Build vs. Buy: Key learnings from tech leaders of America's top online marketplaces
Learn about the common challenges of retail marketplaces and how tech leaders make technical decisions when it comes to building on their platform.
Algolia VS Elasticsearch
Compare the tactical differences between Algolia and Elasticsearch to see which approach is best for meeting your needs.
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
Elasticsearch
- 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
- Flexible use cases, but more 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
Elasticsearch
- 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
Elasticsearch
- 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
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
Elasticsearch
- 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
Elasticsearch
- 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
Elasticsearch
- Requires data engineering to extract information and build visualizations
- Success predicated on do-it-yourself reporting tools
- No business insights provided; additional tools needed
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
Elasticsearch
- 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
- Flexible use cases, but more of the search stack must be coded by developers
- 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
Elasticsearch
- More complex approach to index management
- Requires advanced planning, expertise, and optimization
- Clients must already know their search needs and how they’ll evolve
- 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
Elasticsearch
- 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
- Comes prebuilt with 6 rich UI libraries
- Fully customizable and features built-in security
- Delivers a consumer-grade search experience
Elasticsearch
- Offers only one basic UI library
- Requires investment in an additional layer of security
- Limited UX features limit analytical insights and opportunity for optimization
- 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
Elasticsearch
- 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
- Bolsters search experience with preloaded user and performance insights
- Leverages KPIs to optimize search and discovery
- Surfaces opportunities to improve the search experience
Elasticsearch
- Requires data engineering to extract information and build visualizations
- Success predicated on do-it-yourself reporting tools
- No business insights provided; additional tools needed
Learn a CTO’s perspective on Algolia vs. Elasticsearch.
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