Top 12 Elasticsearch Alternatives 2024

In today's fast-paced digital era, big and small businesses need to sift through vast amounts of data quickly and effortlessly. Elasticsearch is a leading solution in this domain, and it is renowned for transforming complex datasets into searchable, analyzable formats. However, there are other options available. Numerous other search platforms might be an even better match for your unique needs, offering perks like faster performance, greater customization, or smoother integration with your existing systems.

This article explores the top 12 alternatives to Elasticsearch, shedding light on lesser-known yet powerful search engines that can optimize your data retrieval and analysis processes.

Elasticsearch key features

Elasticsearch is a popular open-source distributed search and analytics engine and serves as the core component of the Elastic Stack. It centrally stores your data, enabling lightning-fast search, fine-tuned relevancy, and scalable analytics.

Elasticsearch performs well when used for:

• Log management
• Infrastructure monitoring
• Application performance monitoring (APM)
• Business analytics
• Full-text search
• Real-time data analytics

It also allows you to handle diverse data types, perform advanced analytics with built-in machine-learning features, and scale horizontally to accommodate growing data volumes.

Elasticsearch pros

• Easily scales horizontally and offers high availability through clustering and automatic data rebalancing
• Supports complex queries, aggregations, and real-time analytics for in-depth data insights
• Offers ingest nodes, processors, and integration with Beats and Logstash for efficient data ingestion
• Provides anomaly detection and forecasting to enhance proactive monitoring
• Supports deployments on-premises, on Elastic Cloud, or in Kubernetes environments
• Includes role-based access control, encrypted communications, and compliance with security standards

Elasticsearch Cons

• Has a steep learning curve and can be challenging due to its complexity
• Requires careful configuration and ongoing maintenance
• Potential security concerns if not properly secured
• Less efficient for workloads with frequent data updates or modifications

Elasticsearch Pricing

Elasticsearch is free for self-managed use. For cloud-managed services, plans start at $95/month with core features like security and observability. The $109/month gold plan adds advanced reporting and business hours support, while the $125/month platinum plan includes machine learning, anomaly detection, and 24/7 support. The $175/month enterprise plan offers AI-guided insights, OpenAI integrations, and premium support. Pricing varies by cloud provider and usage, with optional features like synthetic monitoring available.

Factors to consider when choosing an Elasticsearch alternative

Before exploring the best Elasticsearch alternatives, it's important to identify the key factors that will guide you to the right solution for your needs.

Feature set and functionality

A suitable alternative should offer core search and analytics features while addressing Elasticsearch's limitations. Look for advanced search options like faceted and full-text search, scalability for large datasets, real-time indexing, flexible schema management, and efficient query performance with relevance tuning and typo tolerance.

Performance and resource efficiency

The tool should deliver equal or better performance with optimized resource usage. Efficient indexing, minimal storage overhead, and fast queries can reduce costs without compromising speed or accuracy.

Ease of use

User-friendly tools with intuitive APIs, clear documentation, and straightforward setup simplify adoption. Whether you're starting fresh or migrating, ease of use ensures a smoother process.

Compatibility with your tech stack

Ensure the solution integrates well with your languages, frameworks, and systems for easy adoption without extensive adjustments.

Deployment flexibility

The tool should align with your deployment strategy, offering self-hosted, managed, or hybrid options for on-premises or cloud environments.

Integration with other tools

Look for integrations with log management, data visualization tools like Grafana, or communication platforms like Slack. This enhances productivity and collaboration.

Cost-effectiveness

Evaluate whether the tool’s features justify its cost, whether it's a free open-source solution or a paid managed service. Ensure it aligns with your budget and goals.

Community and support

A robust community or dedicated support can be invaluable when challenges arise. Open-source tools benefit from active contributors, while commercial solutions often include fast, reliable support.

By considering these factors, you can confidently choose an Elasticsearch alternative that aligns with your technical and operational needs.

1. Better Stack

Better Stack is an excellent Elasticsearch alternative, leveraging ClickHouse for lightning-fast log search and efficiently handling high-volume telemetry data. It uses SQL-like syntax for querying, simplifying access for a broader audience, and eliminates manual log formatting with structured JSON parsing.

Its custom pipeline reduces infrastructure costs, while continuous log access without cold storage simplifies management. Features like a query builder, pre-made dashboards, and real-time collaboration tools, including commenting and log sharing, make it a great choice.

