CommPulse

Hi everyone, I’m from Korea, and English isn’t my first language, so I used AI to help translate this post. I’ve been experimenting with different AI tools and projects for a while. During that process, I ended up with a much larger token bill than I expected. That experience made me think there should be a better way for people and companies to use AI without constantly worrying about costs, while still keeping the system reliable and safe. At first, I started building something just for my own use. I added features one by one, and over time the project became much bigger than I originally planned. Eventually, I started wondering whether it could become a real business. But I’ve run into a problem. In Korea, I haven’t been able to find many companies that are openly talking about this issue or looking for a solution. I’m not sure whether the problem isn’t serious enough yet, or whether companies simply don’t want to share their AI cost problems publicly. So I’d really appreciate some honest feedback from developers, engineers, founders, or anyone operating AI products in production. Are AI or LLM costs a real problem for you? What part of cost management is the most difficult? How do you currently track costs by model, feature, customer, or request? What kind of tool or service would actually be useful? Have you tried solving this problem already? If so, what didn’t work? Any practical advice or real-world experience would be very helpful. I’m still trying to understand whether this is a problem worth solving and what people actually need. Thank you. submitted by /u/MutedMaintenance6420 to r/FinOps [link] [comments]

Disclaimer: personal affiliation but the tool is free to use https://preview.redd.it/74no8zy0bdhh1.jpg?width=1280&format=pjpg&auto=webp&s=2aadf213c2bce5fba8c0feca988e88dead8d1c50 I built a free CLI that scans AWS for likely waste patterns and reports estimated cost + carbon opportunities. npx greenops-scan Why this might be relevant here: Runs locally and uses read-only AWS APIs. Credentials stay on the operator machine. Produces JSON + PDF findings for triage. I am specifically looking for DevOps feedback on signal quality: which findings are useful vs noisy, and what evidence should be added to reduce false positives. submitted by /u/slawa-pid to r/FinOps [link] [comments]

Disclosure, I work at Login VSI. The FinOps question we keep hearing about AVD Hybrid: which workloads are worth running hybrid vs. fully cloud once you factor licensing and management overhead. Live chat on that, with Marcel Meurer (creator of Hydra, built around cost efficiency), Steve Downs and Andrej Radinger (Microsoft), and Ron Oglesby (Login VSI). Bring questions or drop them here. Thursday, September 10, 11am ET: Register Here submitted by /u/LoginVSIMax to r/FinOps [link] [comments]

If your GitHub Actions jobs are stuck in "Queued" or waiting for a runner, GitHub has now confirmed there is an incident. GitHub says they identified an issue with a database primary and are failing over to a replica. Actions is affected, and GitHub Pages is also experiencing degraded performance. So if you have been checking your workflow, runner settings, permissions or billing, it might not be your configuration. Status page: https://www.githubstatus.com/incidents/y1t7p9fzrlj2 Hopefully this saves someone else from spending an hour debugging their YAML. submitted by /u/Codeblix_Ltd to r/devops [link] [comments]

Currently working at a fast moving startup, and have been assigned work related to providing alerting and monitors to our crucial P0 infrastructure. At a high level, the real goal of this is to provide engineers during incidents a fast way to root-cause whether the page was due to their service breaking, or if the underlying infrastructure is down. For example, if our DNS provider is down, a product engineer may be paged and spend time trying to root-cause why their service is having a bunch of 404s when the actual root cause is down one level of the stack. I was thinking of introducing a common set of protocols that everyone could follow using our paging service (incident.io); they would first spend time to look at our set of monitors and see if anyone was alerted regarding core infrastructure, and if not, they could then move up a level and spend time triaging at the service level. Obviously this would also include aid during the triage by agents. This would also give us metrics on time spent on firefighting our core infra vs service level issues. But I am so totally new to all this, so what does your teams process look like? Has anyone tried something similar before and noticed this was faulty? Truly, I am open ears to anything regarding this sort of triage work, so any insights, small or big is appreciated submitted by /u/MuchoMole101010101 to r/devops [link] [comments]

Hey, for people working with AWS/GCP/Azure and FinOps, What do you find most frustrating about managing cloud costs? Is it actually finding where you're wasting money, or is it more about getting people to do something about the recommendations? For example, if your tools tell you that you could save $10k/month by changing some infrastructure, what usually happens after that? Does someone actually fix it, or does it just sit there because of ownership, risk, engineering effort, etc.? Also curious how you deal with SaaS costs alongside cloud costs. Just trying to understand how people actually handle this in practice. submitted by /u/Severe_Lawyer_3076 to r/FinOps [link] [comments]

