Elastic Search vs SingleStore comparison

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Elastic Search

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Executive SummaryUpdated on Mar 5, 2025

Elastic Search and SingleStore compete in the data analytics and log monitoring category. Based on feature richness and scalability, Elastic Search appears to have the upper hand, while SingleStore excels in performance and SQL compatibility.

Features: Elastic Search offers advanced log monitoring and flexible data indexing, with tools like Logstash and Kibana that enhance search capabilities and visualization. Its scalability and enterprise landscape support make it a strong choice for analyzing rich data sets. SingleStore is known for its MySQL compatibility, fast data ingestion, and distributed architecture, excelling in high concurrency and in-memory storage for analytical tasks.

Room for Improvement: Elastic Search could improve its open-source security features, machine learning capabilities, and user-friendliness. It also needs better pricing transparency and technical support. SingleStore could benefit from more built-in SQL features, improved partition distribution, and enhanced documentation. Expanding connectivity with other services and simplifying SQL functionalities for complex operations is needed.

Ease of Deployment and Customer Service: Both Elastic Search and SingleStore are deployed widely across on-premises and cloud environments. Elastic Search benefits from a strong community and extensive documentation but could improve its direct technical support. SingleStore provides moderate technical support and could enhance community resources and response times.

Pricing and ROI: Elastic Search provides a good ROI as an open-source product, though premium feature licensing costs can be high and the pricing model complicated. SingleStore offers a competitive, clear pricing structure beneficial for long-term analytical and storage needs. Initial and ongoing costs should be carefully weighed for ROI optimization.

To learn more, read our detailed Elastic Search vs. SingleStore Report (Updated: April 2025).

Elastic Search vs. SingleStore

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Elastic Search

Ranking in Vector Databases

2nd

Average Rating

8.2

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Indexing and Search (1st), Cloud Data Integration (9th), Search as a Service (1st)

SingleStore

Ranking in Vector Databases

13th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Database as a Service (DBaaS) (7th)

Mindshare comparison

As of May 2025, in the Vector Databases category, the mindshare of Elastic Search is 5.4%, down from 7.4% compared to the previous year. The mindshare of SingleStore is 1.8%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Vector Databases

Featured Reviews

Anand_Kumar

Enterprise Architect at DXC Technology

Captures data from all other sources and becomes a MOM aka monitoring of monitors

Scalability and ROI are the areas they have to improve. Their license terms are based on the number of cores. If you increase the number of cores, it becomes very difficult to manage at a large scale. For example, if I have a $3 million project, I won't sell it because if we're dealing with a 10 TB or 50 TB system, there are a lot of systems and applications to monitor, and I have to make an MOM (Mean of Max) for everything. This is because of the cost impact. Also, when you have horizontal scaling, it's like a multi-story building with only one elevator. You have to run around, and it's not efficient. Even the smallest task becomes difficult. That's the problem with horizontal scaling. They need to improve this because if they increase the cores and adjust the licensing accordingly, it would make more sense.

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Yasin Sarı

Senior Data Analyst at a comms service provider with 1,001-5,000 employees

High-speed data processing, seamless scalability, and excellent high availability making it an optimal choice for those prioritizing performance and efficiency in a database solution

There's a noteworthy consideration when it comes to collecting massive amounts of data. It is not the optimal choice for direct data collection through queries, and it's more suited for aggregation tasks. Attempting to use it for direct extraction, for instance, might lead to memory-related challenges. While MySQL version five might lack extensive SQL capabilities, SingleStore also has its constraints, requiring simpler SQL writing. This becomes evident when seeking advanced functionalities like window functions or JSON functions, where SingleStore doesn't offer an extensive toolkit, necessitating a more straightforward approach to SQL.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Elastic Search makes handling large data volumes efficient and supports complex search operations."

"Gives us a more user-friendly, centralized solution (for those who just needed a quick glance, without being masters of sed and awk) as well as the ability to implement various mechanisms for machine-learning from our logs, and sending alerts for anomalies."

"The solution offers good stability."

"The AI-based attribute tagging is a valuable feature."

"The most valuable features are the data store and the X-pack extension."

"It is stable."

"I have found the sort capability of Elastic very useful for allowing us to find the information we need very quickly."

"The products comes with REST APIs."

More Elastic Search pros

"The most valuable feature is the ability to create pipelines, streamline and extract data from the pipelines."

"The product can automatically reinstall and reconfigure in case of a shutdown."

"MemSQL supports the MySQL protocol, and many functions are similar, so the learning curve is very short."

"It's a distributed relational database, so it does not have a single server, it has multiple servers. Its architecture itself is fast because it has multiple nodes to distribute the workload and process large amounts of data."

"The ability to store data in memory is a standout feature, enhanced by robust failover mechanisms."

"The paramount advantage is the exceptional speed."