🌟 Key features

• Log and metrics management
• OpenTelemetry-native support
• Incident management
• Website monitoring
• Infrastructure monitoring

➕ Pros

• Faster query speed with ClickHouse, up to 1 billion log lines/second
• SQL-based querying is more accessible than Elasticsearch's DSL
• Cost-efficient with transparent pricing, no need for cold storage
• Automatic JSON structuring simplifies data handling
• Built-in collaboration tools like commenting and shadowing
• Integrated logging, dashboards, alerting, and incident management
• Advanced anomaly detection with flexible alerting.
• Scalable for high log volumes with consistent performance.
• Pre-built dashboards and extensive integrations streamline setup.

➖ Cons

• Focused on observability, unlike Elasticsearch’s broader applications as a search engine.

💲 Pricing

Better Stack offers a free plan with ten monitors, email alerts, a status page, and basic log and metrics retention. Starting at $29/month, the pay-as-you-go plan includes AI incident silencing, private status pages, on-call scheduling, and Slack-based management for growing teams.

Pre-paid bundles and enterprise plans add further savings and advanced features like SLAs and role-based access control. With costs up to 10x lower than Datadog, Better Stack delivers affordable observability.

Check out the full pricing details on the Better Stack pricing page.

2. OpenSearch

OpenSearch, the community-driven alternative to Elasticsearch, was forked from Elasticsearch and Kibana, retaining compatibility with Elasticsearch APIs to ensure a painless transition without workflow disruptions.

It offers advanced search and analytics capabilities, including traditional lexical, vector, and hybrid search methods. OpenSearch Dashboards provide an intuitive interface for data exploration, visualization, and management, making it the closest experience to Elasticsearch.

🌟 Key features

• Asynchronous search
  
• Workflow automation
  
• Cross-cluster replication
  
• Index state management
  
• Performance evaluation
  
• Search methods
  
• Anomaly detection
  

➕ Pros

• Compatibility with Elasticsearch APIs for ingestion and querying
  
• Extensive plugin ecosystem supporting observability, security, and automation
  
• Fully open-source with a transparent community-driven development model
  
• Modular design, allowing easy customization for specific workflows
  
• Fully customizable dashboards through OpenSearch Dashboards
  

➖ Cons

• Fewer GitHub commits indicate slower updates compared to Elasticsearch
• Lacks premium features like role-based access control and document-level security
• High storage costs may force reduced retention periods.

💲 Pricing

OpenSearch is free under the Apache 2.0 license, with costs limited to hosting and maintenance for self-managed deployments.

Amazon OpenSearch Service offers managed hosting:

• Free tier: 750 hours of t2.small/t3.small instances and 10 GB EBS storage per month.
  
• On-demand: Pay for usage, e.g., $0.036/hour for t3.small.
  
• Reserved instances: Up to 52% discounts for 1- or 3-year commitments.
  
• Serverless: Pay only for resources used, with lower-cost dev-test modes.
  

3. OpenObserve

OpenObserve leverages Rust for high performance, handling petabyte-scale data efficiently with S3-backed storage for scalability. It reduces storage costs (~140x lower than Elasticsearch) through optimized data management and offers easy deployment via a single binary or high-availability setups.

Its intuitive GUI supports SQL and PromQL, bypassing the need for Elasticsearch’s DSL. OpenObserve combines logs, metrics, traces, dashboards, real user monitoring, error tracking, and session replay into a single, comprehensive observability platform.

🌟 Key features

• SQL and PromQL support
• Multilingual and accessible UI
• Real-time alerts and notifications
• Dashboards and visualizations
• Logs, metrics, and traces
• Built-in authentication and RBAC

➕ Pros

• Combines logs, metrics, traces, RUM, error tracking, and session replay in one platform, reducing the need for multiple tools
• Simpler deployment with a single binary for smaller setups and high-availability modes for large-scale installations
• Supports versatile storage options like S3, MinIO, GCS, and Azure Blob Storage
• Offers Elasticsearch API compatibility via the zPlane add-on, allowing you to continue using Elasticsearch tools and queries
• Built-in authentication, RBAC, and SAML authentication for secure enterprise use

➖ Cons

• OpenObserve community is smaller and less mature ecosystem compared to Elasticsearch
• OpenObserve does not match extensive customization options for complex queries and advanced use cases

💲 Pricing

OpenObserve is free to download and self-host. The developer plan is free for managed services, offering 200GB monthly ingestion, 15-day retention, and support for ten users. The pro plan ($19/month) includes unlimited users, multi-tenancy, and advanced features like log export. The business plan ($199/month) adds 30-day retention, RBAC, and email support. Enterprise plans offer custom pricing with SLAs, SAML, and single tenancy.

