Feature	VictoriaMetrics	Prometheus
Architecture	Single-node and clustered	Single-node
Performance	High ingestion and query throughput	Efficient for medium-scale setups
Scalability	Horizontally scalable in clustered mode	Limited to vertical scaling
Data Compression	Advanced compression techniques for cost efficiency	Basic compression
Data Model	Multi-dimensional (similar to Prometheus)	Multi-dimensional with key/value pairs
Query Language	PromQL	PromQL
Data Collection	Supports Prometheus scraping	Pull-based model (scraping)
Alerting	Requires external integration (Alertmanager)	Built-in Alertmanager
Long-Term Storage	Designed for efficient long-term storage	Typically complemented with remote storage solutions
Multi-Tenancy	Supported	Not natively supported
Global Querying	Supported in clustered mode	Not supported
Compatibility	Integrates with Prometheus ecosystem	Native component of Prometheus ecosystem
Ease of Use	More complex setup for clustered mode	Simple and straightforward setup
Use Case	Large-scale, high-performance, long-term storage	Medium-scale, immediate querying, and alerting

VictoriaMetrics and Prometheus are both popular tools in the realm of time-series data storage and monitoring, but they have different focuses and features. Here’s a comparative overview:

Prometheus

Overview: Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. Since its inception, it has become a foundational component of many monitoring systems, particularly within the cloud-native ecosystem.

Key Features:

1. Data Model: Prometheus uses a multi-dimensional data model with time series data identified by metric name and key/value pairs.
2. PromQL: Prometheus Query Language (PromQL) allows for powerful and flexible querying of time-series data.
3. Pull Model: Prometheus primarily uses a pull-based model for collecting metrics, scraping them from instrumented targets.
4. Alerting: It includes a built-in alert manager that supports defining and managing alert rules and notifications.
5. Self-Contained: Prometheus is designed to be a self-contained system that can operate independently, with local storage optimized for reliability and performance.
6. Ecosystem: Prometheus is part of the Cloud Native Computing Foundation (CNCF) and integrates well with Kubernetes and other cloud-native tools.

Limitations:

• Scalability: While Prometheus is highly performant for many use cases, its single-node architecture can limit scalability for very large workloads.
• Long-Term Storage: Prometheus’ local storage is not ideal for long-term storage of metrics. It is usually complemented with remote storage solutions for this purpose.

VictoriaMetrics

Overview: VictoriaMetrics is a high-performance, cost-effective, and scalable time-series database designed to handle large-scale monitoring and observability workloads. It can be used as a replacement for Prometheus’ storage layer.

Key Features:

1. High Performance: VictoriaMetrics can handle high ingestion rates and large volumes of data efficiently, often outperforming Prometheus in write and query throughput.
2. Scalability: It supports both single-node and clustered modes, enabling horizontal scaling to handle larger datasets and higher query loads.
3. Data Compression: It uses advanced data compression techniques, resulting in lower storage costs and faster query responses.
4. Compatibility with Prometheus: VictoriaMetrics can ingest data from Prometheus and supports PromQL, making it easy to integrate with existing Prometheus setups.
5. Multi-Tenancy: VictoriaMetrics supports multi-tenancy, allowing multiple teams or users to share the same database instance without interfering with each other.
6. Global Querying: In a clustered setup, VictoriaMetrics supports global querying, providing a unified view of data across all nodes.

Use Cases:

• Large-Scale Monitoring: For organizations that need to handle high volumes of metrics data, such as those running extensive cloud infrastructure or IoT deployments.
• Long-Term Storage: VictoriaMetrics is suitable for long-term storage of metrics data due to its efficient data compression and cost-effective storage.

Complementary Use:

• Remote Write Integration: Prometheus can be configured to use VictoriaMetrics as a remote storage backend. This setup allows users to leverage Prometheus’ rich feature set while benefiting from VictoriaMetrics’ scalability and performance for storage and querying.

Choosing Between Them

• Prometheus: Best suited for smaller to medium-scale setups or as a standalone monitoring and alerting solution within cloud-native environments.
• VictoriaMetrics: Ideal for large-scale or high-performance environments, particularly when long-term storage and efficient data handling are critical.