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Search is no longer just a nice-to-have feature - it’s the backbone of the digital user experience. Whether you’re helping customers find the right product, enabling teams to access key documents instantly, or making sense of massive data logs in real time, having the right search engine can improve your application’s performance. That’s why engineers and tech leaders often find themselves in a debate: Solr or Elasticsearch?
Both are open-source, powerful, and built on top of Apache Lucene, yet both take different approaches to solving the same problem.Solr and Elasticsearch are built on the powerful Apache Lucene library and offer advanced full-text search capabilities. However, their adoption and popularity have diverged over time. As of April 2025, according to the DB-Engines Ranking, Elasticsearch holds the top position among search engines with a score of 128.08, while Apache Solr ranks third with a score of 32.40.
This indicates that Elasticsearch's popularity is approximately four times that of Solr, reflecting its widespread adoption in various enterprise applications.
On Gartner Peer Insights, Elasticsearch has a rating of 4.4 stars from 316 reviews, whereas has a 4.1-star rating from 37 reviews.
Apache Solr is an open-source, enterprise-grade search platform built on Apache Lucene. Renowned for its scalability and fault tolerance, Solr offers advanced features like full-text search, faceted navigation, real-time indexing, and rich document handling (e.g., PDFs, Word files). Its robust architecture supports distributed indexing and load-balanced querying, making it ideal for high-traffic applications. Solr's REST-like APIs and extensive plugin ecosystem allow seamless integration and customization across various enterprise
Apache Solr has powerful capabilities, making it a preferred enterprise search engine for high-performance, scalable, and real-time data retrieval. Below are four standout features that define Solr’s strength in handling complex search use cases:
Solr supports complex search queries with features like stemming, synonym expansion, and hit highlighting, which are perfect for building high-precision enterprise search applications.
Solr’s built-in faceting engine enables users to navigate large datasets with filters based on fields like category, price, or date - key for eCommerce platforms and data exploration tools.
With support for distributed indexing, automatic sharding, and replication, Solr ensures high availability and seamless performance at scale.
Designed for real-time search engine requirements, Solr enables near-instant indexing of new data, making it ideal for log analytics and live content systems.
Apache Solr powers advanced search experiences worldwide. Its scalability, real-time indexing, and powerful querying features make it an excellent choice for various enterprise search needs. Given below are the key areas where Solr delivers significant value:
Solr is widely used to index and search documents in ECM platforms. It offers capabilities like full-text search across PDFs, Word files, and XML—ideal for legal firms, publishing houses, and large corporations.
Solr enables lightning-fast product discovery with faceted search, spell correction, and relevancy tuning. Retailers use it to deliver personalized and accurate search experiences at scale.
With support for real-time indexing and distributed querying, Solr analyzes large volumes of logs and machine data, making it a strong backend for monitoring and observability platforms.
Enterprises deploy Solr to power internal search across wikis, support docs, and internal databases - enhancing employee productivity with fast and relevant access to critical information.
Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene, widely used for real-time enterprise search, log analytics, and observability. It powers leading platforms like Slack, Shopify, and Wikimedia, and anchors the popular Elastic Stack (ELK). As of April 2025, it ranks #1 on DB-Engines among all search engines, holding over 55% market share in the enterprise search space. Its JSON-based querying, schema-less indexing, and high scalability make it a top choice for cloud-native and large-scale data applications.
Elasticsearch delivers high-speed, near real-time search and data analysis across large, distributed datasets - making it ideal for log analytics, security monitoring, and enterprise search solutions.
With built-in horizontal scaling, automatic sharding, and replication, Elasticsearch supports high availability and fault tolerance across cloud-native and large-scale deployments.
Elasticsearch supports a rich JSON-based query DSL, enabling complex full-text search, filtering, faceting, and aggregations—essential for search-driven applications and dashboards.
Seamlessly integrates with Logstash (data ingestion) and Kibana (visualization) for end-to-end data processing and observability, making it a top choice for centralized log management and analytics.
Did you know that Elasticsearch handles 620 requests/sec with 42ms latency, while Solr manages 580 requests/sec with 53ms latency?
