The New Stack Podcast26 Okt 2023

Why the Cloud Makes Forecasts Difficult and How FinOps Helps

Moving workloads to the cloud presents cost prediction challenges. Traditional setups with on-premises hardware offer predictability, but cloud costs are usage-based and granular. In this podcast episode, Matt Stellpflug, a senior FinOps specialist at ProsperOps, discusses the complexities of forecasting cloud expenses with TNS host Heather Joslyn.

Cloud users face fluctuating costs due to continuous deployments and changing workloads. There are additional expenses for data access and transfer. Stellpflug emphasizes the importance of establishing reference workloads and benchmarks for accurate forecasting.

Engineers play a vital role in FinOps initiatives since they ensure application availability and system integrity. Stellpflug suggests collaborating with engineering teams to identify essential metrics. He co-authored an "Engineer's Guide to Cloud Cost Optimization," highlighting the distinction between resource and rate optimization. Best practices involve addressing high-impact, low-risk areas first, engaging subject matter experts for complex issues, and maintaining momentum. This episode also provides further insights into implementing FinOps for effective cloud cost management.

Learn more from The New Stack about FinOps and ProsperOps:

FinOps Overview, News, and Trends

ProsperOps Wants to Automate Your FinOps Strategy

Engineer’s Guide to Cloud Cost Optimization: Manual DIY Optimization

Engineer’s Guide to Cloud Cost Optimization: Engineering Resources in the Cloud

Engineer’s Guide to Cloud Cost Optimization: Prioritize Cloud Rate Optimization

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(300)

Dynatrace's $915M Arize deal bets AI agents are just another app to monitor

Dynatrace completed its $915 million acquisition of Arize on Oct. 1, combining Dynatrace’s application and infrastructure monitoring with Arize’s AI agent tracing and evaluation capabilities. The deal...

5 Okt 20min

Bit Cloud’s next chapter starts after the AI builds your app

For many developers, turning an AI-generated prototype into maintainable software requires more than generating code—it requires infrastructure, collaboration, testing and review. In this episode of T...

1 Okt 31min

CloudBees just committed to an AI-first pivot. Here's why it matters for enterprise DevOps teams

CloudBees CEO Mo Plassnig is leading the CI/CD company through a major transformation as generative AI reshapes software development. Returning to CloudBees eight years after joining through its acqui...

30 Sep 23min

A third option is emerging in the fight over AI and your data

The AI industry has faced a growing enterprise dilemma: companies want access to powerful proprietary AI models without risking sensitive data or intellectual property, while AI labs want to protect t...

23 Sep 30min

Drowning in AI pull requests: Harness's field CTO on code review and a Git repo built for agents

Harness Field CTO Martin Reynolds joins The New Stack to talk about what happens after coding agents start opening pull requests faster than anyone can review them. He explains how he first saw the bo...

7 Sep 26min

How to find failures without drowning in tracing data

Traces provide a detailed view of a request’s journey through data, microservices and applications, helping SREs pinpoint where failures occur and resolve issues faster. But while tracing can reduce d...

3 Sep 31min

Why CPUs still matter in the age of AI agents

As AI evolves from conversational chatbots to autonomous agents, CPUs are becoming an increasingly important part of the infrastructure equation. In this episode, The New Stack speaks with Bhumik Pate...

11 Aug 26min

Why Doist Says Less AI Can Deliver More

Doist CTO Gonçalo Silva says AI is reshaping software development, but success depends on restraint rather than rapid feature expansion. Instead of chasing every AI capability, Doist prioritizes “subt...

31 Jul 35min