Anodot, a leader in AI-driven business monitoring and cloud cost optimization, relies on PostgreSQL databases to power their real-time analytics and anomaly detection capabilities. As their cloud workloads grew in complexity, Anodot faced challenges in maintaining PostgreSQL performance at scale while keeping AWS infrastructure costs under control. By integrating Volumez’ high-performance block storage service, Anodot achieved unprecedented storage performance, ensuring low-latency access to critical data while reducing cloud storage expenses.
Anodot’s PostgreSQL workloads in AWS
PostgreSQL plays a vital role in Anodot’s architecture, supporting the ingestion, processing, and querying of massive time-series datasets in real time. As data volumes increased, so did the need for high-speed storage capable of handling intensive read and write operations.
While AWS Elastic Block Store (EBS) is commonly used for cloud storage, Anodot encountered bottlenecks in achieving consistent high IOPS and low-latency performance, particularly as database workloads scaled. The cost of provisioning high performance EBS volumes also became a concern, leading Anodot to explore alternative solutions that could deliver both performance and cost savings.
Volumez: High-performance storage for PostgreSQL workloads
Volumez Data Infrastructure as a Service platform provided Anodot with a game-changing solution for optimizing PostgreSQL performance. Volumez enables ultra-low latency storage access and high IOPS, ensuring smooth database operations for complex analytical queries and high-velocity data ingestion.
Unlike traditional storage solutions, Volumez operates with a controller-less architecture, establishing direct Linux data paths to maximize performance efficiency. This approach eliminates unnecessary storage overhead, allowing Anodot to fully leverage the power of their AWS compute instances with local NVMe storage, reducing dependency on expensive EBS volumes.
The Challenge: Achieving low-latency PostgreSQL performance at scale
Anodot required a storage solution capable of delivering:
- High IOPS for PostgreSQL transactions – Anodot’s analytics workloads demand high-speed reads and writes to support real-time anomaly detection and monitoring.
- Low-latency storage access – Query performance is critical, as response times directly impact Anodot’s ability to provide timely insights.
- Cost-effective scalability – AWS EBS scaling costs were becoming prohibitive, prompting the search for a more efficient alternative.
The Solution: Volumez delivers superior performance and cost efficiency
By integrating Volumez, Anodot successfully addressed these challenges:
- High IOPS and throughput – Volumez delivered the consistent high-performance PostgreSQL required for real-time analytics.
- Lower storage costs – Anodot reduced its reliance on expensive high-tier EBS volumes by utilizing cost-efficient local NVMe storage.
- Enhanced resilience – Volumez provides built-in data redundancy and zone resilience, ensuring high availability without additional overhead.
Achieving scalable, predictable performance
Volumez’ Data Infrastructure as Code approach allowed Anodot to define its storage configurations in a simple YAML format, streamlining deployment and scaling across AWS environments. This flexibility enabled Anodot to optimize storage performance dynamically, ensuring PostgreSQL instances could handle peak workloads without unpredictable cost spikes.
Conclusion
Anodot’s adoption of Volumez has resulted in significant performance gains and cost savings for its PostgreSQL workloads on AWS. With high-throughput, low-latency storage optimized for analytics-driven workloads, Anodot can scale its data infrastructure efficiently while keeping cloud spending in check. This partnership ensures that Anodot’s AI-driven monitoring solutions continue to deliver real-time insights without compromise.
With Volumez, Anodot is now positioned to handle ever-growing data volumes with greater efficiency, reinforcing its leadership in cloud cost optimization and AI-powered anomaly detection.