Hello everyone, I’m looking for advice on which FinOps certification path would be most relevant for my background: FinOps Practitioner or FinOps Engineer. I currently work as a Cloud Architect, primarily in AWS, across well-architected design, security and compliance, and FinOps-related topics. Cost optimization, governance, and cloud efficiency are already part of my day-to-day work, but I’d like to deepen my FinOps knowledge in a more structured way and become more involved in the community. My goal is not only to earn a certification, but also to improve how I collaborate with engineering, finance, and business stakeholders around cloud spend and value. For someone with this profile, would you recommend starting with the Practitioner certification as the common foundation, or going directly toward the Engineer path? I’d also welcome any advice on the most valuable ways to engage with the FinOps community beyond certification. Thanks a lot submitted by /u/AAAmine8 to r/FinOps [link] [comments]

Suppose, i have a VM with a boot disk with data, 1 additional disk with some files.Once the zone or region goes down for some hours and becomes available after it,will the GCE contents be same as before the outage ? This is not about MIGs, backup and DR services etc.. just would like to know if the compute engine data in disks and IP address be retained once the zone/region is back after the outage. Please clarify if anyone experienced it submitted by /u/suryad123 to r/googlecloud [link] [comments]

I'm u/teamcloudkeeper . FinOps for AI is about to matter for basically everyone, not just teams running massive training clusters. If you're running any AI workload in production, token pricing, GPU costs, and bursty-vs-continuous usage patterns are already there whether you've built practices around them or not. Every org, big or small, is gonna need this in place sooner than later. We've built our FinOps for AI capability around this, augmented by Lens and Tuner . But it's the human behind the tools making the calls on ownership and tradeoffs. That doesn't get automated away. You've gotta start too. Always open if you want to know more about us cloudkeeper.com . submitted by /u/teamcloudkeeper to r/FinOps [link] [comments]

Hi all, Quick reality check needed. I’m transitioning from a Finance/Business background (French Grande École + US exchange) into an MSIS program in the US (Spring 2027) . My target is landing a FinOps Analyst, Cloud Business Consultant, or IT Data Analyst role and i heard that the personnal skills take are the advantages to get hired in the US market and not the degree so i m trying to improve myself. My Cert Strategy: AWS Cloud Practitioner (CCP) & FinOps Certified Practitioner (FOCP) (Before MSIS) AWS Solutions Architect Associate (SAA) & Power BI/SQL (During 1st MSIS semester) My Questions: Does combining a Finance background + MSIS + FOCP + AWS SAA give me a real competitive edge over pure CS or MBA grads for entry/early-career FinOps roles? Is FOCP respected by hiring managers when paired with AWS SAA for someone breaking into the field? Any key tips to position this profile effectively for internships/OPT? Appreciate any honest feedback! submitted by /u/Zestyclose-Brick5463 to r/FinOps [link] [comments]

We are getting a large number of customers unable to access Google Drive in the APAC region this afternoon, anyone else see anything? Nothing on their status page yet, but down detector is lighting up. https://downdetector.co.nz/status/google-drive/ submitted by /u/Hibernat8 to r/sysadmin [link] [comments]

Hi, We’ve been running Veritas NetBackup in an on-prem setup for a few years, but the cost of licensing + infrastructure is getting harder to justify, so we’re now re-evaluating our backup approach. The main focus is VMware workloads, and we’re trying to understand what actually makes sense today in terms of cost and long-term scalability. For a simplified reference point, here’s a smaller scenario we’re using for modeling: We have 10 VMs (~200GB each). Retention is split, 5 VMs kept for 1 year and the other 5 kept for 30 days. That puts us at roughly 2TB initial backup size, with a small daily change rate (~0.1%). From there, we’re trying to understand how costs scale when you factor in: long-term retention tiers snapshot / backup storage growth data transfer / egress (if applicable) licensing vs managed service pricing What we’re trying to compare is basically: AWS-native approach (AWS Backup / EBS snapshots / S3 lifecycle tiers) Veeam or similar third-party backup tools Legacy enterprise setups like NetBackup / Veritas The part that’s still unclear for us is where the real cost differences actually show up in practice. On paper, AWS-native looks simpler, but I’m not sure if it holds up once you scale retention and restore requirements. On the other hand, tools like Veeam add flexibility, but it’s not obvious if they reduce cost or just shift it into a different layer. Before taking anything to management, I’m trying to get a clearer view of what actually drives cost in real deployments (storage growth, retention policies, licensing, operational overhead, etc.) and whether third-party tools genuinely justify their complexity. Would appreciate any real-world input from anyone who’s done a similar migration or comparison. submitted by /u/Immediate-Screen7893 to r/sysadmin [link] [comments]

A lot of companies rely on FinOps teams or cloud optimization partners to manage their AWS spend. But I’m curious, are there certain cost leakages that are so obvious that anyone managing an AWS environment should be able to spot them? Looking to hear some practical, real-world examples from the community. submitted by /u/whispered_word12 to r/FinOps [link] [comments]

submitted by /u/Morningstar_1 to r/FinOps [link] [comments]

