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6 Tips for Choosing Between AWS Elasticsearch and a Custom Elasticsearch Solution

Updated September 2020

Implementing a big data platform is a significant investment. We know the process can be intimidating, and, at times, confusing.

We broke down the thought process for choosing between AWS Elasticsearch and a custom Elasticsearch solution here to help you think through what will be right for you and your team.


Dattell: Works with clients to build a complete big data platform including messaging, transformation, database and presentation layers.

AWS Elasticsearch: Provides a hosted database solution that is designed to meet general requirements.

These first differences are key because they influence much of how the final solution will function and affects the cost and timing.

If you are in a pinch and need only a generic database by the end of the week, AWS Elasticsearch might be the right solution. At Dattell, we build comprehensive solutions, efficiently and effectively. We probably can’t get it running for you by the end of the week, but we could create a pilot in that time. In general, it takes us two to four weeks to build a platform depending on the requirements.

Remember, this project is an investment and will be used for a long time. When choosing a solution, ensure that it is one that sets you up for success.


    Dattell: Secure transportation of data. secure backups of data in original and database form. secure databases. secure presentation.
    AWS: Secure backups in database form, secure databases, secure presentation.
    Dattell: Client’s needs define the product.
    AWS: Client’s needs are compared against a list of supported features.
    Dattell: Free from vendor lock in. Our open source products are supported by many other companies and work in any environment/cloud.
    AWS: Only supported by AWS in AWS cloud infrastructure.
    Dattell: Our clients have the ability to review or have a third party review any part of our implementation.
    AWS: You must have faith in the vendor’s claims.
  5. SPEED
    Dattell: It will take us roughly two to four weeks to build the platform.
    AWS: They can offer a solution more quickly because it is pre-built and only includes the database.
  6. COST
    Dattell: Our flexibility allows us to create a big data platform that meets exactly your requirements, saving on average 70% on server fees over hosted AWS Elasticsearch.
    AWS: It is a fixed cost based off general requirements lacking features such as high variable server size and hot/warm/cold architecture.

Elastic Stack Consulting Services

If you are interested in 24/7 support, consulting, and/or fully managed Elasticsearch services on your environment, you can find more information on our Elasticsearch consulting page.

Schedule a call with an Elastic Stack engineer.

Published by

Dattell - Kafka & Elasticsearch Support

Benefit from the experience of our Kafka, Pulsar, Elasticsearch, and OpenSearch expert services to help your team deploy and maintain high-performance platforms that scale. We support Kafka, Elasticsearch, and OpenSearch both on-prem and in the cloud, whether on stand alone clusters or running within Kubernetes. We’ve saved our clients $100M+ over the past six years. Without our guidance companies tend to overspend on hardware or purchase unnecessary licenses. We typically save clients multiples more money than our fees cost in addition to building, optimizing, and supporting fault-tolerant, highly available architectures.

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