Weaviate Problems & Issues
Real user-reported problems, complaints, and limitations for Weaviate. Based on analysis of 10 signals.
Overall score: 72/100
Issues by Category
Performance degrades at scale (18M+ objects)
GitHub issue #4572 documents scaling problems: 'With 18 million objects ingested, every new query was taking anywhere between 5 to 15 seconds per query.' The main complaint is 'performance when trying to scale things up' - feels fine for small datasets but latency becomes unpredictable at scale.
Memory usage grows heavily with data
Users report 'memory usage keeps increasing heavily as more data is added to the cluster.' Some users report memory reaching 86.4% during indexing of large knowledge bases. Multi-node Kubernetes deployments particularly affected.
gRPC timeout errors during batch imports
Users report 'recurring DEADLINE_EXCEEDED errors during batch imports.' gRPC timeouts frustrate users trying to ingest large datasets. Requires tuning and workarounds that aren't well documented.
Steep learning curve for advanced usage
G2 reviews note Weaviate's 'resource-intensive nature' and 'the need for dedicated resources.' Users report difficulty getting it to 'run and scale reliably, potentially requiring considerable time and engineering effort to make Weaviate-based applications production-ready.'
Documentation gaps for AI API access
G2 reviews suggest 'improving documentation as it's crucial for integrating and using the services easily.' Gaps exist 'particularly for AI docs for direct API access.' New users struggle with advanced configurations.
Reindexing required when adding modules
Hacker News users complain about 'needing to reindex all data when wanting to attach a new module (vectorizer, generator, etc) to a data class, which cramps your style when talking millions to billions of docs.' Module changes are disruptive.
Cloud console is 'incredibly barebones'
Users describe the management console as 'incredibly barebones, with no way to turn off instances and limited ability to self-help when issues occur.' The web interface is not great, though client libraries work well.
Data corruption rendered database unusable
One G2 user reported 'data corrupted and Weaviate became entirely useless, retrieving different data at each request.' During this incident, support 'took a day to respond each time.' Serious reliability concern for production use.
Integration challenges with existing systems
G2 reviews cite 'integration challenges' particularly when 'incorporating Weaviate into existing systems not originally designed for semantic search or knowledge graphs.' RBAC integration with directory services also problematic.
Infrastructure costs escalate with dataset size
G2 notes Weaviate's 'resource-intensive nature that can lead to increased infrastructure costs depending on dataset size and query complexity.' Self-hosted requires significant compute. Cloud pricing changed in Oct 2025, adding complexity.
Regret Patterns
Common reasons users regret choosing Weaviate.
Scaled past comfortable limits
Worked great at 1M objects. At 18M+, queries took 5-15 seconds. Memory grew beyond provisioned capacity. Should have stress-tested with realistic data volumes before committing. Now migrating to Qdrant.
Underestimated self-hosting complexity
Chose self-hosted to save money. Spent weeks tuning Kubernetes deployment. Memory issues, gRPC timeouts, scaling challenges. Engineering time cost more than managed cloud would have. Should have started with Weaviate Cloud.
Module change required full reindex
Needed to add a new vectorizer to existing data class. Discovered this requires reindexing all data. With millions of documents, this took days and required downtime. Should have planned module architecture upfront.
Data corruption during critical period
Database corrupted and returned different results each query. Support took a day per response during the crisis. Lost trust in reliability. Should have had better backup strategy and tested disaster recovery.
Integration with existing systems painful
Assumed Weaviate would slot into existing architecture. RBAC integration with directory service was problematic. GraphQL learning curve for team used to REST. Should have evaluated integration requirements more carefully.
When Weaviate Breaks Down
Specific scenarios where Weaviate fails or underperforms.
Dataset exceeds 15-20 million objects
Query latency degrades from milliseconds to 5-15 seconds. Memory usage becomes unpredictable. The 'main complaint is performance when trying to scale things up.' Requires significant infrastructure investment or migration.
Need to add or change vectorizer module
Adding a module to existing data class requires full reindex. With millions of documents, this can take days. Causes service disruption. Better to plan module architecture from the start.
Self-hosted without DevOps expertise
Memory tuning, Kubernetes configuration, and scaling become overwhelming. gRPC timeouts during imports. Time spent on operations exceeds saved costs. Consider Weaviate Cloud or simpler alternatives.
High-frequency batch imports overwhelm system
gRPC DEADLINE_EXCEEDED errors during large imports. System becomes unstable under write pressure. Need to implement batching strategies and backoff. Test import patterns thoroughly before production.
Cloud console too limited for operations
Management console is 'incredibly barebones' - can't turn off instances, limited self-service. When issues occur, you're dependent on support. Power users frustrated by lack of control.