Cloud Tool Spend Is Spiralling. Here’s How to Take Back Control.

The Cost of Complexity

Cloud data tool pricing is not simple, not uniform and not particularly transparent. You might pay for individual runs, compute time, data volume, data movement, connectors, environments, per user or for features.

It’s additionally confusing because each data tool uses its own jargon and calculations to give you a bill…

	AWS Glue: Pricing is typically based on DPUs and runtime. Cost varies depending on job size, processing time, region and how often the job runs. Supporting services such as Amazon S3, CloudWatch, crawlers, Data Catalog activity and other connected AWS services can also contribute to the overall bill.
	Azure Data Factory: A single workflow can generate multiple billable items, including activity runs, DIU-hours, pipeline orchestration, data movement, data flow execution and operations. Runtime and frequency both affect cost, and egress charges may apply depending on data flows.
	Google Dataflow: Pricing depends on the resources a job consumes, including vCPU, memory, Persistent Disk, shuffle, Streaming Engine and runtime. That means cost is shaped by how the workload behaves, as well as job frequency and duration.
	Databricks: Pricing depends on DBUs, workload type, runtime and the underlying cloud infrastructure. The same platform can have very different cost profiles depending on how compute is selected and managed, and job frequency and duration apply here just as they do with the other platforms.

These descriptions of pricing can feel like you’re trying to read the sun, and when you factor in organisations are using multiple clouds and tools you can see why cost are so difficult to predict.

More Layers. More Cost

To deal with this growing tech sprawl, we’re seeing FinOps teams use AI to build better mechanisms for tracking cloud cost. That helps with visibility, but there’s still a tangled mess of systems and tools underneath.

If you want a better understanding of costs and more predictability, you need to reduce this complexity. Organisations need fewer tools that can work across cloud and on-premises environments, without adding opaque pricing models.

One Platform. No Suprises.

This is where FME gives teams a simpler model. FME replaces the sprawl with a single platform that works across cloud and on-premises environments with a pricing model you can actually predict.

FME lets you add additional users at no extra cost. Some cloud tools don’t. FME allows longer processing jobs without charging more. Some cloud tools don’t. FME lets your process any data volume without adding to your bill. Some cloud tools don’t. FME has premium features without having to buy add-ons. Some cloud tools don’t. FME lets you run the same processes as often as you need without charging you each time. Some cloud tools don’t. FME lets you build complex transformations without charging more. Some cloud tools don’t. FME works on-prem and in the cloud. Some cloud tools don’t.

A fixed multi-year enterprise subscription gives you as much FME as you need, deployable on any cloud or on-premises system.

Predictability Comes from Simplicity

In a hybrid world, cost predictability comes from using fewer tools that can evolve with the architecture, without making the commercial model harder to manage. The more tools, environments, pricing units and processing patterns you add, the harder it becomes to understand what a data process really costs.

Monitoring tools and FinOps processes can help, but they don’t remove the complexity underneath. The simpler way to solve this problem is to reduce the number of moving parts where possible. That means choosing a capable tool like FME that can work anywhere and won’t slap you with an unexpected bill.

The Miso Arms has a new pricing model

Cynthia, The Landlady of The Miso Arms, has heard how some cloud tools price their products and thought she’d do something similar at the pub.