4. Apache Solr

Apache Solr is a flexible, open-source Elasticsearch alternative built on Apache Lucene. It offers advanced full-text search, near real-time indexing, and scalable performance with Apache Zookeeper.

Solr also supports JSON, XML, and CSV formats and multi-tenant setups, including auto-complete and spell-checking for enhanced user engagement. Its caching, distributed querying, and replication ensure reliability, making it a powerful tool for scalable, search-driven applications.

In addition, it offers features like faceted search, geospatial capabilities, and rich content parsing via Apache Tika cater to diverse use cases.

🌟 Key features

• Geospatial search
• Fault-tolerant scaling with Apache Zookeeper
• Near real-time indexing
• Faceted search and filtering
• Distributed indexing and replication
• Auto-complete, spell-checking, and clustering for discovery

➕ Pros

• Allows both schema and schemaless modes
• Excels in managing and indexing rich-text documents like PDFs, Microsoft Word files, and other structured documents, with built-in Apache Tika support
• Solr is governed by the Apache Software Foundation, enabling a collaborative open-source environment without dependence on a single company
• Offers powerful query parsing aligned with Lucene syntax, which allows for crafting highly customized and complex search queries.

➖ Cons

• Reliance on Zookeeper adds complexity compared to Elasticsearch’s efficient cluster management.
• Elasticsearch’s native JSON support outperforms Solr’s JSON Query DSL for real-time data.
• Needs manual cluster rebalancing, while Elasticsearch automates the process.

💲 Pricing

Apache Solr is open-source and free to download, use, and modify. You will only incur costs related to hosting, maintenance, and any infrastructure needed to run Solr, such as servers or cloud services.

5. Grafana Loki

Grafana Loki is a cost-effective alternative to Elasticsearch for log aggregation, indexing metadata instead of content to reduce storage costs. It unifies logs, metrics, and traces with Prometheus-inspired LogQL and integrates well with Grafana.

Loki scales efficiently with a Kubernetes-native design, multi-tenancy, and support for object storage like Amazon S3. Its simplicity, scalability, and deep Grafana integration make it ideal for modernizing observability while cutting costs.

🌟 Key features

• Metadata indexing using labels
  
• Support for object storage
  
• PromQL-inspired LogQL
  
• Multi-tenancy for isolated log streams
  
• Kubernetes-native deployments with decoupled read and write paths
  

➕ Pros

• Significantly reduces storage costs by indexing only metadata
  
• Integrates natively with Grafana for a complete observability stack
  
• Highly scalable and adaptable to cloud-native environments
  
• Offers a simple configuration process for object storage solutions
  
• Supports third-party log collectors for diverse workflows
  

➖ Cons

• Limited full-text search capabilities compared to Elasticsearch
  
• Handling high cardinality labels requires careful configuration to maintain performance
  
• Querying speeds can vary depending on label efficiency
  

💲 Pricing

Grafana Loki is an open-source solution that is free to use and modify and has no deployment restrictions. For managed deployments, Grafana Cloud offers flexible plans starting with a free tier with 50GB of logs, traces, and metrics. Additional costs depend on usage and storage configuration.

6. Meilisearch

Meilisearch is a developer-friendly alternative to Elasticsearch. It combines full-text and semantic search for highly relevant results. It ships with features like typo tolerance, filtering, sorting, and faceting, which suit both enterprise and customer-facing applications.

Moreover, Meilisearch offers a RESTful API and integrates well with React, Python, and Ruby frameworks. Advanced capabilities like geosearch, vector storage, and multi-tenancy enhance its versatility. The cloud version simplifies scaling and monitoring, making it ideal for dynamic workloads.

🌟 Key features

• Hybrid search with semantic and full-text capabilities
  
• Typo tolerance for better match accuracy
  
• Filtering, sorting, and faceted search
  
• Geosearch and vector storage
  
• Multi-tenancy for personalized search
  
• RESTful API and broad SDK support
  

➕ Pros

• Effortless deployment with plug-and-play setup
  
• Highly customizable with smart presets for instant results
  
• Advanced language support optimized for global use cases
  
• Scalability and operational ease with Meilisearch Cloud
  

➖ Cons

• Limited fault tolerance and high availability, as it supports only single-node setups.
• Performance depends heavily on having the entire dataset in RAM.
• Advanced capabilities like recommendations, vector search, and A/B testing are not supported.
• Lacks robust clustering and production-grade features for larger-scale deployments.
• Collects some telemetry data by default, though opt-out options are available.