This is something I've been trying to wrap my head around. If the cloud bill goes down, that's obviously good, right? But what if usage also went down? And if the bill goes up but we're serving twice as much traffic, maybe that's actually an improvement. What do you use to measure cloud efficiency beyond the total bill? submitted by /u/whispered_word12 to r/FinOps [link] [comments]

Our Spark line item on AWS keeps growing faster than our data does and it has become a recurring line in budget reviews. We have done spot instances, auto-termination, right-sizing, the standard checklist. Gains show up, then flatten out fast. Looking for what has worked beyond that basic set, esp for teams running tens of TBs a day. Trying to figure out if the next real gain comes from infrastructure changes rather than squeezing the same clusters harder. submitted by /u/Fantastic_Jello1629 to r/FinOps [link] [comments]

For companies using OpenAI, Anthropic and other AI tools heavily, costs can grow fast — and the monthly bill can be hard to predict. I’m trying to understand how painful this is in practice. For founders, CFOs and finance teams: Is AI spend becoming a meaningful line item? Are unpredictable bills making budgeting harder? Would committing annually for a discount create a cash-flow problem? I’m exploring a model to solve this, but before building anything I want to understand whether the problem is actually painful enough. Would love to hear how you’re managing AI spend today. submitted by /u/krxnewman to r/FinOps [link] [comments]

Real pricing pulled from AWS, no credentials or account needed to preview One command, full cost tree: total monthly cost, per-resource breakdown, and which resource is actually driving the bill (RDS Multi-AZ was 56% of it here). Feels like the kind of check that should just be part of a normal plan/apply workflow. Happy to answer questions on how it resolves the plan or what it does with CloudFormation/Pulumi if anyone's curious. submitted by /u/Independent-Ease-609 to r/FinOps [link] [comments]

got the bill yesterday. another surprise. we migrated our phone system to Azure 3 months ago. I did the math. I calculated everything. compute, storage, network, the works. or so I thought. turns out I completely missed the cost of monitoring logs. application insights. I didn't even think about it. and now it's like 15% of our monthly bill. 15%. for logs I barely even look at. I'm starting to understand why people complain about cloud pricing. it's not that it's expensive it's that it's impossible to predict every month there's something new. I've started using the cost management tools more. trying to be better about tagging resources and setting budgets. but I still feel like I'm one wrong configuration away from a 5000 surprise. anyone else feel like Azure pricing is designed to confuse you. or am I just bad at math submitted by /u/dnickell to r/AZURE [link] [comments]

Now Hiring: Cloud & FinOps / Azure Architect (Remote) We are looking for an experienced Cloud & FinOps / Azure Architect to lead the governance, architecture, security, and cost optimization of our Microsoft Azure environment. This is a strategic leadership role for someone who has strong expertise in Azure architecture, cloud financial management, enterprise data platforms, and cost optimization. You will help design secure, scalable, and cost-efficient cloud solutions while managing large-scale Azure environments with extensive historical data. Key Responsibilities: Lead Azure architecture, governance, and cloud strategy Design secure, scalable, resilient, and cost-effective Azure solutions Develop and implement Azure FinOps strategies for cost visibility and optimization Optimize Azure spending across storage, compute, backup, licensing, and reserved capacity Create data lifecycle strategies for large-scale historical data environments Design and optimize Azure Data Lake, Azure Databricks, CDP, and data integration platforms Improve cloud efficiency through storage optimization, archiving, automation, and cost monitoring Collaborate with Engineering, Data, Security, Finance, Microsoft, and cloud partners Required Skills & Qualifications: Extensive experience with Microsoft Azure enterprise environments Strong expertise in Azure Architecture and Azure Governance Proven experience with Azure FinOps / Cloud Cost Optimization Hands-on knowledge of: Microsoft Azure Azure Storage & Lifecycle Management Azure Data Lake Azure Databricks Enterprise Cloud Architecture Cloud Security Azure OpenAI Experience managing large-scale enterprise data estates (10+ years of historical data) Strong understanding of cloud budgeting, forecasting, cost management, and optimization Experience designing scalable and cost-efficient data platforms Excellent communication and stakeholder management skills Preferred Qualifications: FinOps Foundation Certification or equivalent Experience with Customer Data Platforms (CDP) such as Amperity Knowledge of Master Data Management (MDM) and data governance frameworks Experience with enterprise analytics and large-scale data ecosystems Interested candidates are encouraged to apply! Join us and help shape the future of enterprise cloud architecture and FinOps optimization. Send your cv: [email protected] . submitted by /u/Ok_Feedback_8999 to r/FinOps [link] [comments]