AWS Marketplace: Elastic Cloud (Elasticsearch Service) Reviews

Elastic Cloud (Elasticsearch Service)

Elastic

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4-star reviews ( Show all reviews )

Tom Everson

Search through massive message archives in milliseconds and have supported large compliance data

March 24, 2026
Review from a verified AWS customer

What is our primary use case?

I can describe a few use cases for Elastic Search because in my previous company, we had a message database and needed to implement a search system. We first used Postgres full text search, but it did not work well, so we had to migrate everything into Elastic Search. Elastic Search could better index the data and we could search every document in instant time.

The key differences between Elastic Search and Postgres search, including both pros and cons, are primarily related to indexing speed. In Postgres, the full text search speed is quite noticeable if you have a message document. In Elastic Search, I am not quite certain about when comparing to normal data, but for our use case of searching through message documents, the speed difference is noticeable in Postgres because our documents are very large. Since Elastic Search is primarily built for search, I think it can better search through the document. Our documents were sometimes really large, ranging from 100 megabytes to 200 megabytes per document, so I think Elastic Search handles this much better than Postgres.

What is most valuable?

What I appreciate about Elastic Search is that the best features include the ability to search through very big documents and index and search through them really fast. This is the one thing I value most about Elastic Search.

Regarding stability, I have not had any crashes, downtimes, or performance issues with it. We did have one incident, but it was not from Elastic Search. I think it was an AWS service outage. The downtime was an AWS error, not from Elastic Search.

Concerning scalability, I find it scalable because it is quite scalable right now. We currently have a terabyte of compliance data, and the client can search through that very effectively. We have not experienced any scalability errors so far. I think our compliance data amounts to approximately five or six terabytes of data, which is very large. We can search through that document quite easily, sometimes in 7 milliseconds, sometimes one or two milliseconds. It was quite fast.

What needs improvement?

Apart from the good things, what I would like to see improved or enhanced in Elastic Search is the storage cost. I think the main problem with Elastic Search is that sometimes the storage was quite expensive. We also have a file system in addition to compliance. We have an FDS on our server, and we sometimes want to attach something on top of the FDS and search through every file without having to create a search index dedicatedly.

The missing features or functionalities in Elastic Search that I would like to see included in the future or some functionality that requires enhancement would be the ability to attach to our file system, such as network file system or NFS, or maybe our on-premise NAS server, and then search through everything, whether it is a document, text, or some information from those documents. That may be our primary use case right now, but we do not have that capability. Additionally, I would like to see a better search system so we can locally embed and find through everything.

For how long have I used the solution?

I have been working with Elastic Search for approximately one or two years.

What do I think about the stability of the solution?

Regarding stability, I have not had any crashes, downtimes, or performance issues with it. We did have one incident, but it was not from Elastic Search. I think it was an AWS service outage, not from Elastic Search. The error was an AWS error.

What do I think about the scalability of the solution?

Concerning scalability, I find it scalable because it is quite scalable right now. We currently have a terabyte of compliance data, and the client can search through that very effectively. We do not have any scalability errors so far. I think our compliance data amounts to approximately five or six terabytes of data, which is very large. We can search through that document quite easily, sometimes in 7 milliseconds, sometimes one or two milliseconds. It was quite fast.

How are customer service and support?

I do not know anything about the tech support because I have not escalated any questions to the technical support or customer service teams. We have not talked to anyone.

Which solution did I use previously and why did I switch?

I previously used a different solution for search. The solution I used for the search previously was Postgres full text search.

How was the initial setup?

The initial setup process of Elastic Search was straightforward. I did not face many challenges or complexities except for the fact that we had to extract every document and build a search index. Aside from that, we did not experience much complexity during that time.

What's my experience with pricing, setup cost, and licensing?

When it comes to pricing, I think we had to pay AWS approximately 1,000 to 1,200 per month for the overall stack. I am not quite certain about how much Elastic Search costs specifically because I was not in charge of pricing. The overall system cost was approximately 1,200 to 1,500 per month.

