Hosted Graphite vs Prometheus

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

Hosted Graphite is a Online Services solution with tags like metrics, monitoring, visualization, timeseries, dashboard.

It boasts features such as Real-time graphing and dashboarding, Alerting and anomaly detection, Metrics storage and retention policies, REST API for automation and integration, Role-based access control and permissions, White-labeling and branding options and pros including Easy to set up and use, Scalable and reliable data collection, Pre-built integrations and plugins, Customizable dashboards and graphs, Available as SaaS or self-hosted.

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.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Hosted Graphite

Hosted Graphite

Hosted Graphite is a cloud-based monitoring tool for analyzing time-series metrics and data. It offers real-time graphing, dashboarding, alerts, and anomaly detection for observability of applications and infrastructure.

Categories:
metrics monitoring visualization timeseries dashboard

Hosted Graphite Features

  1. Real-time graphing and dashboarding
  2. Alerting and anomaly detection
  3. Metrics storage and retention policies
  4. REST API for automation and integration
  5. Role-based access control and permissions
  6. White-labeling and branding options

Pricing

  • Free
  • Freemium
  • Subscription-Based

Pros

Easy to set up and use

Scalable and reliable data collection

Pre-built integrations and plugins

Customizable dashboards and graphs

Available as SaaS or self-hosted

Cons

Can get expensive at higher tiers

Limited customization compared to open source

Third-party dependency for critical monitoring

Requires some expertise to get most value


Prometheus

Prometheus

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.

Categories:
monitoring alerting metrics

Prometheus Features

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

Pricing

  • Open Source

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