You can scale out easily just by adding nodes, which is a huge win compared to the hassle of dealing with traditional monolithic databases. Review collected by and hosted on G2.com.

For very large “big data” aggregations—for example, running COUNT across billions of rows—it can be slower than more specialized OLAP databases. Review collected by and hosted on G2.com.

Because it works seamlessly with standard ORMs and drivers, it feels very intuitive and “comfortable” for developers to use. Review collected by and hosted on G2.com.

If your app doesn’t require high availability or massive scale, then the sheer power of YugabyteDB may simply add complexity you don’t really need. Review collected by and hosted on G2.com.

I use YugabyteDB as our production application database and appreciate its horizontal scalability and strong ACID compliance. The PostgreSQL compatibility stands out for me, especially in terms of indexing, query tuning, and query optimization. Despite some initial setup efforts, it's now working well. Review collected by and hosted on G2.com.

I have issues with serializable isolation in YugabyteDB. Also, transaction ergonomics could be improved. Review collected by and hosted on G2.com.

Distributed features, highly scalable, and fault-tolerant. Review collected by and hosted on G2.com.

it’s "over-engineered" for small or medium applications. Review collected by and hosted on G2.com.

It’s open-source and cloud-agnostic, so you’re not locked into any specific cloud provider. Review collected by and hosted on G2.com.

If your app doesn’t require high availability or massive scale, then the sheer power of YugabyteDB may end up being unnecessary complexity for your use case. Review collected by and hosted on G2.com.

As someone new to the software industry, I found YugabyteDB very approachable and powerful at the same time. What I like the most is that it combines the benefits of SQL (which is familiar and easy to learn) with the scalability of NoSQL systems. It feels like the best of both worlds. I didn’t need to worry about complex setup—it was straightforward to get started using the documentation and tutorials. It supports PostgreSQL, which made it easier for me to understand queries and schemas without learning something entirely new. I also appreciated how resilient and reliable it is, especially for cloud-native applications. Review collected by and hosted on G2.com.

Because I'm new to this field, some of the advanced features like multi-region deployments or distributed transactions were a bit overwhelming at first. The learning curve becomes steeper as you dive deeper. Also, while the community is growing, it’s not as large as some older databases, so finding beginner-level examples for very specific use cases was sometimes a challenge. A few more beginner-focused resources or walkthroughs would definitely help. Review collected by and hosted on G2.com.

YugabyteDB gives the best of both worlds—it feels like using PostgreSQL but with the power of a modern distributed database. It automatically spreads data across multiple machines, which means it can handle a lot of users and big workloads without slowing down. It supports SQL, so it's easy to learn if you know basic database concepts. What I liked most is that it has strong consistency (like traditional databases) and high availability (like NoSQL systems). It's great for building applications that need to be fast and reliable at any scale. Review collected by and hosted on G2.com.

It can feel overwhelming at first if you're new to distributed systems. The deployment and configuration involve understanding concepts like tablet servers, placement policies, and replication factors. The documentation is good, but sometimes you have to dig through different pages to fully grasp certain setups. Also, because it’s newer than traditional databases, there aren’t as many community examples or Stack Overflow answers available yet. Review collected by and hosted on G2.com.

Because it works with standard ORMs and drivers, it feels very intuitive and “comfortable” for developers to use. Review collected by and hosted on G2.com.

Managing a multi-node cluster—handling updates, backups, and overall node health—takes noticeably more effort than using a managed RDS instance. Review collected by and hosted on G2.com.

PostgreSQL Compatibility: Allows quick onboarding and minimizes refactoring efforts.

Distributed SQL Engine: Provides horizontal scalability, HA, and geo-distribution out of the box.

Fault Tolerance: Handles individual node failures well, ensuring minimal disruption.

Developer Enablement: SQL-native access with good documentation and active community.

Good OLTP Performance: For high-throughput transactional workloads, YugabyteDB performs reliably under optimal conditions. Review collected by and hosted on G2.com.

