Guide to Elasticsearch competitors: Opensearch, Algolia, Solr, MongoDB, Splunk

When searching for a powerful search and analysis engine, many business owners turn to Elasticsearch for its advanced capabilities. However, you may encounter challenges like high resource consumption, rising operational costs, or difficulties in fine-tuning it for optimal performance. So, is Elasticsearch really the best solution for you?

At Greenice, we’ve helped businesses tackle these search-related issues firsthand. Whether it’s fine-tuning Elasticsearch for optimal results or exploring alternative solutions, we’ve been there. For example, we successfully integrated Elasticsearch into a database of 15,000 exercises for physiotherapists, and also implemented Algolia for a high-performance eCommerce platform.

In this article, we’ll break down what Elasticsearch is, its potential pitfalls, and its undeniable strengths. We’ll also compare it with some of the top alternatives on the market and guide you through the key factors to consider when choosing the best solution for your business.

What is Elasticsearch?

Elasticsearch is a distributed, open-source search and analytics engine designed to handle vast amounts of data in real time. It’s the core of the Elastic Stack, enabling fast search, fine-tuned relevancy, and powerful analytics for various data types—text, numbers, geo, structured, and unstructured. Think of it as a search engine like Google but built specifically for businesses to search and analyze internal data.

It's essential to understand that there are two main versions: the open-source Elasticsearch and the paid Elastic Cloud version. While the open-source version remains free to use, it's not entirely "open-source" in the traditional sense. In 2021, Elasticsearch changed its licensing to the Server Side Public License (SSPL), meaning while the code is still available, it imposes restrictions on how the software can be used, particularly for hosting as a service. This change occurred after a dispute with AWS, which led to the development of OpenSearch as a fully open-source alternative. On the other hand, the paid Elastic Cloud version offers a fully managed service with advanced features, including enhanced security, machine learning, and integrated support, catering to businesses looking for robust search solutions without the overhead of managing infrastructure.

Companies like Airbnb use Elasticsearch to provide relevant search results across millions of listings, while Spotify powers its music search engine with Elasticsearch for instant results. By indexing data from various sources, Elasticsearch enables businesses to search through large datasets almost instantly, making it an essential tool for everything from customer data retrieval to advanced AI-driven tasks.

What are the disadvantages of Elasticsearch?

While Elasticsearch is a powerful tool, it does come with several downsides that businesses should consider:

1. Resource-intensive: Elasticsearch demands significant memory, computational power, and infrastructure to handle large datasets and complex queries. Self-hosting and configuration are time-consuming, requiring ongoing management and DevOps expertise to maintain cluster health and performance.
2. High costs: Scaling Elasticsearch comes with increased infrastructure costs as data grows, requiring more resources like servers and cloud storage. Additionally, newer versions of Elasticsearch may involve licensing fees for advanced features, which can add to the overall cost. 
3. Limited security: Elasticsearch's free version offers limited security features like basic authentication and encryption, which may not be sufficient for projects needing advanced security. For robust security options, users must upgrade to paid plans.
4. Slower search: Elasticsearch's hybrid indexing (in-memory + disk-based) can result in slower search speeds compared to in-memory-only systems, making it less ideal for applications requiring instant response times.
5. Complexity: Elasticsearch has a steep learning curve and often requires fine-tuning for optimal performance. This complexity may necessitate consulting services or specialized staff, making it challenging for businesses without dedicated expertise. These factors can add complexity and cost to using Elasticsearch, making it essential to evaluate whether it’s the right fit for your business, particularly if you're operating with limited resources or technical expertise.

What are the main strengths of Elasticsearch?

Elasticsearch has several key strengths that make it a go-to solution for businesses needing powerful search and data analytics:

