one from unwarranted charges is very important.
Types of Instances and Their Costs
Selecting the correct type of instance is one of the most important dimensions in managing costs related to OpenSearch. Below is a summary of the options that are currently available:
General Purpose Instances
General-Purpose Instances are most suitable for deployments where the workloads are not fixed. They have a blend of CPU and RAM resources. They are widely employed for a range of applications, and they cover most use cases effectively. These instances are inexpensive since they account for less than 30% of the total costs and are beneficial to a large percentage of organizations.
Compute Optimized Instances
Compute Optimized Instances cater for very compute demanding operations as their processing capacity is above average and optimized. These are instance types that come very useful for such return on investment workloads and do facilitate meeting operational requirements efficiently. These come at a higher price, relatively accounting for about 25% of the costs although the benefits may outweigh the cost if the right conditions prevail.
Memory Optimized Instances
Memory Optimized Instances on the other hand are focused on more consuming applications and thereby providing more memory. Such instances are good for large datasets as well as complex queries. However, these instances are usually very costly accounting for up to 20% of the total assumed costs or more although performance benefits may compensate this for some of the jobs.
Storage Optimized Instances
The next step is Storage Optimized Instances, which are used for scenarios requiring very high storage throughput. These instances are designed for data heavy storage and retrieval use cases. The cost of these instances resonates with the specialized pattern of usage of about 15% of the overall costs.
OR1 Instances
These instances are specifically built with ecommerce workloads in mind. Normalization here is applied to both computation and memory resources, which makes it a unique category of instances particularly for OpenSearch users. OR1’s competitive feature among other attributes is the pricing that has been set at around 10% of the overall costs owing to the NICs.
Reserved Instances vs. On-Demand Pricing vs. OpenSearch Serverless
Understanding the difference between reserved and on-demand pricing is crucial for cost management:
Reserved Instances
With the Amazon OpenSearch Service Reserved Instances, you can reserve capacity for between 1 and 3 years allowing you to heavily discount On-Demand pricing. These Reserved Instances are cheaper but function in the same Kubernetes Infrastructure as a Service docker vs On-Demand Instance packaging.

There are three models in respect to payment for Reserved instances available for selection:
No Upfront Reserved Instances (NURI): This option saves you many bucks from the very course which required none without payment. You enter into a contract for either a term of 1 year or 3 years with respective discounts of 31% or 48%. As for the T3.medium instance, The discounts are 18% for 1 year and 28% for 3 years.
Partial Upfront Reserved Instances (PURI): As some of the costs are paid upfront, PURI’s savings are greater than that of NURI. 1 year PURI discounts are at 33% while 3 year PURI discounts are at 50%. As for T3.medium instances, the savings for 1 year is 20% and for 3 years is 30%.
All Upfront Reserved Instances (AURI): These are the most economical of all because total payment is made at the beginning, and no other charges are incurred on an hourly basis. Discounts on these are 35% for 1 year terms and 52% for the 3 year term. For T3.medium, these are 22% and 32% for 1 year and 3 year terms respectively.
On-Demand Pricing
On-demand pricing offers the greatest flexibility and does not require the user to enter into a long-term contract for predictable or short-term workloads. However, this model usually means higher hourly charges. Cost optimization would then appertain to the careful blending of this model with others.
With reference to On-Demand, in the case of the service, an instance is charged for usage hourly and is of multiple genres which includes General Purpose, Compute Optimized, Memory Optimized, Storage Optimized, and OR1 Instances.

This is how much each type of instances would cost:
General Purpose Instances are those whose CPU and memory configuration is not skewed towards any specific workload and therefore it is applicable to many workloads, t3.small.search $0.036/hour, m6g.large.search $0.128/hour are examples.
Compute Optimized Instances help speed up the most demanding internally oriented operations, for example, c6g.large.search $0.113/hour.
Memory Optimized Instances, USPs cater for applications with memory intensive workloads such as r6g.large.search $0.167/hour.
Storage Optimized Instances, such as i3.large.search @ $0.25/hour have been optimized to give high performance storage IO.
OR1 Instances have been optimized for workloads running on OpenSearch, and suitable options for this instance type would be or1.medium.search $0.105/hour.
Amazon OpenSearch Serverless Pricing
Amazon OpenSearch Serverless pricing is based on resource usage and incurs separately charged for Compute and Storage costs.

