Switching from Algolia to Typesense can be a strategic move, but it requires careful planning. Whether you're aiming to reduce costs, improve customization, or gain more control over your search infrastructure, this guide will help you navigate the migration process smoothly.
Why Consider Migrating?
Here are some common reasons why teams switch from Algolia to Typesense:
Cost Savings
Typesense offers a self-hosted option, which can significantly lower costs for projects with high search query volumes.
Self-Hosting & Data Privacy
If your organization requires complete control over infrastructure and data privacy, Typesense’s self-hosting capabilities are a major advantage.
Custom Features
Typesense allows for more flexible search tuning and customization.
Performance Optimization
With built-in optimizations, Typesense can provide better control over sorting, faceting, and filtering.
Key Migration Steps
Migrating from Algolia to Typesense requires a structured approach to ensure a smooth transition. The official Typesense migration guide provides a step-by-step process, covering key differences in API structure, indexing, search queries, and feature implementations. Based on our experience, the main changes to account for include adapting to a typed schema, adjusting filtering and sorting logic, manually handling hierarchical facets, and ensuring autocomplete relevance. By following these steps and making necessary refinements, you can migrate without major disruptions to search functionality.
From our experience, the estimated migration times mentioned in the official Typesense documentation are accurate. We were able to migrate a complex site fully implemented in Algolia within 4 to 5 weeks, while a simpler site took about 2 weeks to complete.
1. Schema Handling & Data Structure
One of the biggest differences between Algolia and Typesense is how they handle data schemas. Algolia allows schema-less indexing, meaning you can push data with varying structures dynamically. This makes it easier to iterate on your data structure without reindexing. However, it can also lead to inconsistencies if data is not standardized.
Typesense, on the other hand, enforces a typed schema, meaning that fields must be explicitly defined before indexing. This approach offers several advantages:
Stronger Data Integrity
Enforces consistency by ensuring all indexed documents conform to a predefined structure.
Optimized Search Performance
Knowing the data types in advance allows for faster queries and efficient indexing.
Better Query Validation
Prevents malformed data from being indexed, reducing potential search errors.
While a strict schema improves stability and performance, it also requires more upfront planning. Any changes to the schema require reindexing, so it’s important to finalize your field definitions early in the migration process.
✅ Action Step
Start by reviewing your current data structure and mapping it to a Typesense schema. If you need flexibility, consider using optional fields instead of relying on a schema-less approach. Refer to the Typesense schema documentation to define your collections effectively.
2. Sorting & Filtering Adjustments
Sorting and filtering work differently between Algolia and Typesense:
Sorting
Algolia uses replicas for sorting, while Typesense allows sorting within a single index, reducing complexity.
Filters
Typesense uses a more explicit filtering syntax. Ensure your filter logic accounts for syntax differences.
Hierarchical Relationships
Algolia requires manually defining attributes for hierarchy (e.g., category.lvl0, category.lvl1). Typesense doesn’t have built-in hierarchical faceting but allows faceting on nested fields.
✅ Action Step
Convert your existing filtering and sorting logic to match Typesense’s syntax. Optimize your queries by leveraging Typesense’s ability to facet nested fields where applicable.
3. Faceted Search & Dynamic Filters
Faceted search lets users refine results dynamically. In Algolia, facets update automatically based on query results. In Typesense:
- Facets need to be explicitly retrieved in the query.
- If structured correctly, fewer queries are needed compared to Algolia.
- Dynamic filters (e.g., selecting "In Stock" should update available colors) must be manually handled.
✅ Action Step
Ensure your UI can handle manual facet updates and retrieve the right facet counts based on selected filters. Refer to Typesense faceting documentation.
4. Autocomplete Functionality
Autocomplete is crucial for user experience. When migrating:
- Update your autocomplete endpoint e.g.
/api/autocompletelogic to return results from Typesense. - Manually specify
query_byfields. - Fine-tune typo tolerance and ranking settings for best results.
✅ Action Step
Compare Algolia’s autocomplete ranking with Typesense’s settings to ensure relevance remains strong.
5. Sponsored & Pinned Results
If you use sponsored or pinned results in Algolia, verify how Typesense handles overrides. The logic remains similar, but implementation details may differ.
✅ Action Step
Test sponsored results to ensure they are displayed in the correct positions in search results.
Pros and Cons of Algolia vs. Typesense
Conclusion
Migrating from Algolia to Typesense is completely feasible but requires adjustments in schema, filtering, and sorting logic. If your project benefits from full customization and reduced costs, Typesense is a strong alternative. However, if you need a fully managed, plug-and-play solution, Algolia might still be the better fit.
✅ Final Action Step
Start with a proof-of-concept migration using a small dataset before committing to a full migration. Use the Typesense migration guide to structure your process effectively.