1. Query complexity: One of Elasticsearch's standout features is its ability to handle complex queries efficiently. It allows users to filter search results based on multiple factors such as price, location, or product reviews, making it highly valuable for eCommerce platforms, real estate websites, and other businesses that need multi-dimensional search. 
2. Scalability: Elasticsearch scales horizontally, which means it can grow seamlessly alongside your business. Whether you're starting small or dealing with millions of daily searches, Elasticsearch can distribute data across multiple servers, maintaining its speed and performance. This makes it ideal for businesses expecting significant data growth or increased web traffic.
3. Reliability: Elasticsearch's distributed nature ensures high availability, meaning your search services stay online even if parts of your system fail. Data is replicated across nodes, which ensures reliability and reduces the risk of downtime, crucial for high-traffic websites.
4. Powerful ecosystem: Elasticsearch is part of the larger Elastic Stack, which includes tools like Kibana for data visualization and Logstash for data processing. This ecosystem adds even more power, especially for businesses that need a comprehensive data analysis stack. For instance, news platforms like The Guardian use Elasticsearch to quickly sift through millions of articles, and Uber relies on it for real-time system monitoring.
5. Flexibility: Elasticsearch works with a variety of data types, from numbers and text to geospatial data. Whether you're dealing with product catalogs, logs, or articles, Elasticsearch's versatility makes it suitable for almost any search or analytics use case. With Greenice’s expertise in implementing Elasticsearch, we can ensure that the system is fine-tuned to your specific business needs, optimizing performance as your data and user base grows.

What is better than Elasticsearch? Key things to consider!

There isn’t a one-size-fits-all solution, and depending on your business needs, other tools might better suit your search requirements. Key considerations include how much data you're handling, the types of search queries you need, and the technical expertise available within your team. In certain cases, alternatives like OpenSearch, Solr, or even specialized tools might offer better flexibility, cost savings, or simplicity.

If after weighing down the pros and cons of ES you decide to choose a different tool, there are a few things to consider to understand which one is better for you. Key factors to consider when choosing an Elasticsearch alternative

1. Do you need the freedom and flexibility of open-source or the simplicity of closed-source?

Open-source Elasticsearch alternatives like OpenSearch and Solr offer significant freedom and flexibility, allowing your team full control over customization, integration, and deployment. This means you can tailor the software to fit your exact needs without being tied to a vendor’s pricing model or roadmap. The community-driven nature of open-source platforms ensures constant improvements, but it also requires your team to have the technical expertise to manage updates, security, and scaling. For example, OpenSearch provides the same core functionality as Elasticsearch but with a more transparent Apache 2.0 license, avoiding the restrictive licensing changes Elasticsearch introduced in recent years.

In comparison, closed-source platforms like Algolia or Splunk are built for ease of use, offering managed services, strong support, and faster setup—ideal for businesses that prefer a hands-off approach. However, this convenience comes at the expense of customization, with limited flexibility and changes dependent on the vendor’s roadmap. While these platforms are great for reducing operational overhead, they may not offer the same level of control that open-source solutions provide. 

Ultimately, the choice between open-source and closed-source comes down to your team's technical capacity and the degree of customization you require. Open-source allows for greater control, but you need to manage it; closed-source simplifies operations but at the cost of flexibility.

2. How big is your data now, and how much will it grow in the future?

The current size of your data and your projected growth are crucial factors when selecting a search solution. Elasticsearch, OpenSearch, and Solr are highly scalable and capable of managing large, distributed datasets. These tools can handle millions of search queries daily while maintaining high performance. If your business anticipates significant data growth, these solutions will scale with your needs, distributing data across clusters to ensure speed and reliability.

For smaller datasets, lighter-weight alternatives like Typesense or ZincSearch may be more efficient, offering similar functionality without the resource-heavy overhead that comes with larger platforms like Elasticsearch. These alternatives are ideal for businesses that prioritize simplicity and lower costs, with better search speed (due to in-memory indexing) and performance.

3. Do you need lightning-fast, real-time search capabilities?

If real-time search is critical to your business, in-memory search engines like Typesense or RediSearch are optimized for speed, making them excellent choices for applications requiring instant results. For example, Typesense is known for providing sub-millisecond search times, which can dramatically improve user experience on platforms that need real-time responsiveness, such as eCommerce websites or live news platforms.

However, in-memory systems are often more expensive to run, as they require more RAM, which can drive up infrastructure costs. Disk-based systems like Elasticsearch are still fast but might not achieve the same level of performance for real-time needs. That said, disk-based solutions are more cost-effective for businesses handling large, persistent datasets that don’t require instantaneous responses.

4. Are you looking for a cost-effective solution that doesn’t compromise on essential features?

When choosing search technology, it's essential to consider where your money is going. Open-source solutions like Sphinx and ZincSearch may be free but come with costs for hosting, infrastructure, and maintenance, which require skilled staff to manage. Paid solutions like Algolia or Elastic Cloud include hosting and minimal maintenance, though they typically have higher upfront costs.

