Prometheus pricing: hidden DIY costs to consider

When the Prometheus open source system monitoring toolkit debuted, it provided burdened observability teams with the tools necessary to thrive in today’s business environment. Offering a metrics-based observability framework, Prometheus assured that operational environments performed optimally.

However, creating a Prometheus setup from scratch often reaches a limit, and companies are realizing that an in-house Prometheus solution is not scalable or dependable enough for their expanding cloud-native ecosystems.

Initiating with a do-it-yourself (DIY) Prometheus setup is a logical first step for many organizations embarking on their cloud-native venture. It’s not only free and open-source but also benefits from robust community support and contributions.

Yet, as the cloud-native landscape expands and engineers require more data to refine their applications and infrastructure, Prometheus requires a more intricate architecture — demanding more staff input — to expand effectively. Eventually, every company faces the reality where maintaining an elaborate in-house Prometheus system is far from cost-free. It becomes increasingly expensive and consumes more engineering resources than managing your production environment itself.

1. Difficulty in accessing data

The limitations of Prometheus become apparent when engineers struggle to easily locate observability data. When scaling Prometheus, it’s essential to launch multiple instances, each managing and scraping data from designated services. This manual sharding of load across Prometheus instances can introduce issues as you scale.

Dashboard and alert management for Prometheus instances

From the standpoint of dashboard and alert management, it’s crucial to configure each dashboard or alert to connect to the correct node/Prometheus instance for data retrieval. Sometimes, a single dashboard or alert might need data from several Prometheus instances, requiring you to federate instances and create subsets of data for the original setups.

Ultimately, scaling Prometheus leads to a proliferation of federated nodes, resulting in a highly complex Prometheus architecture. As this occurs across different zones or regions, you need to federate data in an additional Prometheus instance and merge it across those areas. Engineers often find themselves spending excessive time locating data, running queries, and resolving issues.

2. Reliability issues leading to data loss

Prometheus, right out of the box, has a critical limitation—its failure results in the loss of active and historical data. It’s always advisable to operate multiple instances that scrape the same endpoints, ensuring the metrics are copied.

Dashboard reliability strategies

A best practice is to use load balancers and direct your dashboard instances to these balancers. Although this method generally maintains data availability, rolling restarts of Prometheus instances can create gaps in data continuity. Therefore, you might require a more enduring storage solution or a remote storage strategy, possibly spread across various cloud regions and providers to enhance fault tolerance, which again increases complexity and the operational load on engineering teams.

3. Costs of extended data retention

There is often a demand for longer data retention to improve troubleshooting processes. However, Prometheus is not designed for efficient long-term data storage due to the absence of integrated downsampling features.

For instance, maintaining one instance for six months with a 30-second scrape interval results in approximately 8100 Kbs. Conversely, downsampling to a one-hour resolution for the same period would require about 67.5 Kbs. As the need for prolonged data retention grows, downsampling becomes a critical efficiency factor, though it introduces additional complexity and requires more engineering effort to manage.

4. Difficult compromises driven by data growth

When you face the constraints of DIY Prometheus, you’re often forced to make challenging decisions between data collection and cost. Ideally, all data would be captured to ensure comprehensive availability. However, as your environment transitions from cloud to cloud-native, the volume of observability data increases more rapidly than your production capacity.

For example, if your infrastructure initially ran on VMs and now operates on containers, your infrastructure and cloud costs remain comparable with the same cluster size. However, instead of managing tens of VMs, you’re now overseeing hundreds or thousands of containers, each generating the same level of telemetry data as the VMs, escalating your observability expenses beyond those of the infrastructure supporting your applications. Reducing monitoring as you shift to containerized applications often signals the need for a more scalable solution.

• When DIY Prometheus no longer suffices, it’s time to explore alternatives. In evaluating other options, consider how a new solution can utilize the investments already made in your existing Prometheus setup, particularly in:
• Instrumentation
• Data collection
• Data presentation (dashboards and alerts)

A managed solution should enhance your existing instrumentation and data presentation capabilities while alleviating the escalating costs and operational burdens of managing an observability platform internally.

Chronosphere is designed from the ground up to address the scale, complexity, and reliability demands of cloud-native environments. It empowers engineers by providing quicker, more actionable alerts for efficient triage. It also enables them to dedicate less time to monitoring instrumentation and more to driving innovations that boost business growth.

According to Forrester Research, the average Chronosphere customer experiences a 165% return on investment and $7.75M in benefits over three years. Post-transformation, customers typically see a 60% reduction in observability data volumes while enhancing their observability metrics.

Does Prometheus cost money?

No, Prometheus itself is free as it is an open-source monitoring tool. However, indirect costs may arise from deployment, maintenance, and necessary infrastructure, especially in complex environments.

Is DIY Prometheus actually cheaper than paying for management?

While DIY Prometheus avoids direct subscription fees, it may not always be cheaper in the long run. Small setups can benefit from cost savings, but larger, more complex systems might incur higher costs due to staffing and potential downtime, making managed services a potentially more cost-effective option.