Cons

"The metadata gets stored along with indexes and isn't queryable."

"It should be easier to use. It has been getting better because many functions are pre-defined, but it still needs improvement."

"There should be more stability."

"Could have more open source tools and testing."

"We see the need for some improvements with Elasticsearch. We would like the Elasticsearch package to include training lessons for our staff."

"The price could be better. Kibana has some limitations in terms of the tablet to view event logs. I also have a high volume of data. On the initialization part, if you chose Kibana, you'll have some limitations. Kibana was primarily proposed as a log data reviewer to build applications to the viewer log data using Kibana. Then it became a virtualization tool, but it still has limitations from a developer's point of view."

"The different applications need to be individually deployed."

"Elastic Search needs to improve its technical support. It should be customer-friendly and have good support."

More Elastic Search cons

"For new customers, it's very tough to start. Their documentation isn't organized, and there's no online training available. SingleStore is working on it, but that's a major drawback."

"It is not the optimal choice for direct data collection through queries, and it's more suited for aggregation tasks."

"There should be more pipelines available because I think that if MemSQL can connect to other services, that would be great."

"We don't get good discounts in Pakistan."

"Having the ability to migrate servers using a single command would be extremely beneficial."

"Poor key distribution can significantly impact performance, requiring a backward approach in design rather than adding tables incrementally."

Pricing and Cost Advice

"We are using the Community Edition because Elasticsearch's licensing model is not flexible or suitable for us. They ask for an annual subscription. We also got the development consultancy from Elasticsearch for 60 days or something like that, but they were just trying to do the same trick. That's why we didn't purchase it. We are just using the Community Edition."

"To access all the features available you require both the open source license and the production license."

"Elastic Search is open-source, but you need to pay for support, which is expensive."

"Although the ELK Elasticsearch software is open-source, we buy the hardware."

"The price of Elastic Enterprise is very, very competitive."

"we are using a licensed version of the product."

"The version of Elastic Enterprise Search I am using is open source which is free. The pricing model should improve for the enterprise version because it is very expensive."

"We are paying $1,500 a month to use the solution. If you want to have endpoint protection you need to pay more."

More Elastic Search pricing and cost advice

"I would advise users to try the free 128GB version."

"SingleStore is a bit expensive."

"Using it for analytical purposes can be cost-effective in the long run, especially in terms of infrastructure."

"They have two main options: cloud installation and bare-metal installation, each with different pricing models."

"The product's licensing is not expensive. It is comparable."

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Top Industries

By visitors reading reviews

Computer Software Company

17%

Financial Services Firm

15%

Government

Manufacturing Company

Financial Services Firm

33%

Computer Software Company

12%

Manufacturing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about ELK Elasticsearch?

Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time anal...

What is your experience regarding pricing and costs for ELK Elasticsearch?

I don't know about pricing. That is dealt with by the sales team and our account team. I was not involved with that.

What needs improvement with ELK Elasticsearch?

I found an issue with Elasticsearch in terms of aggregation. They are good, yet the rules written for this are not really good. There is a maximum of 10,000 entries, so the limitation means that if...

What do you like most about SingleStore DB?

The paramount advantage is the exceptional speed.

What is your experience regarding pricing and costs for SingleStore DB?

Using it for analytical purposes can be cost-effective in the long run, especially in terms of infrastructure. While building an on-premise cluster incurs an initial cost for servers with ample RAM...

What needs improvement with SingleStore DB?

Azure Search vs Elastic Search

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Product Reports

Elastic Search

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Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud

No data available

Overview

Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.

Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.

Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.

At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.

Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.

In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.

Elastic

SingleStore enables organizations to scale from one to one million customers, handling SQL, JSON, full text and vector workloads — all in one unified platform.

Sample Customers

T-Mobile, Adobe, Booking.com, BMW, Telegraph Media Group, Cisco, Karbon, Deezer, NORBr, Labelbox, Fingerprint, Relativity, NHS Hospital, Met Office, Proximus, Go1, Mentat, Bluestone Analytics, Humanz, Hutch, Auchan, Sitecore, Linklaters, Socren, Infotrack, Pfizer, Engadget, Airbus, Grab, Vimeo, Ticketmaster, Asana, Twilio, Blizzard, Comcast, RWE and many others.

6Sense, ADNOC, Adobe, Akamai, CARFAX, Cigna, Cisco, Comcast, DBS Bank, Dell, Dentsu, EY, FirstEnergy, GE, Goldman Sachs, Heap, Hulu, IMAX, Kakao, Kroger, LG, LiveRamp, Lumana, NBC, OpenDialog, Outreach, Palo Alto Networks, PicPay, RBC, Samsung, Siemens, SiriusXM, SK Telecom, SKAI, Sony, State Street Financial, STC, SunRun, TATA, Thorn, and ZoomInfo.

Elastic Search vs. SingleStore