7 Elasticsearch Alternative Search Solutions for Your Startup

Search functionality is key to the success and usability of any modern application, as users want to find relevant information quickly and efficiently. Over time, Elasticsearch has become the go-to search solution for many organizations. However, despite its popularity and broad feature set, it may not be the best option for startups due to its complexity, cost, and steep learning curve. No wonder startups are interested in more agile Elasticsearch alternatives.

In this article, you will explore why startups should look into Elasticsearch alternatives for their search solution. Then, you will see the key aspects to consider when comparing search solutions and apply them to the top 7 alternative search services for startups.

Let’s dive in!

Why Elasticsearch May Not Be the Best Search Solution for a Startup

Elasticsearch is the most popular distributed, RESTful search and analytics engine. It falls into the category of search solutions, which provide powerful full-text and vector search capabilities over large datasets, along with features for logs, metrics, and APM (Application Performance Monitoring).

The end goal is to enable users to quickly find relevant information, even with typos in their search queries. Admins can then analyze search data to find patterns, make decisions, and improve business processes.

Integrating an Elasticsearch-powered search feature in your application results in improved user experience and efficiency. Despite these benefits, Elasticsearch may not be the best choice for startups for three key reasons:

• High costs: Handling an Elasticsearch architecture on platforms like AWS can become increasingly costly as data volumes grow.
• Too many features: While beneficial for large enterprises, the extensive feature set of Elasticsearch can be overwhelming and unnecessary for smaller startups.
• Difficult setup and steep learning curve: Setting up Elasticsearch and getting the best out of it requires significant expertise and time.

Startups generally need affordable, simple, and flexible solutions that are easier to set up and maintain. The technology stack should evolve quickly alongside the core business, without the burden of complex infrastructure management. This is why they love to explore some Elasticsearch alternatives.

Aspects to Consider When Evaluating Search Services

Now, assume you are a startup looking for a good Elasticsearch alternative. These are the main elements to keep in mind when comparing search solutions:

• Type: Is it open-source, proprietary, self-hosted, or a cloud service?
• Search types: What are the underlying algorithms? (e.g., vector search, geo search, nearest neighbor search, etc.)
• Features: The list of the main features provided by the search service.
• Pros and cons: The advantages and drawbacks of the solution.
• API clients: The available API client libraries (and programming languages) to interact with the search engine and simplify data retrieval.
• Popularity: Useful statistics about the solution, such as the review score, number of GitHub stars, downloads, installations, users, and more.
• Pricing: The pricing model of the solution, in case of a premium service.

Top 7 Elasticsearch Alternatives for Startups

Let’s use the criteria presented above to list and compare the best Elasticsearch alternatives for startups.

If you are eager to find out what these solutions are and what they offer, take a look at the table below:

FeatureAlgoliaTypesenseZincSearchOpenObserveOpenSearchVespaGitHub stars — 18.6k16.7k10.2k9k5.4kFirst public commit201220152021202320142016Programming languageC++C++GoRustJavaJava, C++Source codeProprietaryOpen sourceOpen sourceOpen sourceOpen sourceOpen sourceDeploymentCloudSelf-hosted/CloudSelf-hostedSelf-hosted/CloudSelf-hosted/CloudSelf-hosted/CloudPricingPremiumFree/PremiumFreeFree/PremiumFreeFree/PremiumSearch typesHybrid, semantic, and moreVector, semantic, and moreFull-textLogFuzzy, wildcard, and moreFuzzy, vector, and moreAPI clientsPHP, JS, Ruby, Python, iOS, Android, C#, Java, Go, Scala, Rails, Symfony, Django, Laravel, Kotlin, Elixir, ColdFusion, Erlang, .NETJavaScript, PHP, Python, Ruby, Go, .NET, Java, Rust, Dart, Perl, Swift, ClojureGo, Python, JavaScript — Python, Java, JavaScript, Go, Ruby, PHP, .NET, RustPython, Java

Time to explore the seven best startup-oriented search solutions!

Algolia is a cloud-based, hosted search engine that provides real-time search results through a comprehensive API interface. It consists of two main components:

1. Search Implementation: Enables developers to create and maintain excellent search experiences for users.
2. Search Analytics: Empowers business teams to analyze and optimize these experiences to meet evolving business objectives.

Algolia is an end-to-end AI search and discovery platform that offers fast and reliable search capabilities in more than 68 languages. Its cloud-native nature makes it more agile and easier to roll out than Elasticsearch. This is especially important for startups, which are always looking for quick setups and integrations.

