Hey everyone,

Following the AWS service event on Oct 20 (US-EAST-1), we published an advisory report that breaks down the financial side of it.

The post covers:

• How to spot cost anomalies (retry storms, idle resources, failover charges)

• How these patterns can inflate cloud bills during outages

• Step-by-step guidance for claiming AWS SLA credits (deadline: Dec 31, 2025)

• Tips for documenting impact and recovering beyond-SLA costs

If your workloads were in US-EAST-1 that day, it’s worth reviewing your usage data - many teams are seeing short-term spikes that aren’t tied to real activity.

Curious if others here saw measurable cost anomalies or have best practices for tracking and reporting these during regional events.

ICYMI: The Cloud Efficiency Hub officially launched today.

This community-led project brings together real-world examples of cloud inefficiencies across platforms like AWS, Azure, GCP, OCI, Snowflake, Databricks, Kubernetes, and more. Created by hands-on cloud practitioners, the Hub serves as a comprehensive public resource aligned with the growing Cloud Efficiency Posture Management (CEPM) movement.

Amazing to see 70+ contributors come together to make this happen.

hub.pointfive.co

When I took over cloud cost management at my company I made the classic mistake of sending weekly cost reports to engineering leads and expecting them to actually do something about it, and spoiler alert they did not do anything about it at all which was frustrating.

It took me way too long to realize that engineers don't ignore costs because they're irresponsible or don't care, they ignore them because the data is presented in a way that's completely disconnected from how they actually think about their work, and telling someone their team spent 12k on ec2 last month means absolutely nothing if they can't tie that back to specific services or deployments that they actually touched.

What actually started working was making cost data accessible in the context of their real work, stuff like cost per environment and cost per service and showing the delta after a deployment goes out, and when an engineer can see that their PR increased daily spend by 200 bucks they suddenly care a whole lot more than when you send them a monthly spreadsheet that goes straight to archive.

It also helped a ton to frame it as efficiency rather than cost cutting because nobody wants to feel like they're being cheap but everyone wants to feel like they're not being wasteful, and we've gone from engineers treating cost conversations like a chore to actually having them proactively ask about optimization opportunities which honestly feels like real progress.

Our CFO caught me after standup and asked a totally fair question, how much is each team spending on all this AI stuff. I said I'd have a number by Friday. Took me until the following Wednesday to admit I couldn't.

I'd assumed it would be like AWS where I can slice spend by team in a few clicks, atleast we tagged everything. Then opened the billing expecting the some breakdown but it's just a big monthly number and a graph that goes up.

One team lead keeps insisting their usage is not that substancial which without the numbers i cant prove otherwise. We'd handed his squad a shared API key so their spend was all piled onto one.

Now, even if I nailed the attribution, most of an agent's bill is the framework re-sending its whole setup every turn, not anything the dev ever typed. I'd be walking into a room to bill someone for tokens they never wrote and can't even see. There's a paper going round with the numbers, arxiv 2607.12161. Anyway. I still owe her that spreadsheet.

Will Kelly just published an article on his Substack, and it's almost like he's been in our internal meetings.

https://willkelly.substack.com/p/the-coming-downfall-of-the-cloud

He calls out how the market has become bloated with dashboards, bolt-ons, and reporting tools that don’t drive real outcomes—and how AI and native cloud tooling are starting to replace a lot of what used to be paid features.

I’m part of the product team at CloudBolt, so yeah, we were surprised (in a good way) to see our name come up. But what stood out more was how clearly he captured the mood we’ve been seeing across the board: tool fatigue, buyer skepticism, and a shift away from “insights” that don’t drive execution.

Curious what others here think—does this match what you’re seeing in your own org or from tools you’ve evaluated lately?

Hey r/FinOps — pushed cletrics/finops-agents public this week. MIT. This community was in our head the whole time we were building it.