This is where the total cost of ownership (TCO) comes into play—it’s more than just the purchase price. TCO includes setup, infrastructure, ongoing maintenance, and hidden costs like training and long-term support. Open-source options may seem cheaper at first but can have significant hidden expenses. Paid solutions often cover these needs, offering a simpler, more predictable cost structure over time. When choosing your search tool, consider both the immediate and long-term costs to make the best decision for your budget.

5. Is log aggregation or time-series data critical to your business operations?

For businesses dealing heavily with log aggregation or time-series data (such as monitoring application performance or system logs), specialized tools like Graylog, Loki, and InfluxDB may offer more tailored solutions than Elasticsearch.

Graylog is designed specifically for log management and allows businesses to analyze large volumes of system logs in real time, making it easier to detect issues or anomalies. Similarly, Loki integrates well with Prometheus for metrics and logging in Kubernetes environments. These tools are optimized for handling logs, often offering better performance and easier setup for log-heavy use cases than Elasticsearch’s general-purpose search engine. 

At the same, ES can be a great all-in-one solution as it has a variety of useful features apart from logs, and doesn’t require multiple extra tools.

6. Do you need advanced capabilities (AI, ML, advanced analytics)?

If your business requires machine learning (ML) or advanced analytics in your search platform, it's important to note that only the paid version of Elasticsearch (Elastic Cloud) offers AI capabilities. The free, open-source version lacks built-in AI features, limiting its utility for advanced search needs. AI-powered search can be vital for enhancing user experiences, from personalizing search results to detecting anomalies in log data, which is why many companies turn to specialized solutions.

In real-life applications, AI is used to improve search relevance by understanding user intent, applying natural language processing (NLP) for better query comprehension, and optimizing results through ranking algorithms. AI can also be used in predictive search (e.g., autosuggestions based on historical user behavior) and in more sophisticated areas like fraud detection and automated recommendations. Solutions like Apache Flink excel in real-time data processing and machine learning, handling tasks like complex event processing, while Kyligence specializes in OLAP, making it ideal for businesses performing large-scale, complex data queries and predictive analytics.

For instance, companies like Amazon use AI in search engines to personalize product recommendations, while Netflix applies ML to predict user preferences and deliver relevant content.

7. Is search within documents or NoSQL databases a priority?

If your data is stored in NoSQL databases like MongoDB or Couchbase, and you need integrated search functionality, these platforms may be more suitable than Elasticsearch.

MongoDB Atlas includes built-in search capabilities tailored for businesses already leveraging MongoDB for document-based data storage. It allows for seamless integration of search and data storage without needing an additional search engine like Elasticsearch. Couchbase also offers full-text search as part of its NoSQL platform, providing a unified solution for data management and search.

8. Do you have specific compliance or security requirements?

Security needs range from basic protections like encryption and authentication to more advanced requirements such as role-based access control (RBAC), auditing, and compliance with regulations like HIPAA, GDPR, or PCI-DSS—especially critical in industries like healthcare, finance, or government.

Elasticsearch’s free version provides basic security features like authentication and encryption, but advanced options like RBAC and auditing are only available in the paid tiers. This limits its ability to meet stricter compliance needs without additional cost.

Alternatives like Splunk and Algolia come with built-in advanced security features. Splunk offers robust encryption, access control, and auditing, making it ideal for compliance-heavy industries such as finance and healthcare. Algolia provides secure data protection tailored for customer-facing platforms, especially in eCommerce and FinTech.

9. Are you dealing with specific data types that require specialized search engines?

If your business works with highly specialized data, such as visual or geospatial data, you may benefit from using search engines like Yext or Vision AI that are optimized for specific data types.

Yext excels at location-based searches, making it ideal for businesses that need to provide users with precise location data (such as real estate platforms or travel agencies). Vision AI, on the other hand, is tailored for visual data, enabling businesses to perform image-based searches or identify objects within images. These specialized search engines outperform general-purpose tools like Elasticsearch when it comes to accuracy and efficiency for niche data types.

