Elastic Search vs PostgreSQL comparison

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Elastic Search

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Executive SummaryUpdated on Mar 5, 2025

PostgreSQL and Elasticsearch compete in the database and search engine markets respectively, addressing versatile data management needs. In terms of features, PostgreSQL offers extensive database functionalities, while Elasticsearch excels in search capabilities, making them leaders in their domains for specific use cases.

Features: PostgreSQL includes full SQL compliance, support for various programming languages, and robust documentation. It is known for write-intensive task scalability and extensibility. Elasticsearch, on the other hand, provides real-time search capabilities, is optimized for handling extensive text-based data, and integrates well with other tools for data indexing and search.

Room for Improvement: PostgreSQL could improve its read-only query performance and add SQL features like 'INSERT IGNORE' to compete with NoSQL in handling large data volumes. Elasticsearch faces challenges in usability and data management over large indices. Enhancing its machine learning integration and adopting more flexible licensing could enhance its enterprise appeal.

Ease of Deployment and Customer Service: Both PostgreSQL and Elasticsearch offer on-premises and cloud deployment options. PostgreSQL provides broader hybrid cloud deployment support, whereas Elasticsearch sees frequent implementation in both private and public cloud environments. Community support is rich for both, but PostgreSQL enjoys more commendations for active engagement, unlike Elasticsearch, which could benefit from improved documentation and streamlined support services.

Pricing and ROI: PostgreSQL's open-source nature keeps it free, bringing immediate ROI against commercial rivals like Oracle, reducing costs significantly. Elasticsearch, also open-source, offers paid features for advanced capabilities. It balances skilled support costs with node-based licensing, making it cost-effectively flexible compared to expensive solutions like Splunk, with suitability depending on whether traditional data management or advanced search analytics is the focus.

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

Elastic Search vs. PostgreSQL

December 2025

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Helped 881,707 peers since 2012

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

6.5

Number of Reviews

Ranking in other categories

Indexing and Search (1st), Cloud Data Integration (5th), Search as a Service (1st)

PostgreSQL

Ranking in Vector Databases

9th

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

125

Ranking in other categories

Open Source Databases (2nd)

Mindshare comparison

As of February 2026, in the Vector Databases category, the mindshare of Elastic Search is 3.9%, down from 6.4% compared to the previous year. The mindshare of PostgreSQL is 6.4%, up from 4.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Vector Databases Market Share Distribution
Product	Market Share (%)
Elastic Search	3.9%
PostgreSQL	6.4%
Other	89.7%

Vector Databases

Featured Reviews

Vaibhav Shukla

Senior Software Engineer at Agoda

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

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.

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Akhilesh Chipre

Senior Assosiate Consultant at Applied Materials

Real-time data capture optimizes database performance but Views create problems

One of the most valuable features is real-time data capture; it optimizes database performance. I think using real-time data capture reduces job running time and the amount of data sent at once with batch loads. Replicating the same data daily isn't optimal, so real-time reception improves application performance and reduces latency.

Read full review

Quotes from Members

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

Pros

"I value the feature that allows me to share the dashboards to different people with different levels of access."

"One thing I appreciate about Elastic Search is the ability to aggregate everything into one dashboard, so I can have monitoring, logs, and traces in one portal instead of having multiple different tools to do the same."

"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 most valuable feature is the out of the box Kibana."

"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 analytics with Elastic benefits us due to the huge traffic volume in our organization, which reaches up to 60,000 requests per second. With logs of approximately 25 GB per day, manually analyzing traffic behavior, payloads, headers, user agents, and other details is impractical."

"It provides deep visibility into your cloud and distributed applications, from microservices to serverless architectures. It quickly identifies and resolves the root causes of issues, like gaining visibility into all the cloud-based and on-prem applications."

"Using real-time search functionality to support operational decisions has been helpful."

"It gives us the possibility to store and query this data and also do this efficiently and securely and without delays."

More Elastic Search pros

"The built-in code procedural language is the most valuable. It has a built-in layer for code procedures. Its installation is very easy and quick, and it is free. It is also stable, and its performance is also good."

"The product's deployment is easy."

"It has completely met our needs. It works, and it is robust. We haven't had any problems with what PostgreSQL does for us and the way it does it. That's why we've been using it for so long. We understand it, and it does the job."

"The most valuable feature is the performance."

"The database has excellent performance."

"It is very simple to manage."

"With the database, you can provide a multi-component at the same service with the same performance, scalability, or all those things."

"PostgreSQL makes it very adaptable to several descriptions of a record. Instead of having several tables or several relations for one entity, I can adapt this entity. It can be a multiform entity. For example, here in Mexico, a company and a person can be sold to us as a physical entity or a physical person."

Cons

"There is a lack of technical people to develop, implement and optimize equipment operation and web queries."

"Better dashboards or a better configuration system would be very good."

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

"I think the pricing of Elastic Search is really, really expensive."

"I would like to be able to do correlations between multiple indexes."

"Could have more open source tools and testing."

"Elastic Search needs to improve authentication. It also needs to work on the Kibana visualization dashboard."