Explore the official site to find out why Algolia is a good Elasticsearch alternative.

📚 Type: End-to-end AI search and discovery platform

🔎 Search types: Hybrid keyword and vector search, semantic search, faceted search, and geo search via NeuralSearch

🛠️ Features:

• Support for over 68 languages
• Indexing, auto complete, and stemming
• Configurable typo tolerance
• Highlighted results to show which section and words matched user queries
• Advanced language processing, like options for plurals handling, stop words, optional words, and prefixing behavior
• Synonym handling
• Support for location-based queries with geo-aware search results
• Multiple sorting strategies
• Grouping and deduplication of records
• Advanced personalization capabilities so that different users get different results for the same queries
• Search analytics available in a visual dashboard
• AI recommendations

👍 Pros:

• Average response time of 1 to 20 milliseconds for query processing
• Over 100 releases per year
• 99.999% uptime guarantee via SLA
• Dozens of community integrations with CMS and e-commerce platforms

👎 Cons:

• Proprietary search engine with vendor lock-in
• Can become expensive, especially on projects with high search volumes and large datasets

🔗 API clients:

• Official API clients for PHP, JavaScript, Ruby, Python, iOS, Android, C#, Java, Go, Scala, Rails, Symfony, Django, Laravel, Kotlin
• Community API clients for Elixir, ColdFusion, Erlang, .NET

📊 Popularity:

• 17k paying customers
• 1.7+ trillion searches every year
• 500k+ developers using Algolia
• 30 billion indexed records
• 4.4/5 on Gartner Peer Review
• 4.5/5 on G2
• 4.7/5 on Capterra

💰 Pricing:

• Build:
• Free plan
• Up to 10k search requests/month, with 1m records included
• Grow:
• Free/pay-as-you-go plan
• Free up to 10k search requests/month, then $0.50 per additional 1k search requests
• Free up to 100k records, then $0.40 per additional 1k records/month
• Premium:
• Search experience with some AI features
• Contact sales for pricing
• Elevate:
• Full AI search offering
• Contact sales for pricing

Typesense

Typesense is a typo-tolerant search engine optimized for instant search-as-you-type experiences. It serves as an open source alternative to Algolia — addressing some of its key issues — and represents a batteries-included alternative to ElasticSearch.

The team designed every feature from the ground up, with a close eye on making it intuitive and easy to use. This makes Typesense ideal for startups seeking an easy-to-integrate, quick-to-setup, and fast search solution.

Discover how Typesense compares to alternatives.

📚 Type: Open source, typo-tolerant search engine

🔎 Search types: Vector search, semantic search, geo search, fuzzy search, conversational search, image and voice search

🛠️ Features:

• Typo tolerance
• Meticulously architected from the ground up for low-latency instant searches.
• Support for dynamic sort results based on a particular field at query time
• Advanced faceting and filtering capabilities
• Support for grouping results and getting distinct records
• Support for federated search across multiple collections in a single HTTP request
• Ability to search and sort by results around a latitude/longitude or within a bounding box
• Ability to send questions and receive responses with fully-formed sentences, as with ChatGPT, but over your own data.
• Possibility to search through images using text descriptions
• Capture and send queries via voice recordings
• Connect one or more collections via common reference fields and join them during query time.
• Synonym handling
• Curation and merchandizing to boost particular records to a fixed position in the search results

👍 Pros:

• Batteries-included developer experience with a 30-second setup
• Several examples with code covering different real-world use cases
• Integration with Firebase, AWS DynamoDB, MongoDB, Supabase, Laravel, WordPress, Magento, and popular documentation technologies
• In-depth guides to simplify migration from Algolia

👎 Cons:

• No out-of-the-box user-level search personalization
• Implementing search recommendations requires custom implementation

🔗 API clients:

• Official API clients in JavaScript, PHP, Python, Ruby
• Community API clients in Go, .NET, Java, Rust, Dart, Perl, Swift, Clojure

📊 Popularity:

• 18.6k stars on GitHub (41 committers)
• 14 million docker pulls
• 5 billion cloud searches per month
• 5/5 on G2

💰 Pricing:

• Open Source (self-hosted): Free via a GPL license
• Typesense Cloud: Prices change based on memory and CPU resources

ZincSearch

ZincSearch is an open source and lightweight alternative to Elasticsearch. It provides a search engine written in Go that requires low resources and is designed for full-text indexing. Under the hood, it relies on bluge as the underlying indexing library.