I do not find it cost-effective. I am not quite certain. Maybe the client might complain, but I am not certain. We just built out the system.

Which other solutions did I evaluate?

Before choosing Elastic Search, I evaluated other options. At first, we tried to go with Redis search because we really needed fast retrieval, but Redis search was closed source at that time, so we could not go with Redis search. We had to try Elastic Search and it performed quite surprisingly well.

What other advice do I have?

Given my experience with Elastic Search, a piece of advice or recommendation I may share with other organizations considering it is that if you are looking for a simple search, I am not certain whether I would recommend Elastic Search. However, if you are handling message data with a massive amount of data and you need sub-millisecond search time, I think in that scenario Elastic Search outperforms everything. I would give this product a rating of eight out of ten. Especially if you are using SQL to search through the data, Elastic Search really outperforms SQL when you have to search through massive data.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)

Tahir-Khan

Indexing millions of daily records has been streamlined and search performance meets our needs

February 27, 2026
Review provided by PeerSpot

What is our primary use case?

Elastic Search use cases for us involve maintaining a huge amount of data per day, around millions of transactions for each record. We are maintaining all this data with Elastic, and Elastic is doing a fantastic job by doing the indexing. The algorithm is very good, enabling us to process the data very fast.

We are conducting searches with Elastic Search because the data volume is too high. With a couple of indexing configurations, we are able to achieve our goal.

What is most valuable?

A good feature of Elastic Search is that they have something called policies, which we can make hot and cold, all related to data retention, and that is what I appreciate the most.

What needs improvement?

From the UI point of view, we are using most probably Kibana, and I think they can do much better than that. That is something they can fine-tune a little bit, and then it will definitely be a good product.

Maintenance in terms of Elastic is that they can improve the UI and UX, and if they fine-tune it a little bit, then it will be much better.

For how long have I used the solution?

I have used Elastic Search for the last two years in my career.

What do I think about the stability of the solution?

So far I haven't noticed any lagging, crashing, or downtime with Elastic Search.

What do I think about the scalability of the solution?

The scalability of Elastic Search is good, and I am satisfied with that as of now, and the performance is good.

How are customer service and support?

I don't think I have ever had to contact technical support.

How was the initial setup?

I find the initial deployment of Elastic Search easy; it is quite straightforward.

Approximately, I am able to deploy Elastic Search within two to three hours for the first time.

What about the implementation team?

To deploy, one or two people will be enough because you need Logstash to be configured to bring the data to Elastic Search for indexing.

Which other solutions did I evaluate?

We tried to implement big data pipelines and all, and we tried to use Spark as well for analytics and data cleaning, but I think Elastic is better in that field. I didn't find anything better than that.

Mustafa U.

Powerful and Scalable Search Solution

February 18, 2026
Review provided by G2

What do you like best about the product?

What I like most about Elasticsearch is its speed and flexibility. It handles large amounts of data efficiently and makes searching very fast. It is also versatile enough to be used for both search and analytics use cases.

What do you dislike about the product?

One thing I dislike about Elasticsearch is that it can become complex to manage as it grows. It requires careful planning and monitoring to avoid performance and stability issues. Licensing and pricing changes over time have also created some uncertainty for users.

What problems is the product solving and how is that benefiting you?

Elasticsearch helps us quickly search and analyze large amounts of data in one place. It makes it easier to find relevant information, monitor systems, and generate insights from logs or application data. This improves visibility and allows us to respond to issues faster and make better decisions.

Computer Hardware

Powerful Log Database with Helpful Integrations for Easy Parsing

February 12, 2026
Review provided by G2

What do you like best about the product?

You can use it as a database and classify all type of logs. The integrations they have helps you to parse them

What do you dislike about the product?

Sometimes correlations can be difficult between different technologies

What problems is the product solving and how is that benefiting you?

Handling logs

Vikas Kumar C.

Best No-SQL Databases with vector search and AI use cases

February 12, 2026
Review provided by G2

What do you like best about the product?

