Meilisearch Problems & Issues
Real user-reported problems, complaints, and limitations for Meilisearch. Based on analysis of 10 signals.
Overall score: 72/100
Issues by Category
Service jams with large datasets (30M+ docs)
GitHub issues report that after importing 30 million documents, 'the service experiences jams three or four times daily, each lasting several to ten minutes, making the service unavailable.' Large datasets require careful management.
Only searches first 100k of 1M+ records
Users report that 'despite successfully importing 1 million+ records, Meilisearch doesn't search 9/10ths of the records, searching maybe only the first 100k.' This creates a critical gap where most data is effectively invisible.
Indexing falls behind by 30-50 minutes on updates
For frequently changing data, Meilisearch struggles to keep up. Users report 'updating the index on single events like user starring a post is not effective' with 'the index getting backlogged and behind by 30-50 minutes.' Not suited for real-time update scenarios.
Storage bloat: 10MB data becomes 200MB indexed
Hacker News users raise concerns about storage efficiency: 'a 10MB document is taking approximately 200MB according to their documentation.' This 20x storage multiplier becomes expensive at scale and limits dataset size.
Meilisearch Cloud suffers frequent outages
Multiple G2 reviewers report cloud service reliability issues. One user describes Meilisearch Cloud as 'just a bunch of incompetent people unable to solve any issues' with 'search freezes lasting for three hours frequently.' Cloud resiliency needs significant improvement.
Documents silently fail to index without warnings
GitHub issue #4438 documents how 'when indexing new documents through the JS API, Meilisearch returns a JSON indicating a successfully enqueued task, but documents may silently fail to index without logging or meaningful response.' No error visibility.
Sort order breaks after 4-5 pages of results
GitHub issue #5180 documents that 'sort order is not working as expected, with results resetting after 4-5 pages.' Pagination with sorting becomes unreliable for users browsing deep into result sets.
Dashboard lacks sophistication for index management
Users feel 'the dashboard should be more sophisticated to effectively manage indexes.' The dashboard doesn't provide insights on search requests like response times and top queries. Advanced use cases require workarounds or external tooling.
Missing auth/permissions support requires workarounds
Users report having to 'use workarounds to handle lacking auth/permissions support.' Meilisearch doesn't natively support per-user search permissions, requiring Redis or other tools to manage access control.
Cloud pricing expensive at high traffic
G2 reviews note 'if you have a lot of traffic, the pricing per search on the cloud can be expensive.' Recent pricing changes have raised concerns, as 'the new pricing model might not feel as fair or cost-effective for those with large indexes but lower usage.'
Regret Patterns
Common reasons users regret choosing Meilisearch.
Chose Meilisearch Cloud, hit reliability walls
Teams attracted by Meilisearch's simplicity chose Cloud for convenience, only to experience multi-hour outages affecting production search. Wished they had self-hosted or chosen a more reliable managed service.
Dataset grew beyond comfortable limits
Started with 100k documents and Meilisearch was blazing fast. As data grew to millions, indexing slowed dramatically and service became unstable. Didn't anticipate scalability ceiling.
Real-time updates don't work as expected
Expected Meilisearch to handle real-time updates like user actions or inventory changes. Discovered 30-50 minute index lag making search results stale. Had to implement workarounds or switch tools.
Needed per-user permissions, Meilisearch doesn't have them
Built multi-tenant app assuming permissions could be added later. Discovered Meilisearch has no native auth/permissions. Had to build custom middleware with Redis, adding complexity.
Storage costs exploded due to index bloat
Didn't realize 10MB of documents becomes 200MB indexed. Infrastructure costs multiplied unexpectedly as dataset grew. Had to provision more disk/RAM than anticipated.
When Meilisearch Breaks Down
Specific scenarios where Meilisearch fails or underperforms.
Dataset exceeds 30 million documents
Service experiences jams 3-4 times daily, each lasting several minutes. Search becomes unavailable during these periods. Meilisearch single-node architecture hits limits. Need to migrate to distributed solution like Elasticsearch.
Frequent data updates (100+ per minute)
Index falls behind by 30-50 minutes, making search results stale. Meilisearch optimizes for search speed over index speed. Real-time e-commerce inventory or social media feeds become problematic.
Production depends on Meilisearch Cloud
Multi-hour outages impact production search with no fallback. Cloud reliability issues surface. Either need to self-host with proper redundancy or switch to a provider with better SLAs.
Multi-tenant application needs permissions
Meilisearch has no native per-user or per-tenant permissions. Must build custom middleware layer with Redis or similar. Adds architectural complexity that tools like Elasticsearch handle natively.
Need deep analytics and monitoring
Dashboard doesn't show response times, top queries, or detailed metrics. Need to build custom logging and analytics pipeline. Algolia's built-in analytics become appealing.