Comparison to alternatives

Prometheus vs. Graphite


Graphite focuses on being a passive time series database with a query language and graphing features. Any other concerns are addressed by external components.

Prometheus is a full monitoring and trending system that includes built-in and active scraping, storing, querying, graphing, and alerting based on time series data. It has knowledge about what the world should look like (which endpoints should exist, what time series patterns mean trouble, etc.), and actively tries to find faults.

Data model

Graphite stores numeric samples for named time series, much like Prometheus does. However, Prometheus's metadata model is richer: while Graphite metric names consist of dot-separated components which implicitly encode dimensions, Prometheus encodes dimensions explicitly as key-value pairs (labels) attached to a metric name. This allows easy filtering, grouping, and matching by these labels via in the query language.

Further, especially when Graphite is used in combination with StatsD, it is common to store only aggregated data over all monitored instances, rather than preserving the instance as a dimension and being able to drill down into individual problematic ones.

As an example, storing the number of HTTP requests to API servers with the response code 500 and the method POST to the /tracks endpoint would commonly be encoded like this in Graphite/StatsD: -> 93

In Prometheus the same data could be encoded like this (assuming three api-server instances):

api_server_http_requests_total{method="POST",handler="/tracks",status="500",instance="<sample1>"} -> 34
api_server_http_requests_total{method="POST",handler="/tracks",status="500",instance="<sample2>"} -> 28
api_server_http_requests_total{method="POST",handler="/tracks",status="500",instance="<sample3>"} -> 31


Graphite stores time series data on local disk in the Whisper format, an RRD-style database that expects samples to arrive at regular intervals. Every time series is stored in a separate file, and new samples overwrite old ones after a certain amount of time.

Prometheus also creates one local file per time series, but allows storing samples at arbitrary intervals as scrapes or rule evaluations occur. Since new samples are simply appended, old data may be kept arbitrarily long. Prometheus also works well for many short-lived, frequently changing sets of time series.

Prometheus vs. InfluxDB

InfluxDB is a very promising new open-source time series database. It did not exist when Prometheus development began, so we were unable to consider it as an alternative at the time. Still, there are significant differences between Prometheus and InfluxDB, and both systems are geared towards slightly different use cases.


The same scope differences as in the case of Graphite apply here.

Data model / storage

Summary: InfluxDB stores rows of events with full metadata for each event; Prometheus only stores numeric samples for existing time series. Both are good for different use cases.

While InfluxDB's data model also allows annotation of data with arbitrary key-value pairs, it differs significantly from Prometheus in the way this data is modeled and stored. At its core, InfluxDB stores timestamped events with full metadata (key-value pairs) attached to each event / row. Prometheus stores only numeric time series and stores metadata for each time series exactly once, and then continues to simply append timestamped samples for that existing metadata entry. In a test from March 2014, dumping typical Prometheus time series data into InfluxDB required 11x more disk storage in InfluxDB than in Prometheus due to this different data model.

If you are only interested in tracking the development of existing named time series (for example, the cumulative count of HTTP requests with the method POST to the /api/tracks endpoint on the instance, Prometheus will require much less storage space than InfluxDB. Further, Prometheus indexes all time series dimensions for efficient filtering, while InfluxDB currently only indexes tables by row timestamps (issue to track adding column indexes:

Still, InfluxDB is better geared towards the following use cases:

  • Storing all individual events, not just time series of values.
    • E.g. storing every HTTP request with full metadata vs. storing the cumulative count of HTTP requests for certain dimensions.
    • Logs and Metrics and Graphs, Oh My! describes the difference between event logging and metrics recording.
  • Storing time series with completely unbounded dimensionality.
    • E.g. storing user IDs or email addresses in the key-value metadata vs. storing bounded dimensionality like the HTTP method, HTTP handler and instance ID.

There are other storage features, such as downsampling, which InfluxDB supports and Prometheus does not yet.


Prometheus servers run independently of each other and only rely on their local storage for their core functionality: scraping, rule processing, and alerting.

InfluxDB is by design a distributed storage cluster with storage and queries being handled by many nodes at once.

This means that InfluxDB will be easier to scale horizontally, but it also means that you have to manage the complexity of a distributed storage system from the beginning. Prometheus will be simpler to run, but at some point you will need to shard servers explicitly along scalability boundaries like products, services, datacenters, or similar aspects. Independent servers (which can be run redundantly in parallel) may also give you better reliability and failure isolation, though that is debatable, since InfluxDB also can tolerate node outages due to data replication.

Prometheus vs. OpenTSDB

OpenTSDB is a distributed time series database based on Hadoop and HBase.


The same scope differences as in the case of Graphite apply here.

Data model

OpenTSDB's data model is almost identical to Prometheus's: time series are identified by a set of arbitrary key-value pairs (OpenTSDB "tags" are Prometheus "labels"). There are minor differences though, such as that Prometheus allows arbitrary characters in label values, while OpenTSDB is more restrictive. OpenTSDB is also lacking a full query language, only allowing simple aggregations via its API.


OpenTSDB's storage is implemented on top of Hadoop and HBase. This means that it is easy to scale OpenTSDB horizontally, but you have to accept the overall complexity of running a Hadoop/HBase cluster from the beginning.

Again, as in the case of InfluxDB, Prometheus will be simpler to run initially, but will require explicit sharding once the capacity of a single node is exceeded.