It’s one of the best NoSQL databases on the market. It makes it easier to collect logs from many different sources and to define integrations for them. It provides many features within one tool like vector search, machine learning, alerting and a lot

What do you dislike about the product?

I don’t like the breaking changes that come with version upgrades, because they have a big impact when multiple teams depend on the deployment.

What problems is the product solving and how is that benefiting you?

We collect telecom metrics from around 1,000 servers, which helps us search for and debug errors, create KPIs, and set up rules and alerting based on that data. As a result, it reduces manual effort and is easy to integrate with other systems. The best part is elasticsearch can be used for varied use cases. Its a single point of monitoring for our whole telecom stack.

Gambling & Casinos

Real-Time Bet Monitoring That Helps Us Improve Before It Happens

February 12, 2026
Review provided by G2

What do you like best about the product?

It helps us monitor bets in real time, and we can even see where we need to improve before it happens.

What do you dislike about the product?

It gives us a real-time view of our infrastructure logs. The downside is that shards sometimes get corrupted, and we need to restore them, but we don’t have clear visibility into that process.

What problems is the product solving and how is that benefiting you?

It provides operators with real-time logs and supports the compliance team in meeting regulatory requirements.

Anurag Pal

Search and aggregations have transformed how I manage and visualize complex real estate data

February 10, 2026
Review from a verified AWS customer

What is our primary use case?

I am using Elastic Search not only for search purposes but for rendering on maps as well.

I have not searched any vectors so far, so I cannot provide you with the exact output of that.

I was not using vectors in Elastic Search because I was using a vector database. As I mentioned, I use other databases for that. I have not explored it because when it comes to the data, Elastic Search will become expensive. In that case, what I suggest to my clients is to go with PostgreSQL, a vector database, or any other vector database. They are a startup, which is the problem.

We are using streams.

What is most valuable?

My favorite feature is always aggregations and aggregators. You do not have to do multiple queries and it is always optimized for me.

I always got the perfect results because I am using full text search with aliases and keyword search, everything I am performing it. It always performs out of the box.

It is easy because I have been doing it for years. The last version I remember is 3.5 or 3.1 that I used. Since then, I have been following Elastic Search and the changes they do. For configuration, I have never seen any problem.

What needs improvement?

Elastic Search consumes lots of memory. You have to provide the heap size a lot if you want the best out of it. The major problem is when a company wants to use Elastic Search but it is at a startup stage. At a startup stage, there is a lot of funds to consider. However, their use case is that they have to use a pretty significant amount of data. For that, it is very expensive. For example, if you take OLTP-based databases in the current scenario, such as ClickHouse or Iceberg, you can do it on 4GB RAM also. Elastic Search is for analytical records. You have to do the analytics on it. According to me, as far as I have seen, people will start moving from Elastic Search sooner or later. Why? Because it is expensive. Another thing is that there is an open source available for that, such as ClickHouse. Around 2014 and 2012, there was only one competitor at that time, which was Solr. But now, not only is Solr there, but you can take ClickHouse and you have Iceberg also. How are we going to compete with them? There is also a fork of Elastic Search that is OpenSearch. As far as I have seen in lots of articles I am reading, users are using it as the ELK stack for logs and analyzing logs. That is not the exact use case. It can do more than that if used correctly. But as it involves lots of cost, people are shifting from Elastic Search to other sources.

When I am talking about pricing, it is not only the server pricing. It is the amount of memory it is using. The pricing is basically the heap Java, which is taking memory. That is the major problem happening here. If we have to run an MVP, a client comes to me and says, "Anurag, we need to do a proof of concept. Can we do it if I can pay a 4GB or 16GB expense?" How can I suggest to them that a minimum of 16GB is needed for Elastic Search so that your proof of concept will be proved? In that case, what I have to suggest from the beginning is to go with Cassandra or at the initial stage, go with PostgreSQL. The problem is the memory it is taking. That is the only thing.

For how long have I used the solution?

I have been using Elastic Search since around 2012.

What do I think about the stability of the solution?

I have never seen any instabilities, even from the initial state.

What do I think about the scalability of the solution?

