Not long ago, some business leaders seemed to view enterprise search as a “nice-to-have.” Or, at the very least, they considered it more of a supporting utility than a strategic, business-critical platform.
Organizations use enterprise search tools to help employees locate internal documents and enable support teams to surface relevant information. And, of course, retailers have long needed search to help customers navigate their product catalogs. But historically, expectations have been relatively modest: Users entered a few keywords and received a list of links, and then they did the remaining legwork themselves to figure out which results were relevant.
It’s little surprise, then, that many companies have historically relied on self-managed open-source solutions like Apache Solr for enterprise search.
But even though the role of enterprise search has changed, many organizations’ approach to search has not. The rise of GenAI has trained customers to expect companies’ search tools to understand their search intent, deliver more relevant answers, and surface useful information even when their queries are imprecise. Employees largely expect the same from internal tools. And search has become a critical layer in the generative AI tools that organizations are building to give users instant access to context-informed intelligence.
Leaders are beginning to understand the hidden costs of self-managing such a critical capability and are beginning to seek alternatives.
Growing Demands
Eliminating the cost of a software license does not eliminate the cost of delivering enterprise search. Organizations must still supply the infrastructure and engineering talent required to keep the platform available and secure.
Buyers tend to look at the topline number first when evaluating enterprise tools, and the $0 license fee associated with Apache Solr. But the total cost of a self-managed search platform also includes servers, storage, monitoring tools, and backups, among other expenses. Solr relies on a separate Apache ZooKeeper ensemble for cluster coordination, meaning teams must deploy, monitor, upgrade, and maintain an additional distributed system.
These demands only grow as search becomes more important, with customer-facing AI tools and employee knowledge assistants requiring higher availability, lower latency, and even more robust security. Despite the considerable expertise available in open-source communities, self-managed search deployments do not automatically come with guaranteed response times, vendor escalation paths, or a single point of accountability. And Solr’s relatively small maintainer base and slower release cadence can make it more difficult for organizations to keep pace with developments in real-time indexing, vector search, and cloud-native operations.
Also, every hour that internal engineers spend simply keeping search operational is an hour that they cannot devote to building new capabilities and better user experiences.
Some organizations continue to find self-managed platforms to be worth the operational overhead. But as enterprise search becomes increasingly critical to business operations, others are concluding that a fully managed solution like Amazon OpenSearch Service is both more reliable and more economical.
The Benefits of Amazon OpenSearch Service
Amazon OpenSearch Service is a managed retrieval engine built on OpenSearch for agentic AI, search, and analytics. It combines traditional lexical search with vector and hybrid retrieval, helping organizations match exact keywords while also understanding the semantic meaning behind users’ queries. OpenSearch also includes native tools for embedding generation, neural search, reranking, and retrieval-augmented generation.
Cluster Insights surfaces issues with prescriptive recommendations, and OpenSearch Serverless eliminates capacity planning with automatic scaling that adapts to demand without downtime.
All of this is to say that Amazon OpenSearch Service lets your team offload infrastructure management, allowing your engineers to focus on building.
OpenSearch itself is an open-source, community-driven project at the Linux Foundation, with more than 2 billion downloads and contributions from thousands of contributors across hundreds of organizations. This means that you can benefit from neutral governance and long-term sustainability, while also taking advantage of the scalability, availability, and operational simplicity made possible by partnering with Amazon for delivery and management.
Real-world case studies show dramatic results for companies that make a switch. For example, the social media analytics firm Audiense saw a 1,400% reduction in query time after migrating workloads from Apache Solr to Amazon OpenSearch Service. The company’s engineers now spend around half an hour per quarter on maintenance, compared to frequent manual maintenance beforehand.
BigData Boutique worked with Yotpo, an ecommerce retention marketing company, to migrate to Amazon OpenSearch Service from its existing self-managed search solution, after errors from manual configurations led to multiple problems. As a result of the move, the company lowered cluster costs by 11% and cut around three weeks of annual maintenance work.
As with any major migration, there will be challenges. Organizations often lean on a partner like BigData Boutique to help them navigate API and feature differences, query language translation, configuration migration, custom plugins, change management, and other hurdles.
Making the Move
To learn more, watch the on-demand webinar hosted by Techstrong, all about migrating Apache Solr workloads to Amazon OpenSearch Service.
We cover:
- Why organizations are modernizing their search platforms.
- The key differences between Apache Solr and Amazon OpenSearch Service, including performance, scalability, and AI/ML capabilities.
- How to solve common challenges and streamline your migration journey.
Watch if your organization is looking to improve search performance, reduce management burdens for internal teams, or simply prepare your environment for the future of AI. If you already know you’re looking to make a change, click here to schedule a meeting about migrating from Apache Solr to OpenSearch.