DalmatinerDB vs Prometheus

Struggling to choose between DalmatinerDB and Prometheus? Both products offer unique advantages, making it a tough decision.

DalmatinerDB is a Development solution with tags like metrics, timeseries, erlang.

It boasts features such as Fast write throughput, Built-in sharding and replication, Query language for analyzing time-series data, HTTP API for writing and querying metrics, Plugins for ingesting data from various sources and pros including Highly scalable and distributed architecture, Very fast writes for time-series data, Erlang runtime provides fault tolerance, Open source with permissive MIT license.

On the other hand, Prometheus is a Ai Tools & Services product tagged with monitoring, alerting, metrics.

Its standout features include Multi-dimensional data model with time series data identified by metric name and key/value pairs, PromQL, a flexible query language to leverage this dimensionality, No reliance on distributed storage; single server nodes are autonomous, Time series collection happens via a pull model over HTTP, Pushing time series is supported via an intermediary gateway, Targets are discovered via service discovery or static configuration, Multiple modes of graphing and dashboarding support, and it shines with pros like Highly dimensional model allows flexible and efficient queries, PromQL supports aggregation and recording rules for pre-calculation, Built-in alerting and notification routing, Highly available with simple operational model, Native support for Kubernetes, Strong ecosystem integration.

Feature	DalmatinerDB	Prometheus
Overall Score	N/A	1
Primary Category	Development	Ai Tools & Services

Product Overview

DalmatinerDB

Description: DalmatinerDB is a fast, distributed metrics database written in Erlang. It is optimized for storing time-series data like metrics and events. It can handle high volumes of writes with low latency.

Prometheus

Description: Prometheus is an open-source systems monitoring and alerting toolkit. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if certain conditions are met.

Key Features Comparison

DalmatinerDB Features

• Fast write throughput
• Built-in sharding and replication
• Query language for analyzing time-series data
• HTTP API for writing and querying metrics
• Plugins for ingesting data from various sources

Prometheus Features

• Multi-dimensional data model with time series data identified by metric name and key/value pairs
• PromQL, a flexible query language to leverage this dimensionality
• No reliance on distributed storage; single server nodes are autonomous
• Time series collection happens via a pull model over HTTP
• Pushing time series is supported via an intermediary gateway
• Targets are discovered via service discovery or static configuration
• Multiple modes of graphing and dashboarding support

Pros & Cons Analysis

DalmatinerDB

Pros

• Highly scalable and distributed architecture
• Very fast writes for time-series data
• Erlang runtime provides fault tolerance
• Open source with permissive MIT license

Cons

• Limited query capabilities compared to full-featured databases
• Lacks some features of commercial time-series databases
• Smaller community than more popular databases

Prometheus

Pros

• Highly dimensional model allows flexible and efficient queries
• PromQL supports aggregation and recording rules for pre-calculation
• Built-in alerting and notification routing
• Highly available with simple operational model
• Native support for Kubernetes
• Strong ecosystem integration

Cons

• Pull-based model can miss short-lived spikes between scrapes
• No automatic removal of stale metrics (extra storage usage)
• Limited tooling for stats analysis, forecasting, anomaly detection
• No built-in federation for massive scale
• Steep learning curve for PromQL and architecture

Pricing Comparison

DalmatinerDB

• Open Source

Prometheus

• Open Source