Elasticsearch alternatives

Let’s compare Elasticsearch with some of its most popular competitors:

1. OpenSearch vs Elasticsearch: Open-source nature

OpenSearch is an open-source search and analytics engine developed by Amazon, forked from Elasticsearch. It offers the same core capabilities like full-text search, distributed architecture, and real-time analytics, making it ideal for website search, enterprise search, and log analytics. OpenSearch focuses on open-source governance, while Elasticsearch has moved to a more restrictive license.

Use cases

Both tools are used in industries that require fast data retrieval and analysis, such as eCommerce (product searches), IT (log analytics), and media (content search). OpenSearch is favored by businesses looking for an open-source solution to avoid vendor lock-in.

Distinctive features

• Open-source: OpenSearch is fully open-source with the Apache 2.0 license, offering greater freedom and customization. Elasticsearch has shifted to a more restrictive license, limiting some open-source usage.
• Cost-effectiveness: OpenSearch is free for all features, including advanced ones, while Elasticsearch requires paid subscriptions for certain features.
• Amazon ecosystem integration: OpenSearch is often used within Amazon's ecosystem, particularly with OpenSearch Dashboards, offering tighter integration with AWS services for log aggregation and analytics.
• Security and compliance: OpenSearch offers advanced security features, like role-based access control, for free. Elasticsearch includes these features only in its paid tiers.

2. Solr vs Elasticsearch: Customized search

Solr is an open-source search platform built on Apache Lucene, providing robust search functionalities like full-text search, real-time indexing, and distributed search. It has been widely used for over a decade and is favored for highly customizable search implementations, especially in large-scale applications. But note that the learning curve is even harder for Solr than it is for ES.

Use cases

Solr is used by major companies such as Netflix, eBay, and Instagram for search across large datasets, eCommerce platforms, and log analysis. Its ability to index and search multiple websites simultaneously makes it a popular choice for multi-site search, content management systems, and applications requiring real-time indexing. Industries like eCommerce, media, and technology rely on Solr for complex, large-scale search solutions.

Distinctive features

• Open-source and community-driven: Solr is fully open-source and community-driven, allowing contributions from anyone, unlike Elasticsearch, where contributions are tightly controlled by the Elastic company.
• Shard placement: Solr has a more static approach to shard placement, requiring manual rebalancing of data when nodes join or leave a cluster, while Elasticsearch automatically handles this process.
• Cost-effectiveness: Solr offers advanced features in its open-source version without requiring paid subscriptions, making it a more cost-effective option for businesses that need customization but want to avoid licensing fees. 
• Faceted search and structured data: Solr excels in handling structured data and documents (such as PDFs and Word files) and is highly efficient in faceted search and structured data analysis, which is not the primary focus of Elasticsearch.
• Security features: Solr offers advanced security features such as role-based access control and field-level security out-of-the-box in its open-source version, while Elasticsearch requires a paid subscription for these features.

3. MongoDB vs Elasticsearch: Simplicity

MongoDB is an open-source NoSQL database designed to store and manage large volumes of document-oriented data. It is well-suited for applications that require flexible data models, such as content management systems (CMS), eCommerce, IoT, and real-time analytics. While MongoDB offers built-in search functionality through its MongoDB Atlas Search feature, its primary focus is data storage rather than search optimization. In contrast, Elasticsearch is a search engine built on top of Apache Lucene, optimized for fast, full-text search across large datasets.

Use cases

MongoDB is widely used in industries that require large-scale data storage with flexible schema management, such as finance, eCommerce, IoT, and content management. Common use cases include storing product catalogs, customer data, and real-time analytics data. MongoDB is ideal for handling NoSQL data structures, providing a reliable, scalable solution for CRUD operations without needing advanced search capabilities.

Elasticsearch, on the other hand, is used in industries that require fast, complex searches, such as media, eCommerce (product search), and IT (log analytics). Its full-text search capabilities make it the better choice for applications requiring search-first functionality, such as website search, log analysis, and business analytics.