Current Challenges and Limitations

We list below the most critical issues and limitations currently impacting our YugabyteDB deployment for the Iris application:

1. DDL Atomicity and Concurrency

Concurrent DDL on different objects often fails or causes schema mismatch errors.

2. Truncate Behavior

Truncate operations retain old tablets, causing resource sprawl (CPU, disk).

3. Slow Aggregations / Analytical Queries

Aggregate functions (e.g., COUNT, SUM, GROUP BY) perform poorly on large tables.

4. Large Query Errors

Queries fail with RPC message size errors; workarounds require non-trivial gflag tuning.

5. Index Creation Challenges

Index creation on large tables is slow (can take hours) and unstable if DMLs are running.

Failure of concurrent DDLs can result in application downtime or stale views.

6. Intermittent Application Slowness

During high-ingestion windows (e.g., Spark + C# clients), CPU spikes to 80–85%.

7. Slow Queries Despite Indexing

Poor performance even with correctly designed indexes.

8. DR Limitations

DR requires symmetrical 3-node cluster and does not replicate DDL—this increases manual effort.

9. Node Crashes

Occasional crashes due to pg_client_use_shared_memory bug.

10. Resource Utilization

Max 1800 concurrent connections across 6 nodes (300/node).

High CPU usage (80%+) under 5500 OPS and 1500+ connections.

11. PITR Disk Usage

PITR with 2-day retention consumes 1–2 TB of disk.

Expected behavior, but storage overhead is significant.

12. Audit Logging

pgaudit causes crashes and lacks centralized log management.

Prefer audit logs to be stored as queryable tables.

13. Tablet Rebalancing

Rebalancing takes 2–3 hours post node failures.

14. Schema Name Change Not Reflected in UI

15. Query Performance Monitoring

No centralized query metrics dashboard across nodes.

pg_stat_statements is per-node; requires custom data aggregation.

16. Lack of ORM Support

Prisma ORM lacks native Yugabyte support.

Clear timeline for a smart driver integration is still needed.

17. Other Issues

Dead tuples causing transaction failures .

Clock skew-related tserver crashes .

Incorrect health checks leading to table drop incidents.

Backup to S3 failed due to endpoint misconfig .

Recommendations & Expectations

Top Priorities for Upcoming Releases:

Full concurrent DDL/DML support

Improved join and aggregation performance

Central query dashboard across universe

Audit log offloading and centralization

Smart tablet rebalancing and table-level recovery

Simplified backup/restore UX (especially for S3)

Documentation and Usability:

Better defaults for performance-related gflags.

Clear guidance on best practices for DDL coordination and high-throughput ingestion.

Support & Training:

More structured training on query optimization and resource tuning

Roadmap visibility for critical features (e.g., Prisma ORM support)

Final Thoughts

We appreciate Yugabyte’s continued partnership and responsiveness to issues. The platform shows strong promise for OLTP workloads and mission-critical deployments, but there are clear gaps—especially around operational tooling, analytical query support, and DDL concurrency—that we hope to see addressed in the near-term roadmap.

Our team remains committed to collaborating with Yugabyte to improve the product and looks forward to further performance and reliability enhancement Review collected by and hosted on G2.com.

YugabyteDB is an excellent database for anyone needing a powerful distributed system. It works like PostgreSQL, so you can use the same familiar queries, but it also takes care of all the tricky parts of distributed design for you. This makes it easy to use, even for complex applications.

The best part is their support team—they're quick to respond and really helpful, making sure everything runs smoothly. If you're looking for a database that's reliable, easy to work with, and backed by a great team, YugabyteDB is a great choice. Review collected by and hosted on G2.com.

YugabyteDB is powerful but has a few downsides. It has a learning curve for distributed systems and a smaller community ecosystem compared to established databases. Some advanced features may also feel less mature, requiring extra effort during implementation. Review collected by and hosted on G2.com.