VictoriaMetrics Co-Founder on Scalable Observability Solutions
How VictoriaMetrics tackles monitoring challenges at scale with efficiency
1Career Background
Dzmitry Lazerka is a seasoned software engineer with extensive experience in machine learning and large-scale data systems. Before co-founding VictoriaMetrics in 2018, he worked at Lyft’s autonomous vehicle division, Spire Global, Bellgram, and Google through EPAM Systems. His projects spanned autonomous driving, maritime prediction, search, analytics, and distributed data processing, giving him deep insights into scalable backend systems.
2VictoriaMetrics Origins
VictoriaMetrics began as an open-source time-series database designed for high performance, scalability, and low operational overhead. The idea emerged from practical challenges faced by Lazerka and his co-founders with existing monitoring systems like Prometheus and InfluxDB. They aimed to create a solution that could handle large-scale data without becoming resource-intensive or operationally complex.
3The Scaling Problem
Traditional monitoring systems struggle as infrastructure grows. Metrics multiply with added services, instances, and labels, leading to high memory and CPU usage. Lazerka observed this firsthand at Lyft and Spire, where systems that worked at one scale became costly and unwieldy at another. VictoriaMetrics was built to address this inefficiency.
4Open-Source Advantage
VictoriaMetrics adopted an open-source model to let engineers validate its performance independently. Instead of marketing claims, users could test it with real production workloads. This transparency built trust and demonstrated its efficiency compared to alternatives like Thanos or InfluxDB.
5Observability Cost Challenges
Observability costs often spiral due to unchecked metric cardinality. Adding labels can turn one metric into millions of unique time series, increasing ingestion, storage, and query costs. These costs grow gradually, making them hard to spot until they become significant.
6Data Retention Pitfalls
Storing all observability data at the same resolution and retention period is a common mistake. High-value metrics for alerts or SLOs don’t need the same treatment as low-value diagnostic telemetry. Treating them equally leads to unnecessary infrastructure expenses.
7Engineering Solutions
VictoriaMetrics addresses cost challenges with techniques like streaming aggregation, which reduces storage needs by pre-aggregating metrics. Separating high-cardinality workloads and applying tiered retention policies further optimizes resource usage without sacrificing critical monitoring capabilities.
8Architectural Flexibility
The platform supports OpenTelemetry, Prometheus, Grafana, and Kubernetes, allowing seamless integration into existing observability stacks. This flexibility lets organizations adopt VictoriaMetrics incrementally, avoiding vendor lock-in and simplifying transitions.
9Enterprise Offerings
Beyond its open-source core, VictoriaMetrics provides enterprise and fully managed cloud deployments. Features like anomaly detection, powered by machine learning, enhance its value for large-scale operations needing advanced observability tools.
10Lessons from Lyft and Google
Lazerka’s experience at Lyft and Google highlighted the importance of scalable data systems. At Lyft’s Level 5 division, he worked on autonomous vehicle data pipelines, where inefficient monitoring could delay critical insights. These experiences shaped VictoriaMetrics’ focus on performance and simplicity.
11The Future of Observability
Lazerka believes observability should be treated as an engineering system, with costs optimized like any other infrastructure. As data volumes grow, solutions like VictoriaMetrics will become essential for balancing visibility and affordability in cloud-native environments.
12Key Takeaway
VictoriaMetrics exemplifies how solving a specific engineering problem—scalable monitoring—can lead to broader industry impact. Its open-source roots, efficiency gains, and focus on real-world usability make it a standout in the observability space.
Original article: Unite.AI — VictoriaMetrics Co-Founder on Scalable Observability Solutions