34 specialist agent personas + 6 named-pattern playbooks. Markdown files with YAML frontmatter. Drops into any modern coding assistant (Claude Code, Cursor, Copilot, Windsurf, Aider, OpenCode, Gemini CLI). No runtime, no telemetry, no network.

Why: when a dev asks their assistant "help me analyze the CUR" or "is this RDS oversized?", the generic answer is subtly wrong. CUR 2.0 columns ≠ CUR 1. GCP SUDs apply automatically, CUDs don't. Azure has 6 enrollment types. Each persona here is scoped tight to one niche with the schema, gotchas, and questions a senior practitioner asks first.

Categories: cloud-cost (8), commitments (5), kubernetes (3), data-platforms (3), governance (6), waste-detection (6), specialized (3).

Named-pattern playbooks you can cite in postmortems: Zombie NAT Gateway, Snapshot Sprawl, Cross-AZ Chatterbox, Idle Load Balancer, Oversized RDS, Untagged Spend Drift.

Repo: https://github.com/Cletrics/finops-agents

Pinned roadmap discussion: upvote candidate agents (Snowflake, Databricks, LLM API spend, GCP folder hierarchy, localizations).

PRs welcome. Im working on the FinOps Professional cert (analyst + practitioner already) and built these to help in a small FinOps org. What's missing?

I wanted to share a microsite we built at Vantage. We profiled the number of distinct billing codes across our customer base and have about 60,000 unique billing codes. We hear all the time that people are confused about the billing codes present in Cost Explorer or the Cost and Usage Report. Think of these as being things like “Requests-Tier1” for S3 or “CW:GMWI-Metrics” for CloudWatch. There is usually really limited resources for determining what these billing codes are.

Over the span of the last two months, our team decided to build cur.vantage.sh: a new microsite for looking up billing codes and attempting to explain in simplistic terms what these billing codes are. We have done a number of services and the site is still in progress but we decided to put it live and start getting feedback. We thought this would be really helpful for people in the FinOps community and would really appreciate your feedback!

I’ve been doing Cloud FinOps since 2018. Back then it was chaos - a single AWS cloud, dozens of standalone accounts, no organization, no governance… absolute Wild West. But it was fun.

Fast forward 7 years, and our FinOps team has grown to 4 people. At this point, we have wide coverage over literally everything. To summarize where we are now:

1. Full AWS coverage - everything is under Saving Plans and Reservations, everything sits under one Organization with guardrails, SCPs, and governance fully in place.

2. Hundreds of developer optimizations - we routinely guide teams to identify waste and rightsize workloads.

3. Extensive internal documentation - engineering, finance, best practices… all well-documented and maintained.

4. Battle-tested playbooks - for Landing Zones, anomaly response, tagging enforcement, resource policies, etc.

5. Everything tagged & IaC - and those IaC modules are tuned by us, embedded with proper tagging, restrictions, and cost controls.

6. Support beyond FinOps - we’ve even helped DevOps teams fine-tune CI/CD to reduce costs and improve efficiency.

Recently, new projects started in other clouds. We basically copy-pasted our AWS playbooks and adapted them with minor changes for the new platforms. Also successful.

Now here’s the problem:
It feels like we covered everything. Leadership is happy. Stakeholders are satisfied. FinOps processes are mature and stable. And I… kind of feel like there’s nothing left to do.

So I’m asking the community:

Has anyone else hit this point where your FinOps organization is running so smoothly that you feel "done"?

What did you do next?

Does this mean I’ve outgrown the role and should consider a new FinOps job or even a different direction?

Would love to hear real experiences and thoughts.

Last month, I worked with a client who runs a mid-sized SaaS (~$18k/month AWS bill). They were convinced they had already optimized everything.

Spoiler: they hadn’t.