Distinctive features

• Document and NoSQL focus: MongoDB is optimized for document-oriented NoSQL data storage and CRUD operations, ideal for unstructured data and flexible schemas, unlike Elasticsearch, which prioritizes search.
• Flexible data models: MongoDB handles diverse data types well, making it better for applications needing flexible, schema-less storage like CMS or IoT platforms.
• Scalability for data storage: MongoDB excels at large-scale data storage with automatic sharding, suited for distributed data management rather than just fast search.
• Integrated querying and storage: MongoDB combines data storage and querying in one system, perfect for apps where storage is primary, and search is secondary.
• Compliance and security: MongoDB Atlas offers built-in security features like encryption and compliance certifications, ideal for businesses needing secure, compliant data storage.

4. Splunk vs Elasticsearch: Analytics

Splunk is a proprietary software platform primarily used for monitoring, searching, analyzing, and visualizing machine-generated data in real time. Its primary focus is on IT operations, security analytics, and machine data processing, making it ideal for businesses that need to monitor system logs, detect anomalies, and visualize data from multiple sources. Elasticsearch, on the other hand, is an open-source search engine built for full-text search, data analytics, and log management. While Elasticsearch is versatile across various use cases, Splunk specializes in security information and event management (SIEM) and operational insights.

Use cases

Splunk is widely used in enterprise IT environments for log monitoring, security analytics, and operational intelligence. Its features make it a powerful tool for Security Operations Centers (SOCs) and businesses needing to monitor real-time machine data from various sources. It’s commonly found in industries like finance, telecommunications, and IT services where security and system reliability are critical.

Elasticsearch, on the other hand, is versatile enough for eCommerce product searches, website search, and log analytics. It is widely used in sectors like eCommerce, media, and IT for its fast full-text search, scalability, and integration with analytics tools like Kibana. 

Distinctive features

• Proprietary and supported: Splunk is closed-source with built-in vendor support, offering easier setup and management, especially for non-technical teams, but at a higher cost.
• Real-time machine data analysis: Splunk specializes in real-time log monitoring and anomaly detection, making it ideal for IT operations and security (SIEM) without the need for additional tools like Elasticsearch.
• Pricing by data ingestion: Splunk charges based on daily data ingestion, which can become expensive for large volumes but offers comprehensive out-of-the-box solutions for log analysis and security. 
• Advanced machine learning: Splunk provides built-in, user-friendly machine learning for real-time predictive analytics, whereas Elasticsearch's ML features require a paid subscription.
• Compliance and security: Splunk excels in regulated industries with advanced compliance and security features, offering comprehensive tools for meeting strict regulatory requirements.

5. Algolia vs Elasticsearch: Speed and ease of use

Algolia is a proprietary, fully managed search-as-a-service platform that focuses on delivering fast and highly relevant search results in real time. It's ideal for businesses that prioritize speed, ease of implementation, and minimal infrastructure management. Algolia provides instant search experiences, making it perfect for applications where user experience is key, such as eCommerce websites and content-heavy applications. In contrast, Elasticsearch is built for more complex, large-scale search and data analysis use cases. Elasticsearch offers full-text search, filtering, and advanced data aggregation features, making it a powerful tool for enterprise-level applications.

Use cases

Algolia is most commonly used in industries like eCommerce, media, and SaaS, where fast search and seamless user experience are critical. Applications that need real-time, instant search, such as product searches on eCommerce platforms or content retrieval on media sites, benefit most from Algolia. It’s also popular for businesses that prefer a fully managed service without the complexity of handling infrastructure.

Elasticsearch, on the other hand, is widely used in industries requiring complex search functionality, real-time data analysis, and large-scale data management. Its applications include enterprise search, business analytics, log management, and data processing across sectors like IT, finance, and media.

Distinctive features

• Fully managed: Algolia handles infrastructure, updates, and support, ideal for businesses wanting an easy-to-manage, hassle-free solution.
• Instant search: Algolia is designed for real-time, lightning-fast search, making it perfect for consumer-facing apps and eCommerce with minimal latency.
• Pricing: Algolia charges based on queries and operations, which can become costly with high traffic, but it’s efficient for moderate needs. 
• Search-optimized: Algolia excels in simple, user-friendly search for product catalogs and websites, focused on speed and relevance rather than complex data searches.
• Built-in security: Algolia provides integrated security and maintenance, a convenient choice for businesses that prefer not to manage these themselves.

