Algolia vs Elasticsearch
Compare the tactical differences between Algolia and Elasticsearch to see which approach is best for meeting your needs.
| Algolia | Elasticsearch | |
|---|---|---|
Overview While both companies are industry leaders in search, they offer dramatically different playbooks. One key difference is that Algolia is a Cloud-native, purpose-built, managed service that increases developer productivity 5x vs. Elastic’s software that can be hosted in the Cloud. | Cloud native: scalable, agile, and easy to roll out Simplifies every step of the search development process, including indexing, relevance tuning, building user interfaces, and analyzing search trends Designed specifically to create great consumer experiences | Powerful software built primarily for power users focused on log and end-point search use cases Requires more back-end search expertise and development time to handle speed, scalability, and global needs Very flexible, but more parts of the search stack must be coded by developers |
Search Indexing How you build your data architecture impacts how quickly users can search through it to find content. Companies need to keep the user experience at the forefront, and that means creating a simple, accessible infrastructure for current and future success. | Worry free, low maintenance, and delivers performance to scale Better organizes large data volumes (for example, product SKUs) Easy configurability helps teams fine-tune for a better search experience | More complex approach to index management Requires advanced planning, expertise, and optimization Clients must already know their search needs and how they’ll evolve |
Search Relevance Align search results with user behavior and business objectives. Search results need to accurately reflect what users are seeking; Digital, web and IT teams need the ability to adjust their tools, which enables greater customization - to achieve optimal user and business outcomes. | Built on transparent search rules that are simple and easy to manage Utilizes business insights to improve accuracy of search results order Integrates with AI to help engineers fine-tune search performance | Algorithms are more complex, unpredictable, and harder to control Optimizing one set of search results may hurt others, potentially cutting into revenue It’s hard to see where changes make the biggest impact |
AI Elasticsearch Relevance Engine (ESRE) is a collection of tools for adding vector search capabilities. Like Elasticsearch more generally, ESRE requires extensive engineering resources. By contrast, Algolia Neural Search works immediately out of the box. | AI-native Neural Search with end-to-end AI processing with both keywords and vectors ensuring the most relevant results at scale Algolia manages AI algorithms to ensure performance even as the underlying index and content changes While Elastic has vector solutions, nobody except Algolia has been able to do this at scale while keeping the costs at keyword level for realistic enterprise-wide applications as well as complexity levels to a minimum for broader adoption | A new out-of-the-box encoder model, full vector database capabilities, bring-your-own-model capabilities Reciprocal Rank Fusion (RRF) is an algorithm which can be added to combine keyword and vector search results, but which requires customers to determine correct blending of weights The full ESRE suite is available via Elastic’s Platinum or Enterprise licenses |
Front end Building out search tools comes down to one goal: create search with customers in mind. Teams that prioritize their own convenience over the user experience will feel it in the pocketbook. | Comes prebuilt with 6 rich UI libraries Fully customizable and features built-in security Delivers a consumer-grade search experience | Offers only one basic UI library Requires investment in an additional layer of security Limited UX features limit analytical insights and opportunity for optimization |
Personalization Providing curated results to deliver more individualized experiences is critical for engagement. Customers expect access to relevant content faster than ever, and that means using the right tools to proactively filter out what they’re not looking for. | Leverages AI to create a more personalized user experience Built-in integrations with Relevance and Analytics Instantly implements changes up to 100x faster than alternatives | Build-it-yourself model requires extensive coding Implementation is always reliant on development team speed and search expertise May take weeks for developers to build sophisticated tools |
Analytics Collecting mountains of data doesn’t help if you don’t have a plan for how to analyze, visualize and optimize based on the usage patterns. Take the essential step to maximize search efficacy based on user behavior metrics. | Bolsters search experience with preloaded user and performance insights Leverages KPIs to optimize search and discovery Surfaces opportunities to improve the search experience | Requires data engineering to extract information and build visualizations Success predicated on do-it-yourself reporting tools No business insights provided; additional tools needed |
Search that understands
More than 17,000 customers in 150+ countries selected Algolia to grow customer satisfaction and sales. Because when your customers feel understood, they click and they come back.
“Algolia is a breeze to work with. With Algolia, our editorial team has seen significant productivity improvements when building the daily online edition of The Times and weekly edition of The Sunday Times, with search being 300-500 times faster than our prior solution.”
Matt Taylor
Editorial Product Manager @ The Times
“Algolia is very fast — able to keep up with our level of traffic… The API and SDK options are really great, and the ability to handle traffic at scale (we have a high volume)”
Matt Goorley
Engineering Manager @ LTK
“[Algolia] was very professional from the start. We had a great Customer Success Manager and team that provided a lot of help and was a great partner.”
Clint Fischerström
Head of Ecommerce @ Swedol
“I think we’ve grown leaps and bounds with Algolia. There's a lot of features that we still can tap into, which is great because I feel like we've gotten a ton out of it already.”
Geoff Lyman
Digital Experience Solutions Manager @ Hershey's
“Instead of having to go into the back end and the catalog—which would have been a technical headache—we were able to figure it out in a matter of a day, test it, and ‘boom’ it’s live.”
Courtney Grisham
Director of E-Commerce @ Shoe Carnival