Compute Capacity:
It is mentioned in terms of OpenSearch Compute Units in short as OCU where 1 OCU comes with 6 GB of RAM and 1 associated vCPU. The first collection requires only 2 OCUs but can go up depending on the collection type and data size.
Indexing: $0.24 per OCU per hour.
Search and Query: $0.24 per OCU per hour.
Managed Storage: $0.024 per GB per month.
This model maximizes efficiency because expenses are commensurate with usage helping businesses that have no predictable loads or workloads since they are burstable in nature. Rest all the prices are approximate, for exact pricing check the region wise Amazon OpenSearch Service cost breakdown.
Free Tier and Entry-Level Options
AWS has a Free Tier for OpenSearch where users can sign up without any payment, which is intended which one can use to understand and use the service. This Free Tier allows usage of up to 750 hours per month for t2.small.search or t3.small.search instance types which can be used for light workloads and to run tests initially. Economically, one will also be getting 10 GB of ElasticBlock Store (EBS) which they will use to save their data as they try to learn this platform.
This particularly comes in handy for developers and businesses who are at the stage of determining whether or not OpenSearch will meet their needs since it allows a hands-on experience. But it should be noted that it is going to be more expensive with the standard service rates, when the limits that have been set have gone beyond and this can be costly if careful monitoring is forgotten to be practiced.
Cost Optimization Strategies
Cost Optimization within OpenSearch is very important to ensure that resources are not wasted. Try these strategies out:
Choosing the Right Instance Type
There is a huge difference in cost when choosing the wrong instance type for your workload. It is very important to understand what your application needs and where the performance and prices can meet to ensure that you do not waste resources from your instances.
Utilizing Caching Mechanisms
No matter how much resources are spent on trying to build a system, there is always a need to access data over and over again. Employing a caching mechanism can improve the performance and lessen the costs because the number of requests made to your OpenSearch instances are lowered.
Efficient Data Ingestion Practices
More focused means of handling data ingestion can help cut overwhelming expenditures in migrating and acquiring additional space for storage. Sending multiple documents in a single request rather than a single one and using data transformation services such as Amazon Kinesis Data Firehose will ease the process of data ingestion and its costs.
Pump AI for Cost Optimization

The integration of Pump AI with your Amazon OpenSearch Service environment could be an optimization tool with considerable cost-cutting solutions. For example, a company would reduce the expense on OpenSearch to 48%, from $100 to $52, leveraging Pump AI. Because Pump AI relies on machine learning algorithms to predict resource demands based on usage patterns, this allows businesses to strategically scale their infrastructure and avoid waste through optimization and identification of periods of peak use as well as underutilized resources.
Tracking Costs with AWS Tools
AWS has robust solutions for tracking and running your cost of OpenSearch effectively. The tools include:
AWS Cost Explorer

AWS Cost Explorer gives insight into your spending regarding OpenSearch. Cost Explorer enables you to track your expenditures and note where you could make changes to reduce the costs. This can be achieved by studying and noting trends in your cost and usage over time.
Cost Allocation Tags

Cost allocation tags provide domain-specific details on cost. The business will be able to budget accurately costs in terms of projects or departments and proper financial transparency is afforded. To understand better, refer to the article How to set up.
Conclusion
Understanding the various components of pricing in Amazon OpenSearch cannot be scary. There are methods through which cost optimization strategies can prove to be very effective in maximizing investments in this product. Therefore, there is a need for constant monitoring, strategic planning, and leveraging AWS tools to have an OpenSearch environment that is cost-effective as well as conducive to your organization's goals.
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