"Its licensing needs to be improved. They don't offer a perpetual license. They want to know how many nodes you will be using, and they ask for an annual subscription. Otherwise, they don't give you permission to use it. Our customers are generally military or police departments or customers without connection to the internet. Therefore, this model is not suitable for us. This subscription-based model is not the best for OEM vendors. Another annoying thing about Elasticsearch is its roadmap. We are developing something, and then they say, "Okay. We have removed that feature in this release," and when we are adapting to that release, they say, "Okay. We have removed that one as well." We don't know what they will remove in the next version. They are not looking for backward compatibility from the customers' perspective. They just remove a feature and say, "Okay. We've removed this one." In terms of new features, it should have an ODBC driver so that you can search and integrate this product with existing BI tools and reporting tools. Currently, you need to go for third parties, such as CData, in order to achieve this. ODBC driver is the most important feature required. Its Community Edition does not have security features. For example, you cannot authenticate with a username and password. It should have security features. They might have put it in the latest release."

More Elastic Search cons

"There could be a plugin to distribute the data on servers for the product."

"The solution needs to improve the query, documentation, and JSON data functionality."

"Sometimes, the views create problems. If you don't have the view, sometimes what happens is you need to have the drivers properly set up for PostgreSQL."

"The user interface for the clients could be easier to use as they are small businesses. From a technical support perspective, the documentation could be improved."

"It needs more integration."

"PostgreSQL could improve by adding data warehousing tools."

"The scalability is limited."

"It could be improved by using parallelization. You want basically, distributed computing."

Pricing and Cost Advice

"The tool is not expensive. Its licensing costs are yearly."

"We use the free version for some logs, but not extensive use."

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

"The solution is free."

"The tool is an open-source product."

"The basic license is free, but it comes with a lot of features that aren't free. With a gold license, we get active directory integration. With a platinum license, we get alerting."

"The pricing and license model are clear: node-based model."

"The price of Elasticsearch is fair. It is a more expensive solution, like QRadar. The price for Elasticsearch is not much more than other solutions we have."

More Elastic Search pricing and cost advice

"Our company pays for it. There are free versions available, but for advanced features, you obviously have to pay."

"We do not pay for licensing."

"There is an annual license."

"It could be much cheaper. If you would like to build an application on Amazon today, PostgreSQL is the standard database with Redshift. If you want other databases, you can add them, but PostgreSQL is the basis of everything. It's a question of money, that's it."

"The solution requires a license."

"It is free. In terms of operating costs, it basically needs the same platform on which Oracle runs."

"Affordable solution."

"The tool is cheaply priced compared to other RDBMS providers in the market."

Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

12%

Manufacturing Company

Government

Computer Software Company

13%

Financial Services Firm

11%

Comms Service Provider

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	37
Midsize Enterprise	10
Large Enterprise	43

By reviewers
Company Size	Count
Small Business	57
Midsize Enterprise	26
Large Enterprise	46

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?

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.

What needs improvement with ELK Elasticsearch?

How does Firebird SQL compare with PostgreSQL?

PostgreSQL was designed in a way that provides you with not only a high degree of flexibility but also offers you a cheap and easy-to-use solution. It gives you the ability to redesign and audit yo...

What do you like most about PostgreSQL?

It's a transactional database, so we use Postgres for most of our reporting. That's where it's helping.

What is your experience regarding pricing and costs for PostgreSQL?

The tool is free of cost. For now, it's not about making money. But once we perfect it, we can offer it to customers willing to pay for support and other services. Most of my deployments are free.

Algolia vs Elastic Search

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Compared 4% of the time

LanceDB vs Elastic Search

Compared 3% of the time

More Elastic Search Competitors

Firebird SQL vs PostgreSQL

Compared 23% of the time

MySQL vs PostgreSQL

Compared 9% of the time

MariaDB vs PostgreSQL

Compared 7% of the time

Chroma vs PostgreSQL

Compared 5% of the time

LanceDB vs PostgreSQL

Compared 5% of the time

Product Reports

Download Elastic Search product report

Elastic Search

February 2026

Download PostgreSQL product report

January 2026

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

PostgreSQL is a versatile and reliable database management system commonly used for web development, data analysis, and building scalable databases.

It offers advanced features like indexing, replication, and transaction management. Users appreciate its flexibility, performance, and ability to handle large amounts of data efficiently. Its robustness, scalability, and support for complex queries make it highly valuable.

Additionally, PostgreSQL's extensibility, flexibility, community support, and frequent updates contribute to its ongoing improvement and stability.

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.

1. Apple 2. Cisco 3. Fujitsu 4. Instagram 5. Netflix 6. Red Hat 7. Sony 8. Uber 9. Cisco Systems 10. Skype 11. LinkedIn 12. Etsy 13. Yelp 14. Reddit 15. Dropbox 16. Slack 17. Twitch 18. WhatsApp 19. Snapchat 20. Shazam 21. SoundCloud 22. The New York Times 23. Cisco WebEx 24. Atlassian 25. Cisco Meraki 26. Heroku 27. GitLab 28. Zalando 29. OpenTable 30. Trello 31. Square Enix 32. Bloomberg