ZincSearch represents a free drop-in replacement for Elasticsearch if you are searching using Kibana and ingesting data using Ingest APIs. Note that Kibana is not supported with ZincSearch, as this provides its own basic UI for data exploration.

📚 Type: Open source search engine for full-text indexing

🔎 Search types: App search

🛠️ Features:

• Full-text indexing capabilities
• Web UI for querying data written in Vue.js
• Compatible with Elasticsearch APIs for data ingestion
• Out of the box authentication system
• Schemaless data representation
• Index storage in disk
• Support for data aggregation

👍 Pros:

• Easy installation through binary files for multiple platforms
• Docker image for quick setup
• Complete configuration in about 2 minutes
• Simple, developer-oriented, lightweight tool

👎 Cons:

• Not designed for log search
• No high availability architecture

🔗 API clients:

• Official API clients in Go, Python, JavaScript

📊 Popularity:

• 16.7k stars on GitHub (57 committers)

💰 Pricing: Free

OpenObserve

OpenObserve is an open source, cloud-native log search, infrastructure monitoring, and APM solution. It is designed to handle logs, metrics, traces, and analytics, offering full compatibility with the OpenTelemetry standard.

You can think of OpenObserve as a mix of Prometheus for metrics, Elasticsearch for logs, Jaeger for traces, and Grafana for dashboards. Thanks to its compatibility with several storage services, it can significantly reduce storage costs — promising to lower them to about 140 times less than Elasticsearch.

Check out the docs to see why OpenObserve is a good alternative to Elasticsearch.

📚 Type: Open source and cloud-native observability platform built specifically for logs, metrics, traces, and analytics

🔎 Search types: Log search

🛠️ Features:

• Support for storing and exploitation of logs, metrics, and traces
• Full compatibility with the OpenTelemetry standard
• Real User Monitoring (RUM) via performance tracking, error logging, and session replay
• Over 14 different chart types for comprehensive data visualization
• Advanced ingest and query functions for data redaction, log reduction, and compliance
• Support for querying logs and traces via SQL and metrics via SQL and PromQL
• Advanced embedded GUI with an intuitive interface
• Versatile storage options, including local disk, S3, MinIO, GCS, Azure Blob Storage
• High availability and clustering to ensure reliable and scalable performance
• Dynamic schema to adapt your data structure seamlessly
• Built-in authentication with RBAC privilege system
• UI available in 11 languages, including English, Spanish, German, French, Chinese, and more

👍 Pros:

• Open source
• Docker image available for easy setup
• First configuration in less than 2 minutes
• Integration with Slack, Amazon S3, Microsoft Teams, Google Cloud Storage, and Minio

👎 Cons:

• Not so easy to integrate with other technologies
• Limited to log search

🔗 API clients: None

📊 Popularity:

• 10.2k stars on GitHub (53 committers)

💰 Pricing:

• Open Source (self-hosted): Free via an AGPL license
• Open Source (self-hosted): Paid via an enterprise license
• OpenObserve Cloud: 4 different plans, from a free plan for developers to a plan dedicated to enterprises

OpenSearch

OpenSearch is an open source software suite for search, analysis, and observability. Forked from Elasticsearch 7.10, it maintains backward compatibility with Elasticsearch’s REST API, query syntax, and responses. That makes it the most straightforward drop-in solution among the Elasticsearch alternatives in this list.

OpenSearch allows users to seamlessly ingest, protect, search, aggregate, visualize, and analyze data, just as in Elasticsearch, but with all the benefits of open source. Its community nature and origins make it ideal for startups, as you will find many users to learn and share lessons learned with.

Read more about why OpenSearch is not Elasticsearch.

📚 Type: Open source search and analytics suite

🔎 Search types: Fuzzy search, wildcard search, geo search, vector search, neural search

🛠️ Features:

• Application and infrastructure monitoring with log data storage and automated alerts
• Centralized logs for real-time security monitoring and forensic analysis
• Observability capabilities through logs, metrics, and traces for real-time application and business monitoring
• Anomaly detection to identify and notify about atypical data
• KNN (K-nearest neighbors) for finding nearest neighbors in vector data
• Performance analyzer to monitor and optimize cluster performance
• SQL and piped processing language support for querying data
• Advanced indexing capabilities
• Asynchronous search for running background search requests
• Cross-cluster replication for high availability and disaster recovery
• Extensible architecture with dozens of plugins available

👍 Pros:

• Community-oriented project with a lot of active users on GitHub and Slack
• Easy setup via Docker
• Highly fast and scalable
• OpenSearchCon to meet the community and see the search suite in action

👎 Cons:

• Can be complex to be configured for optimal performance
• Same learning curve as Elasticsearch

🔗 API clients:

• Official API clients in Python, Java, JavaScript, Go, Ruby, PHP, .NET, Rust, Rust, Hadoop

📊 Popularity:

• 9k stars on GitHub (302 committers)
• 4.2/5 on G2

💰 Pricing: Free

Vespa

Vespa is an open source search engine and vector database. Originally launched as a pioneering search engine in 1997, it became part of Yahoo through the Overture deal in 2003 and was open sourced in 2017.

