Scalr is happy to announce the release of Scalr Cost Analytics!
Cloud has dramatically transformed the process of resource provisioning, shifting control away from IT and towards a self-service model for developers. The resulting complexity makes it difficult to accurately predict cloud spend, allocate budget, and find inefficiencies. This is further complicated when enterprises split their cloud capacity across public and private clouds.
Yet, with an ever-changing cloud landscape (ex. Rackspace's recent exit of the pure-play IaaS market), it’s all the more critical for enterprise leadership to have visibility into the financial impact and business value of their different cloud options.
Here at Scalr, we work with enterprises that are pioneering cloud adoption in their industry; our cloud management platform (CMP) helps enterprise DevOps teams efficiently build cloud-native infrastructure, and enables their IT counterparts to govern the usage of cloud resources.
We saw that Scalr users and their colleagues were dealing first hand with the problems caused by a lack of financial transparency into their cloud resources. As such, we are building Scalr Cost Analytics: cost management tooling to restore visibility and control over cloud spend. This blog post details the major components of the tool’s initial release.
Why is Cloud Cost Management tooling needed?
Generally speaking, using cloud resources breaks the old model of fixed infrastructure costs, leading to two main problems.
1. Cloud Costs are hard to predict
Unlike traditional infrastructure capacity which comes with a pre-determined price independent of actual usage, cloud’s pay per use model entails that costs can vary extensively from one day to the next, as DevOps teams provision and tear down entire infrastructure clusters.
What’s more, self-service provisioning results in multiple departments or clients simultaneously tapping into the enterprise’s cloud, making it even more difficult for Finance to answer questions such as: “How much cloud budget should I allocate?”, “Who should I give it to?”, and “Will there be overspend?”
To solve this problem, and to keep up with real-time provisioning, Finance needs tooling that generates real-time cost reporting and predictions, and tracks budgets.
2. Cloud costs are hard to understand
When cloud resources are provisioned and decommissioned over the course of just a few hours or days, it becomes difficult to keep track of which resource does what exactly, and to whom it should be charged back to.
In fact, as far as Finance is concerned, cloud accounts tend to turn into buckets of instances and volumes that are delicate or confusing to reason about — and don’t even think about optimizing them! In turn, when IT receives an aggregate cloud bill at the end of the month, mapping capacity usage to specific users can be an arduous if not impossible task.
To solve this problem, and enable cloud cost optimization, what Finance and IT need is tooling that delivers a better understanding of the story behind each cloud resource: “Why was this instance provisioned?", "Who owns this volume?", and "Can we decommission this resource?”
In other words: Finance and IT need tooling that provides actionable cost data.
Surveying the existing landscape of cloud cost management tools
With these two challenges in mind, how can your enterprise use cloud in a financially responsible manner?
Several options have been available for cloud cost management, but face their own issues.
The Amazon Billing Management Console. Though vastly improved from its release in 2013, the console does not help answer “who” or “why” when it comes to spending the cloud budget. Furthermore, it is of course AWS-only.
Tools like Cloudability, CloudVertical, and Cloudyn. Though all have extensive features, the first two do not support private clouds, and CloudVertical does not provide budgeting capabilities. Cloudyn is a quality tool, but it is based on resource tagging, which is prone to human error (with misspelled of forgotten tags), and remains time-consuming for the DevOps engineers that provision cloud resources.
A competing CMP vendor offers a Cost Analytics product. However, its software focuses exclusively on drilling down into aggregated cost data through filters in a separate interface. With very limited bidirectional integration with their CMP, (which is where users are actually launching resources from!) it can be difficult to make their cost data actionable.
DIY: We won’t go here as organizations that prefer to build over buy do so because they can and not always because they should.
But why is Scalr Cost Analytics different? What makes Scalr Cost Analytics unique is that it is built directly into the Scalr Cloud Management Platform (CMP) itself.
As a CMP, Scalr thoroughly understands which cloud resources are being utilized, in what amounts, by whom, and for what purpose. In turn, Scalr Cost Analytics is able to leverage the insight gained from this integration to provide Finance, IT, and DevOps with contextualized expenditure predictions and actionable cost data.
What Can You Do With Scalr Cost Analytics?
Scalr Cost Analytics is designed to provide actionable cost insights and contextual tooling to the main cloud stakeholders in enterprises: DevOps, IT, and Finance.
- Model their existing accounting schema to categorize cloud costs
- Allocate cloud budget to business units, projects, and teams with their own leads
- Analyze cloud spend across multiple cloud platforms, down to individual server farms
- Forecast weekly, monthly, quarterly and yearly costs and overspend
IT will be able to:
- Break down spend by cloud server and resource types for projects, teams, and business units
- Plan for capacity with spend matched with server count and usage
- Pinpoint uncontrolled costs and apply lease management policies and quotas
- Audit the cloud bill for errors and inconsistencies
DevOps will be able to:
- View the costs of running their applications at design time BEFORE launching resources
- Choose the most cost efficient server types
- Understand the financial impact of usage spikes and dips