From powering internal search to enabling real-time observability, Elasticsearch has become a foundational engine for modern data applications. Its speed, scalability, and powerful querying make it suitable for a wide array of enterprise-grade use cases:
Elasticsearch is a core component of the ELK Stack, widely used for collecting, indexing, and analyzing logs from servers, applications, and infrastructure in real-time—crucial for DevOps and SRE teams.
Companies integrate Elasticsearch into websites, intranets, and applications to deliver fast, relevant search results across structured and unstructured content - enhancing user experience and internal productivity.
Elasticsearch powers modern SIEM platforms by ingesting and correlating security events across systems. This helps detect threats, automate alerts, and ensure compliance with cybersecurity frameworks.
Retailers and marketplaces use Elasticsearch to drive real-time product discovery, with advanced filtering, relevance tuning, and autocomplete features - supporting dynamic, personalized eCommerce experiences.
Following are some key differences between Solr and Elasticsearch:
Choosing between Solr and Elasticsearch depends heavily on your enterprise’s specific architecture, scalability needs, team expertise, and integration goals. Both engines are capable and production-ready - but here’s how they stack up in real-world enterprise scenarios:
Recommended To Use: Elasticsearch
Elasticsearch is purpose-built for cloud-native environments. It supports real-time search, distributed processing, and seamless integration with tools like Kibana, Logstash, and Beats. It’s ideal for enterprises running DevOps, SIEM, and observability pipelines.
Recommended To Use: Solr
Solr’s schema-driven architecture and robust XML configuration allow for deep customization—perfect for enterprises with in-house search teams or legacy Java-based infrastructures that need fine-grained control over indexing and querying.
Recommended To Use: Elasticsearch
Elasticsearch excels in faceted analytics, complex nested queries, and real-time dashboards, making it a preferred choice for applications that need search and analytics, such as fraud detection, personalization engines, and product search.
Recommended To Use: Solr
Solr is often the go-to option for regulated industries (e.g., finance, healthcare) that require on-premise deployment and compliance-ready infrastructure with minimal vendor lock-in.
Elasticsearch is typically the better fit for cloud-first, real-time analytics-driven enterprises. Solr remains a strong contender for custom-heavy, on-premise, or Java-integrated ecosystems. Each has its niche - your choice should align with your enterprise architecture, compliance needs, and future scalability roadmap.
Here's a breakdown of when you should choose each, depending on your business needs, infrastructure maturity, and data ecosystem.
Solr and Elasticsearch offer powerful search capabilities but cater to different needs. Solr excels in complex search scenarios and integrates well with big data platforms, making it suitable for enterprises with such requirements. Elasticsearch, with its real-time analytics and scalability, is ideal for organizations focusing on log analysis, security analytics, and business intelligence. Ultimately, the choice should be guided.
At Zymr, we specialize in designing and implementing scalable, AI-powered search architectures tailored to enterprise needs. Whether you're building a Solr-based document retrieval system or deploying Elasticsearch for real-time observability, our experts bring deep experience in cloud-native platforms, DevOps automation, and AI-infused engineering to help you unlock the full potential of your data.
Let’s build a search solution that powers your next leap forward. Explore our capabilities and see how we can help you achieve your big-picture goals.
Elasticsearch is increasingly favored in modern, cloud-native applications due to its real-time analytics, scalability, and seamless integration with the Elastic Stack (ELK). However, Solr remains firm in legacy environments and custom enterprise search systems, especially where schema control and on-premise deployment are preferred.
Solr offers greater deployment flexibility for enterprises needing strict data governance, making it more suitable for GDPR and HIPAA-compliant environments. Since it can be fully self-hosted without vendor lock-in, Solr gives organizations tighter control over data privacy and access policies.
Yes, Elasticsearch provides more streamlined backup and snapshot tools, especially when used with Elastic Cloud, making disaster recovery faster and more automated. Solr also supports backups, but its processes often require more manual configuration and scripting for enterprise-grade resiliency.
Elasticsearch offers superior cloud integration, with native managed services like Amazon OpenSearch and Elastic Cloud on Azure and GCP. These services provide auto-scaling, security, and out-of-the-box monitoring - ideal for cloud-first enterprise architectures.
Elasticsearch is increasingly favored in modern, cloud-native applications due to its real-time analytics, scalability, and seamless integration with the Elastic Stack (ELK). However, Solr remains firm in legacy environments and custom enterprise search systems, especially where schema control and on-premise deployment are preferred.
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