Managing cloud database costs is a top priority for organisations striving for financial efficiency. With the interesting adoption of FinOps principles, businesses seek better control over their cloud spending while maintaining performance. DBtune emerges as a powerful solution, leveraging AI-driven automation to optimize database performance and reduce costs effectively.
Challenges of database cost optimization:
Databases are the backbone of modern applications but can also be a significant financial burden. Common cost challenges are:
- Over-provisioned resources: Excessive compute and storage allocations drive up costs unnecessarily.
- Inefficient query execution: Poorly optimized queries increase CPU and memory usage.
- Lack of visibility: Without insights into database performance, optimizing costs will become a difficult task.
Why FinOps Matters for Open-Source Databases
Open-source databases like PostgreSQL, MySQL, and MariaDB offer flexibility and cost savings over proprietary solutions. However, running them in the cloud—whether self-managed on AWS, GCP, or Azure, or via managed services like RDS, Cloud SQL or Azure Flexible Servers —can lead to unpredictable costs. Key cost challenges include:
- Overprovisioning resources (CPU, memory, storage)
- Inefficient query performance leading to higher compute costs
- Underutilized instances that waste budget
- Lack of visibility into cost vs. performance trade-offs
How DBtune supports FinOps principles:
DBtune addresses these challenges by providing AI-driven database tuning solutions that go with FinOps methodology.
- Enhanced cost visibility: DBtune offers detailed analytics of database workloads, enabling teams to identify and optimize resource-intensive queries. This transparency supports informed budgeting and financial forecasting.
Automated performance optimization by continuously monitoring workload patterns, it applies optimizations like:
- Enhancing query performance by reducing compute costs.
- Adjusting resources based on demand.
- Ensures queries run efficiently without requiring manual intervention.
Bridging Finance and Engineering by fostering collaboration between finance and engineering teams by providing clear, actionable cost insights. Engineers can optimize performance while the finance team maintains better control over cloud expenses.
Optimization targets in DBtune:
DBtune allows users to optimize for either Throughput or Average Query Runtime, depending on their workload characteristics:
- Throughput (TPS – Transactions Per Second): Recommended if your database is experiencing CPU or I/O bottlenecks. If the workload is already running at capacity and cannot process more transactions efficiently, DBtune can help increase overall throughput and performance at peak load.
Average Query Runtime (Latency in ms): Ideal for workloads composed of complex queries where reducing execution time is the priority. Even if the system isn’t heavily bottlenecked, bigger database instances will lead to greater query runtime improvements.
Comparison of Performance with DBtune vs Without DBtune:
In the graph, we could visualize performance improvements based on two key metrics: Throughput and Average Query Runtime. The X-axis can represent time or workload characteristics, and the Y-axis can show the performance metric (e.g., Transactions per Second for Throughput, or Latency in milliseconds for Query Runtime).
- Without DBtune: Higher latency and lower throughput
- With DBtune: Significantly lower latency and higher throughput.
The following table highlights the performance improvements when using DBtune with community PostgreSQL, a commonly used open-source database.
Key Observations:
Baseline for RDS m5.4xlarge:
- This represents the performance of large instances without tuning.
- FinOps Angle: This larger instances deliver higher TPS initially and are costly.
Baseline for RDS m5.2xlarge:
- This shows the performance of a smaller, more cost-efficient instance without optimization.
- FinOps Angle: This is half of the cost of above instance but the TPS is lower. This gives trade-off cost between cost and performance.
Performance with PGtune on m5.2xlarge:
- This shows improved performance compared to above baseline instances but not fully improved.
- FinOps Angle: PGtune provides moderate cost performance improvement.
Performance with DBtune on m5.2xlarge:
FinOps Angle: DBtune delivers maximum performance to cost efficiency.
This shows a significant improvement in performance. The TPS surpasses even the baseline of m5.4xlarge instance. Which shows cost effective and excellent performance.
Conclusion:
In conclusion, DBtune offers a unique and powerful solution to address the financial and performance challenges organizations face in managing cloud database costs. This makes DBtune an indispensable tool for businesses that are adopting FinOps principles and aiming for better financial efficiency while maintaining optimal database performance.