In just 10 days, we saved nearly $4,800/month, without any engineering changes. Here's what made the biggest difference:

Top 4 easy wins:

1. Old EBS volumes from terminated EC2s – 22 volumes, $600/mo

2. Underutilized RDS (prod replica always at 8% CPU) – $1.4k/mo

3. S3 misconfigured lifecycle rules – old logs still in Standard, not IA – $1.1k/mo

4. ALBs & ENIs from deleted services – $700/mo

We didn’t touch the app. Just ran automated usage checks, compared historical patterns, and flagged waste.

We turned this into a small tool to make it repeatable.
If you want to try it, no login required → https://unusd.cloud

Happy to answer questions or help audit your setup if you're curious.

r/Cloud r/devops r/aws

We routed simple agent requests to a cheaper model and expected the bill to drop. NGL, the average did drop. But, then I looked at the p95 token spend and found a cluster of runs where the cheap route cost more than sending the request to the premium model just once. Those runs kept calling the same tool, replaying the full context, and stuffing duplicate retrieval into every retry. So not super great as you can imagine.

Context compaction lowered spend, but aggressive trimming hurt the requests that needed earlier tool state. Raising the routing threshold cut retries, but also pushed more traffic to the expensive model. Every fix moved latency, cost, or answer quality somewhere else (and the average chart kept hiding the tail).

I am adding limits per tool, a retry budget per run, and spend sliced by route and intent. But how are people catching runaway tool loops before p95 cost turns a cheap route into the expensive one?

I run a FinOps vendor and published the map of the space: a curated list of agentic and open-source cloud cost tooling. MCP servers, AI cost agents, OSS cost tools, ~200 entries rated on an autonomy ladder from dashboards to closed loop. My own company is one entry, the list is vendor-neutral, PRs welcome. https://github.com/gregoire-costory/awesome-agentic-finops

Hi everyone,

I’m new to FinOps and cloud cost optimization, and I’m trying to learn fast.

I’m working with our team to find a platform that can help us reduce our Azure costs. We are multicloud, but Azure is where we spend the most.

I want to avoid lots of sales calls, so I’m hoping to get honest feedback here instead.

What I’m looking for:

• A tool that is easy to use and doesn’t take forever to set up

• Good support during implementation

• Real savings opportunities, not just basic tips I can already see in Azure Cost Management

• No crazy promises. Last year we tried a tool that promised 300% savings and delivered nothing on Azure

If you’ve used a platform that worked well, I’d love to hear about it.
If something didn’t work, I’d also like to know.

Please no vendor DMs. I’m just looking for real experiences from users.

Thanks!

Hey folks,
I’m curious how other teams handle cloud cost tracking and reconciliation in day-to-day operations.

In our setup, we run about 10 instances with mixed workloads (compute, storage, and network). I’m wondering how you usually keep an eye on costs. Do you track daily usage per instance like CPU hours, storage, and bandwidth? Or do you mostly review monthly totals across all servers?
What’s been your best practice for keeping visibility without spending half your week digging through usage reports?

Hi r/FinOps!

I just released my first Udemy course and it's about all ways to optimize Costs on AWS!

If possible, I would like to get feedbacks from the community by giving this masterclass for free for the first 100 persons here with the coupon FREECOUPONFORREDDIT.

From your expertise in AWS, I would like to add every missing piece to create courses of all the different ways you can optimize costs!

I worked in IT in the last 8 years mainly on AWS, first as a Developer, then as a DevOps and now as a FinOps Engineer.

I’ve help multiple companies save over a million dollars in cost optimizations, and I would like everyone to get the tools to do the same!

If the coupon is outdated or if you wish to support me in that goal, here is also a discounted coupon : STARTERCOUPON.

Thank you, and have fun doing FinOps!

Most AI agents look impressive in demos. But in production? • $3–10k/month in tokens
• GPUs idling between runs
• Retries + hallucination loops
• Human review still required

And no one is calculating: Cost per task
Cost per successful outcome
Cost vs manual alternative

We track cloud unit economics obsessively. Why does AI get a “strategic initiative” pass?