💲 Pricing

Meilisearch offers a free, open-source version for flexible, self-managed deployments, while its cloud plans start at $30/month. Cloud options provide added benefits, including analytics, automated scaling, and premium support, making it a scalable choice for projects of any size.

7. Quickwit

Quickwit is a cloud-native search engine for large-scale datasets. Its decoupled compute and storage model leverages cost-effective solutions like Amazon S3, enabling sub-second latency for direct queries on raw object storage.

Unlike Elasticsearch, Quickwit offers a lightweight framework with schemaless indexing and compatibility with OpenTelemetry and Jaeger, simplifying log management and tracing. Indexing metadata reduces storage costs and operational complexity while delivering real-time insights.

🌟 Key features

• Object storage support
• Schemaless indexing
• Multi-tenancy support
• GDPR-ready retention policies and targeted deletions
  

➕ Pros

• Built for cloud-native environments with smooth S3 integration
  
• Reduces storage costs with efficient object storage utilization
  
• Real-time performance for massive datasets
  
• Stateless design simplifies scaling and deployment
  
• High performance from Rust-based architecture with vectorized processing
  

➖ Cons

• Limited built-in analytics or visualization tools
  
• Focused on logs and traces, less suited for broader document-based searches
  

💲 Pricing

Quickwit is free and open-source, offering an affordable, high-performance solution for large-scale log and trace management. Its architecture directly reduces infrastructure costs by leveraging object storage, while its lightweight, stateless deployment keeps operational overhead low.

8. Typesense

Typesense is a fast, simple alternative to Elasticsearch, delivering sub-50ms response times with a lightweight, self-contained binary. It supports typo tolerance, faceting, dynamic sorting, and semantic search, making it ideal for diverse applications like e-commerce and geospatial search.

Typesense is scalable and secure with Raft-based clustering and scoped API keys and offers predictable managed cloud pricing, combining power with ease of use.

🌟 Key features

• Low-latency search
• Typo tolerance and intelligent ranking
• Dynamic sorting, faceting, and grouping
• Semantic and vector search capabilities
• Multi-tenant API keys
• Geospatial search with filtering and bounding box options

➕ Pros

• Simple setup and minimal resource requirements
• Supports flexible ranking and real-time query adjustments
• Advanced capabilities like hybrid and multimodal search
• Predictable open-source pricing with no hidden limits on usage
• Robust support for developer tools and integrations
• Intuitive RESTful API and language SDKs

➖ Cons

• Requires the entire dataset to fit in RAM for optimal performance, limiting its use for large datasets
• Does not support out-of-the-box personalization or server-based search analytics
• Advanced features like recommendations and A/B testing require custom implementations
• Fewer built-in integrations compared to longer-established competitors like Elasticsearch

💲 Pricing

Typesense is open-source and free to use on your infrastructure, with no restrictions on records or queries. Its managed cloud service offers predictable hourly pricing based on cluster configuration.

9. ZincSearch

ZincSearch is a lightweight, efficient open-source alternative, offering single-binary deployment and schema-less indexing for diverse datasets. Its Vue-based web UI enables easy data querying and management.

It supports full-text indexing, aggregation, and highlights, handling hundreds of gigabytes efficiently. ZincSearch is ideal for small to medium datasets, excelling in simple, streamlined search cases.

🌟 Key features

• Schema-less mode
• Full-text indexing
• Aggregation support
• Highlight support
• Compatibility with Elasticsearch DSL

➕ Pros

• Lightweight architecture requires minimal resources
• Schema-less design simplifies indexing diverse datasets
• Elasticsearch API compatibility ensures easy migration
• Open-source and cost-effective, with no licensing fees
• Built-in authentication for secure access
• Single binary, which simplifies deployment

➕ Pros

• Not optimized for large-scale distributed environments
• Lacks advanced analytics and aggregation features

💲 Pricing

ZincSearch is free and open-source, allowing you to deploy and operate it without licensing costs. Like other tools we have seen, costs are limited to infrastructure, hosting, and maintenance.

10. Manticore Search

Manticore Search is a high-performance Elasticsearch alternative built for speed, efficiency, and versatility. Forked from Sphinx, it features a SQL-first approach and MySQL protocol compatibility, making integration seamless for teams familiar with relational databases.

Its multithreaded architecture and query parallelization deliver fast responses, while flexible storage options—row-wise, columnar, or docstore—adapt to datasets of all sizes. Manticore supports advanced full-text search with over 20 operators, vector search for semantic matching, and geo-spatial queries, catering to diverse search needs.