Vespa supports vector search, lexical search, and search in structured data, all within the same query. Its integrated machine-learned model inference enables real-time application of AI, making it great for startups needing to quickly derive insights from data.

Delve into the relationship between Vespa, Elasticsearch, and Solr.

📚 Type: Open source search engine and vector database

🔎 Search types: Fuzzy search, vector search, geo search, lexical search, streaming search

🛠️ Features:

• Ability to add, modify, and remove documents with durable write acknowledgments in milliseconds
• Automatic data corruption repair from uncorrupted replicas
• Simple HTTP/2 API for writing and retrieving data
• Response times typically in tens of milliseconds
• Rich data schemas that allow various primitive types, collections, structs, and tensors
• Referencing and querying fields from referenced documents can be done without performance penalties
• Queries support with structured filters, free text, and vector search operators
• Streaming search mode for low-latency access to billions of documents per node
• Result ranking via configurable functions, including machine-learned models and ONNX integration
• Indexing with linguistic transformation
• Support for multiple ranking phases and advanced text ranking features
• Support for nested groupings and data aggregation
• Support for multiple clusters of any size with fault tolerance and automatic node routing
• Tokenization and stemming in English, German, French, Spanish, and Italian

👍 Pros:

• Easy installation via RPM files, Docker image, or deployment in AWS
• Application configuration specified in a single application package for simplified deployment
• Multi-tenant architecture with no single points of failure
• Real-time data ingestion, processing, and streaming

👎 Cons:

• Confusing documentation
• Only a few languages fully supported, limiting the company’s growth in international markets

🔗 API clients:

• Official API clients in Python and Java

📊 Popularity:

• 5.4k stars on GitHub (79 committers)

💰 Pricing:

• Open Source (self-hosted): Free via an Apache 2.0 license
• Vespa Cloud: 3 different plans, from a basic plan for individuals to a plan dedicated to enterprises

Solr

Solr is an enterprise search platform built on Apache Lucene, an open source, Java-based, information retrieval library. It supports advanced full-text search with advanced filtering capabilities. Solr is suitable for startups that need to handle scalable volumes of traffic.

It is based on open standards and is designed to drive powerful document retrieval applications. In particular, it responds with structured documents to simple HTTP requests.

📚 Type: Open source enterprise search platform built on Apache Lucene

🔎 Search types: Wildcard search, spatial search, dense vector search, fuzzy search, proximity search, range search.

🛠️ Features:

• Documents can be added via JSON, XML, CSV, or binary over HTTP
• Querying via HTTP with results in JSON, XML, CSV, or binary
• Advanced full-text search capabilities
• Comprehensive built-in admin UI
• Support for near real-time indexing for quick updates
• Extensible architecture that supports plugins for custom functionalities
• High schema flexibility with both schemaless and configured schema modes
• Powerful faceted search and filtering capabilities.
• Built-in support for geospatial search for location-based queries
• Advanced configurable text analysis in several languages

👍 Pros:

• Standalone search server with a REST-like API interface
• Based on open interfaces like XML, JSON, and HTTP
• Ideal for handling high-volume traffic at large scales
• High performance and scalability

👎 Cons:

• Hard-to-explore documentation that can be overwhelming for new users
• Confusing query language

🔗 API clients:

• Official API client library in Java
• 3rd-party API clients in Python, Ruby, PHP, C#/.NET, Scala, Perl, JavaScript, Clojure, Go, Rust, R, C++

📊 Popularity:

• 1.1k stars on GitHub (387 committers)
• 4.1/5 on G2
• 4.3/5 on Capterra

💰 Pricing: Free

Conclusion

In this guide, you learned why Elasticsearch may not be the best search solution for a startup. Fortunately, there are more affordable, open source, and easier-to-configure technologies for searching data. Here, you explored the seven best Elasticsearch alternatives with quick setups and low resource usage. As a startup, knowing these affordable solutions can help you save a lot of time and money!

Originally published at https://semaphoreci.com on July 31, 2024.