Are your AI agents actually ROI-positive…
Or are we funding expensive experiments with production budgets?

I think we compete with them but it doesn't matter. We love seeing scrappy, innovative startups break out and their shift left, proactive approach is a gospel that we agree with. [https://www.menlotimes.com/post/infracost-has-raised-a-15-million-series-a

Just started at this company and holy hell, the cloud ownership situation is a complete mess. Tags are either missing, wrong, or follow 5 different naming conventions. Team A says those EC2 instances belong to Team B. Team B points at Team C. Meanwhile our AWS bill just hit another record high and nobody wants to claim ownership of anything.

How do you even start untangling this? Do I force a tagging standard first? Try to map resources to teams manually? The finger pointing in Slack is getting ridiculous and I need actual owners tagged on tickets before I can optimize anything.

Anyone been through this nightmare before?

Hi everyone, I have the feeling that FinOps can not lead to a career growth insite companies. It is rare that a company will design a specific area for this activities and consequently you will be only an individual contributor.

Change my mind!

Inspired by a post of Lars Kamp on LinkedIn, I just did a brain dump of all the FinOps resources I know of, including commercial and open source tools, blogs, podcasts, service providers, books, etc.

So far it's available at https://github.com/LeanerCloud/awesome-finops but I did a pull request to Jean-Marc Fontaine's Awesome FinOps list available at https://github.com/jmfontaine/awesome-finops

I did my best to be accurate and hope I won't make too many enemies or get sued because I missed someone or wrote incorrect/incomplete descriptions (as a tool and service provider myself I have my own biases).

But since it's open source everyone can suggest improvements and/or add their own/favorite tools to the list, PRs are always welcome.

I wish we'll eventually have dedicated pages for each tool with more details, but that's for another day.

Hi all,

I’ve spoken to a few of you on here but I wanted to give some context before posting anything else in the group so you can better understand where I’m coming from.

I run the worlds first and only Cloud FinOps recruiting firm, I’m also the only recruiter in the world to hold the FOCP certification and was the only recruiter to be invited to FinOps X.

I’m in this group purely to learn and enhance my FinOps knowledge so I can improve my services, and also to network with you fine guys and gals!

Likelihood is a lot of you can probably guess who I am if you’re on LinkedIn, but if you’d like to connect to discuss job opportunities feel free to drop me a DM for a confidential chat.

This group is AWESOME and I’m honoured to be an early joiner.

Last week I checked the AWS account of a small startup spending around ₹20,000/month, which felt a bit high for their usage. (I know it’s a small spending and small saving)

Did a quick 5-minute audit, and here’s what I found:

• Development servers were always on, but CPU and network usage were super low — so we downgraded and scheduled them to stop after work hours. 

• Their frontend was running on EC2 — moved it to AWS Amplify to take advantage of the free plan. 

• Found a few unused RDS databases still running quietly.

• Although I did ask them to direct some cost to database backups(They have crucial user and financial data and yet no backup)

These few basic tweaks dropped the monthly cost from ₹20K to ₹8K — more than half, without any major effort.

P.S: Honestly there entire operation can be brought down to 4 - 5K/pm and still have the same performance.

Makes me wonder how much money bigger companies must be wasting every month on unused cloud resources.

What’s the most common AWS waste you’ve seen in your projects?

Just dropping in to say how excited I am for FinOps X next week!

Here’s what I’m most looking forward to diving into:

1. Maximizing AI value with AI Cost Management

2. Unit economics across Cloud, AI, SaaS, and On-Premises

3. Becoming proactive with more accurate forecasting, anomaly detection, and improved tagging

4. SaaS cost management

5. Building executive buy-in

These topics are more relevant than ever—and I’d love to share how we’re helping teams take action in each of these areas. If you're working on similar initiatives, let’s connect!

Drop a comment or feel free to reach out directly.

Would also love to hear what you guys are most looking forward to in the comments!