I have checked it for a petabyte of records. It is scalable.

How are customer service and support?

One person can do it, but when it comes to DevOps, we need a team always. Only if we have to manage Elastic Search, one person is fine.

How would you rate customer service and support?

Neutral

Which solution did I use previously and why did I switch?

I have used Solr and MongoDB as direct alternatives. According to the situation, it basically happens based on what the client wants. Sometimes they want Cassandra in place of Elastic Search. Our thing is only to suggest them. When it comes to the server costing, they are always asking, "Can we move to another server?" For example, I was working with a lower attorney's application and we implemented Elastic Search. For AWS only, we had to take two instances of 32GB for Elastic Search. After a few months only, the client asked, "Anurag, is it possible if we can go to another source if the latency is reduced or if some concurrency will reduce?" In that case, we had to move to Cassandra. Alternatives, I do use them.

What other advice do I have?

Elastic Search is working fine with streaming. I do not have any problem with that. I do not feel any problem with it because the library works well for the solution I am providing in Go. The libraries are healthy over there and it has worked well. I am satisfied with that. If there are some lags, I manage that. I have not used it. My review rating for Elastic Search is 9.5 out of 10.

Which deployment model are you using for this solution?

On-premises

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)

Luis S.

Elastic is good but the costs cannot be predicted

February 04, 2026
Review provided by G2

What do you like best about the product?

It is a tool that supports a community which generates many improvements and helps in support.

What do you dislike about the product?

When it is licensed and used in the cloud, the costs are not clear, making payments difficult and managing consumption.

What problems is the product solving and how is that benefiting you?

It is used as a repository for searches, whether from our SIEM solution or NOC.

Himanshu Y.

Great Experience with Elastic Search

January 23, 2026
Review provided by G2

What do you like best about the product?

It was easy to set up, and it was just as easy to get started right away.

What do you dislike about the product?

It’s a little slow when indexing bulk records from a CSV file.

What problems is the product solving and how is that benefiting you?

Elasticsearch helped us set up typo-resistant, faster searches to meet our clients’ search needs.

Vaibhav Shukla

Search performance has transformed large-scale intent discovery and hybrid query handling

January 22, 2026
Review from a verified AWS customer

What is our primary use case?

My use case has evolved over time with Elastic Search. Initially, we started with it as a searching solution. Before Elastic Search, our primary source of truth was SQL databases, the traditional RDBMS. We thought about taking the data from the traditional RDBMS because they were not able to cater to the scale that we wanted to achieve, so we migrated the data from MySQL, keeping it as the primary source of truth, but for the searching mechanism and wildcard searches, we migrated to Elastic Search.

My experience with the relevancy of search results in Elastic Search includes both traditional keywords and full-text search. In the supply chain industry, with millions of orders and customers such as CMA CGM, Maersk, or Kuehne+Nagel, filtering out those orders was essential, using a shipment number, transportation order number, or an origin or destination number. In the gaming industry at FDJ United, full-text searches make more sense to understand gaming intent. For example, when a user searches for 'I really want to play action games', we break down that full-text query, use custom text analyzers, and derive the intent behind the user's query in combination with a vector database alongside Elastic Search.

My assessment of the effectiveness of hybrid search, combining vector and text searches, shows that Elastic Search is remarkable for text-based searches. I have explored other solutions, but none can beat Elastic Search in that area. When I combine hybrid searches with vector databases, they store the mathematical representation of the data. For instance, to find the top 10 closest proximity based on a query, the vector database uses cosine similarity on the available data and suggests the top 10 results while Elastic Search can keep the metadata, enabling quick access to the entire database based on derived intent.

I have utilized trusted GenAI experiences related to semantic search and text-based search in my current project using Elastic Search. My go-to solution for text-based searches will always be Elastic Search, but for semantic search, I am trying to build a solution that emphasizes system-level understanding agents. For example, if a new engineer queries the agent for a system explanation, it scans all the relevant data and provides a comprehensive analysis of the service, contextualizing inputs to reduce hallucination, controlled temperatures for the LLM model, and reducing nucleus sampling. As for knowledge preservation, I use a vector database to store significant outputs generated by the LLM, depending on user preferences regarding the gravity of the analyses performed.

