YugabyteDB Pricing Overview

What I enjoy most about YugabyteDB is its balance between easy to use and powerful functionality. It has been fundamental to my development as a beginner software engineer. I am especially fond of the PostgreSQL compatibility because it allowed me to leverage my existing knowledge of SQL into taking advantage of the database without having to learn an entirely different syntax. The learning curve was not extreme and I was able to get started right away, which was very helpful when I was just building my confidence with databases to that point.

The ease of implementation was another standout feature; I was able to integrate it easily into my projects. Connecting YugabyteDB to my app was easy, thanks to being able to use supported PostgreSQL libraries. The automatic replication and sharding of my data just worked with little effort from me, allowing for simplicity in a previously complicated setup. I also found it easy to scale applications to my needs, such as its functionality for multi-region replication. The customer support I received was also helpful, particularly when I ran into a few challenges during setup.

What truly sets YugabyteDB apart for me is its flexibility and reliability. I use YugabyteDB frequently in my projects , especially when I need a scalable and reliable solution .Whether I’m working on a small project or something larger, the database's ability to handle varying loads and provide strong consistency gives me confidence that my data is secure and up-to-date. Review collected by and hosted on G2.com.

Even though the PostgreSQL compatibility is great, I found that some of the advanced features that are specific to YugabyteDB are a little more challenging to get used to initially, especially if you are not already familiar with distributed databases. 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.