🌟 Key features

• Full-text search
• Vector search
• Faceted search and geo-spatial filtering
• SQL-first syntax with MySQL compatibility
• Multithreaded architecture
• Row-wise and columnar storage
• Real-time data insertion and retrieval
• Built-in replication and load balancing

➕ Pros

• SQL-native syntax simplifies integration with relational databases
• Outperforms Elasticsearch in speed and resource efficiency for many workloads
• Supports a wide range of data formats, including JSON, XML, and CSV
• Lightweight, with no heavy dependencies, and quick setup via Docker or native binaries

➖ Cons

• Not fully ACID-compliant, though transactions and binlogs provide data safety
• May require additional effort for niche integrations compared to Elasticsearch's broader ecosystem
• Less mature community size compared to Elasticsearch

💲 Pricing

Manticore Search is fully open-source and free to use on any infrastructure, with no restrictions on records or queries. You will need to pay for hosting or maintenance.

11. Apache Lucene

Apache Lucene is a high-performance, full-featured search library and a solid alternative to Elasticsearch. Written in Java, it powers search engines like Elasticsearch and Solr with efficient indexing, capable of handling massive datasets at speeds over 800GB per hour. Its compact index size reduces storage needs while maintaining excellent search quality.

Lucene supports advanced features like ranked searches, phrase queries, wildcard capabilities, and vector-based analytics, making it ideal for applications like semantic search and recommendation systems. Its modular design allows you to fine-tune ranking models, integrate typo-tolerant suggesters, and customize query logic.

🌟 Key features

• High-speed indexing
• Full-text search capabilities
• Scalable nearest-neighbor search
• Faceting, joins, and result grouping
• Compact index size, reducing storage costs
• Incremental and batch indexing

➕ Pros

• Fully customizable ranking and search models
• Transparent and cost-effective due to its open-source nature
• Cross-platform compatibility with implementations in multiple programming languages
• Modular design ensures flexibility for diverse use cases

➖ Cons

• Requires advanced technical expertise for integration and maintenance
• Lack of a built-in administrative UI for monitoring and management
• Lacks pre-packaged distributed cluster management, relying on custom implementations

💲 Pricing

Apache Lucene is entirely open-source and free, with no licensing fees, allowing you to focus your budgets solely on hosting, infrastructure, and maintenance.

12. Sonic

Sonic is a lightweight, schema-less alternative to Elasticsearch, allowing flexible indexing and search for unstructured data. Acting as an identifier index, it links object IDs to external databases, ideal for use cases like messaging systems or helpdesks. Sonic optimizes disk usage, saving 30-40% in storage, and supports near real-time indexing with batch updates for fast search result updates.

🌟 Key features

• Identifier-based indexing
• Real-time indexing and search
• Compact storage
• Multi-language support
• Parallel processing
• Error-tolerant searches

➕ Pros

• Consumes minimal resources, requiring only ~30MB of RAM
• Supports near real-time indexing and search operations
• Schema-less architecture simplifies setup and indexing for unstructured or dynamic data
• Support for over 80 languages and stop-word removal
• Multiple deployment options, including pre-built binaries, Docker images, and Rust builds,

➖ Cons

• Uses 32-bit IDs, supporting up to 4.2 billion objects per bucket.
• Retains only the most recent results per word in a configurable sliding window.
• Operates at the word level, lacking sentence-level NLP capabilities for next-word predictions.
• Batches index rebuilds, causing delays before new entries are fully available unless manually triggered.
• Limited to the Sonic Channel protocol, with no HTTP API, requiring custom implementations for unsupported languages.
• Performance depends on SSD-backed storage; HDDs are significantly slower due to disk-based queries.

💲 Pricing

Sonic is free and open-source, making it a lightweight yet powerful alternative to Elasticsearch without the costs. Its minimal resource requirements, like consuming just ~30MB of RAM, help keep operational expenses low.

Final thoughts

In the evolving world of search and analytics, finding the right tool for your needs is essential. While Elasticsearch is a solid option, the alternatives we’ve explored provide fresh approaches to today’s challenges.

If you’re unsure which tool is right for you, consider testing them gradually to see which best fits your setup and requirements. Taking the time to experiment can help you find the perfect solution.

Article by

Stanley Ulili

Stanley is a freelance web developer and researcher from Malawi. He loves learning new things and writing about them to understand and solidify concepts. He hopes that by sharing his experience, others can learn something from them too!

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