For a better understanding, let’s look at Elasticsearch alternatives comparison table:

After considering each alternative, you'll have a clearer idea of the best option for your needs. If none seem to fit perfectly, don't worry—new solutions are constantly emerging, while others fade in popularity. For example, Sphinx was once a leading choice for search and analytics, and while it's still powerful for certain projects, technology evolves rapidly. In just a few months, there may be new contenders among the top Elasticsearch alternatives, offering even more possibilities for your business.

Our experience

At Greenice, we have experience working with Elasticsearch and its alternatives across various industries. Whether you're looking to optimize Elasticsearch or explore another solution, we can help you choose and implement the best search technology for your business. Our team specializes in crafting tailored solutions that align with your goals, ensuring seamless performance, scalability, and cost-effectiveness.

Here are some of our projects that have ES integration:

Enhanced search for a therapist exercise platform

We leveraged Elasticsearch to improve Wibbi’s (ex-Physiotec) vast rehabilitation exercise database of over 15,000 exercises. Wibbi needed a faster, more accurate search system to help therapists create personalized Home Exercise Programs (HEPs) for patients. 

The platform struggled with slow, inaccurate searches. Therapists required a system that could handle complex queries, support multiple languages.

Another important requirement was the integration of the platform with Electronic Medical Records (EMRs) to enhance patient care.

Elasticsearch's scalability and advanced features, like full-text search and real-time filtering, allowed therapists to efficiently find the right exercises. We also integrated Wibby with 25+ EMR systems, resulting in faster, more precise searches, improved therapist productivity, and better patient outcomes.

Streamlined search for a home services review platform

We helped NoCowboys, a top New Zealand home services review site, enhance search functionality and platform performance. With thousands of service providers, NoCowboys needed a faster, more accurate way to connect users with the best local businesses.

Their existing search engine was outdated and struggled to deliver relevant results, considering location, business ratings, and review recency.

Elasticsearch’s scalability and advanced search logic allowed us to optimize the system for real-time, accurate results. Factoring in user proximity, ratings, and reviews, the new search engine significantly improved relevance and speed, boosting user engagement and business growth.

Scaling a global architecture and design marketplace

Arcbazar, the world's largest crowdsourcing marketplace for architecture and design, needed improved performance, scalability, and user experience as it expanded globally.

Their outdated codebase and limited search functionality made it difficult for users to navigate a growing number of design contests and international users.

We implemented Elasticsearch to enhance search speed and accuracy, supporting multiple languages and currencies for global reach. Additionally, we developed an AI-powered award suggestion system based on over 1,500 past contests. These updates improved user engagement and participation, helping Arcbazar double its competition and increase average award amounts.

Optimized media search for a creative stock marketplace

At MotionElements, a global marketplace for stock media, we transformed the backend by migrating to the Laravel framework and integrating Elasticsearch. With a vast library of videos, music, and digital assets, Elasticsearch allowed users to quickly find and filter through specific media content, significantly enhancing the search experience for filmmakers and digital artists. This update has made MotionElements a go-to platform for creative professionals worldwide, providing a fast, efficient way to search for high-quality stock media.

Enhanced search for a cosmetic ingredients marketplace

We integrated Algolia to resolve search-related issues, including filters, logic, and search results, for a marketplace selling cosmetic ingredients to businesses. Along with optimizing product management and improving the overall search experience, we addressed server access and rights issues, ensuring the client had full control and smooth operations across the platform. This upgrade significantly improved the user experience and search accuracy on the site.

Conclusion

Elasticsearch is a highly efficient, open-source search and analytics engine that allows websites and applications to quickly search through large volumes of data in real time. It's particularly useful for businesses that manage vast datasets, such as eCommerce platforms, news websites, or any content-driven system. The key advantage of Elasticsearch lies in its speed and scalability. It can handle both simple and complex queries, allowing users to search for everything from exact matches to fuzzy logic queries, which makes it incredibly flexible for various business needs. Additionally, it distributes data across multiple nodes, ensuring performance remains fast even as your data grows.

Ultimately, Elasticsearch strikes a balance between speed, flexibility, and scalability, which makes it an ideal choice for businesses that need robust search capabilities. If you're unsure which solution to choose, the Greenice team can guide you through every step of the process, helping you determine the right technology for your business and ensuring seamless integration tailored to your specific needs. With our expertise in custom web development and search technologies, we’ll ensure that your search engine performs optimally, scales with your growth, and delivers the best possible user experience.