What is most valuable?

The best features of Elastic Search that I appreciate include its capability for eventual consistent systems where you do not need hard consistency, and it scales very smoothly. For wildcard searches and regex patterns, it really scales massively. It offers ILM, indexation lifecycle management, which allows you to enable a search for a span of six months for the data fed into the system while moving the rest to a new cluster. The structure of the inverted index document facilitates its core features, and I find how Elastic Search understands, indexes, and creates mappings for your data to be remarkable.

What needs improvement?

While Elastic Search is a good product, I see areas for improvement, particularly regarding the misconception that any amount of data can simply be dumped into Elastic Search. When creating an index, careful consideration of data massaging is essential. Elastic Search stores mappings for various data types, which must remain below a certain threshold to maintain functionality. Users need to throttle the number of fields for searching to avoid overloading the system and ensure that the design of the document is efficient for the Elastic Search index. Additionally, I suggest utilizing ILM periodically throughout the year to manage data shuffling between clusters, preventing hotspots in the distribution of requests across nodes.

For how long have I used the solution?

I have been using Elastic Search for more than six years.

What do I think about the stability of the solution?

In terms of stability, I would rate it eight out of ten regarding downtime, bugs, and glitches.

What do I think about the scalability of the solution?

For scalability, I assign it a ten out of ten.

How are customer service and support?

I would rate Elastic Search's technical support as nine out of ten.

Which solution did I use previously and why did I switch?

Before Elastic Search, our primary source of truth was SQL databases, the traditional RDBMS.

How was the initial setup?

Estimating the return on investment from Elastic Search is nuanced; however, I can share that initially, search times from traditional RDBMS were around two to three seconds, and with Elastic Search, we reduced that to 50 milliseconds, indicating a significant improvement.

What about the implementation team?

Assessing the complexity of deploying Elastic Search, I have a gray area because a separate DevOps team handles that aspect, but from my experience writing code and utilizing its features, I find it not complex at all.

What was our ROI?

What's my experience with pricing, setup cost, and licensing?

On the subject of pricing, Elastic Search is very cost-efficient. You can host it on-premises, which would incur zero cost, or take it as a SaaS-based service, where the expenses remain minimal.

Which other solutions did I evaluate?

When comparing Elastic Search to other vendors and products, I have recently explored Algolia, which is also a fully managed service. Elastic Search offers a choice between hosting on-premises or as a fully managed service, which has been beneficial compared to other solutions.

In my company's relationship with the vendor, I have always worked in product-based companies using Elastic Search, often as part of solutions from companies such as Manhattan Associates and in the gaming sector. For B2B industries, they sold to large clients such as Maersk and CMA CGM while my current company, Agoda, operates in the B2C space.

What other advice do I have?

Elastic Search does require some maintenance, especially when considering features such as ILM if you want to enjoy its capabilities. Maintenance tasks depend on the established data pipeline and may introduce some friction.

Currently, we are not using Elastic streams for log ingestion; previously, we utilized the ELK and EFK stacks with Logstash for log ingestion and Kibana for visualization. I also observe a trend where companies migrate to Grafana Loki instead of ELK.

Regarding integration aspects, Elastic Search has exposed REST APIs for all its services, making it easy to integrate with third-party models or endpoints regardless of the underlying infrastructure, as any modern development language can interact with these REST services.

I have not used the attack discovery feature.

My deployment of Elastic Search is on-premises.

At Agoda, we handle over 1.2 billion searches daily, facilitated by Elastic Search.

While I have been at my current company for four months, I am still getting to know my colleagues; however, I know there is a dedicated team focused on Elastic Search. This team exposes a service that acts as an intermediary for communication between Elastic Search and other services.

In my department, there are more than 100 people, whereas the overall organization consists of thousands, exceeding 10,000.

I would rate this review overall as a nine out of ten.

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