r/FinOps

Starting a job in FinOps pivoting from GovCon (7 YOE as SFA). I ran aross Ben Van de Maas "Complete learning path to finops".

https://benjaminvandermaas.medium.com/complete-learning-path-for-finops-2d8e0c8416ee

If you were basically starting from scratch on the technical side and FinOps side would you follow his path or is there another direction? I want to build solid fundamentals through an optimized learning path.

Learning path is basically,

1. Tech Fundamentals - https://learn.cantrill.io/p/tech-fundamentals

2. AWS Cloud Practioner - https://github.com/kananinirav/AWS-Certified-Cloud-Practitioner-Notes

3. AWS Certified Solutions Architect - https://learn.cantrill.io/p/aws-certified-solutions-architect-associate-saa-c03

4. FinOps Foundation website + the Cloud FinOps Book by J.R. Storment

5. Tooling

6. People

I'll have a great mentor to start but very much into self-study as well and want to make sure I'm setting my self up for a long successful career.

I have spent the last few years working in operations and support with a heavy focus on escalations and operational excellence. I am currently finishing up my AWS SAA to pivot into a full cloud ops role. I already hold my CCP and FOCP certifications.

I am looking for guidance on how to break into this space, ideally in a remote capacity. I want to find a position that allows me to build my skills without the extreme, high-stress cycle I am used to in my current operations background. I know that passing a test is a totally different planet compared to what happens in a real production environment, so I am looking for a sanity check from someone actually in the trenches.

If you have been in this space for a while and would be willing to share some perspective here, I would appreciate the insight. Even better, if you are open to a quick 10 minute call to tell me how you got your start and how you navigated finding a role that was sustainable, please shoot me a DM.

I am happy to respect your time and keep it brief. Thanks for any help.

I'm close to 3.5 years into my job doing Non Functional Testing and performance engineering. I'm looking to switch out to a product-based firm soon, but I want to avoid roles that are going to force me through a brutal LeetCode/DSA interview gauntlet. I suck at that and want it out of my way.

I'm currently doing my AWS SAA to get my cloud knowledge back up to speed since I haven't really touched it since 2022. For after I'm done, I'm looking at a longer transition into FinOps or Governance. I think I like controlling costs and I kind of excel at it naturally since it feels pretty adjacent to optimizing system performance and tracking down resource hogs.

Has anyone made a similar jump from testing/performance into FinOps? Just looking to hear from people who might have figured out if this is a realistic long-term plan, and what I can do alongside my SAA to be battle-ready faster.

Most AI cost conversations are about absolute spend ("our Anthropic bill went up X%"). The conversation I keep getting pulled into with founders and engineering leads is different. They don't want to spend less on AI. They want to know how much of what they are spending is actually producing value vs going to coordination failures, retry storms, agent loops, and similar waste patterns.

Three questions if you have a minute:

1. At your company, do you attribute AI spend to outcomes at the workflow or feature level, or is it bucketed as a generic API cost line item?
2. How much of your AI spend would you guess is "productive" vs "wasted on retries, loops, or runaway behavior"? Even a rough percentage if you don't have hard data.
3. If you had a tool that said "X% of your AI spend last month went to failed coordination patterns, here are the specific patterns", would that change how you approach AI FinOps?

Not selling anything. Trying to figure out whether FinOps practitioners are already thinking about AI waste at the pattern level, or whether the conversation is still mostly at the volume and model-choice level.

in your honest read, do the engineering and product teams you work with actually care about their AI bill yet, or is it still "just a cost that exists" until you flag it? Trying to figure out whether AI cost awareness has spread beyond the FinOps function, or if FinOps is still doing most of the worrying alone.

Cloud cost visibility was already broken BEFORE AI. IE - shared infrastructure, tagging drift, cross-team ownership disputes, costs that don't connect to customers or features or outcomes. Most teams were already flying blind on a bill they couldn't explain.

AI doesn't JUST add to that problem. It MULTIPLIES it because the unit of cost is no longer something you can track at the infrastructure level.

Every other cost variable drifts in ways you can at least investigate. AI token consumption is different. It scales with what each user does, not how many users you have. A user who pastes a 50-page contract into your AI feature can cost 100 times more than a user who asks a simple question. Same subscription price. Same customer tier. Completely different cost to serve and almost nothing in the current landscape traces that back to the customer, the feature, or the workflow that caused it.

If you thought attribution was hard before AI, you haven't seen anything yet.

Curious how others are approaching this. Are you treating AI spend as its own attribution problem or folding it into general cloud cost management?

Feels like this has become much more common lately.

We keep seeing invoices issued to one company, but the incoming transfer arrives from a totally different entity/account name with little or no explanation attached.

At low volume it’s manageable, but once transaction counts grow it starts creating real delays around matching, overdue tracking, and finance visibility.

Curious whether other teams are seeing the same thing recently and how you’re handling it operationally without turning reconciliation into detective work.

Hey, guys. Just what the title says. I looked up this job and seemed like a pretty great fit, but Im unsure of what the starter roles are to get into this role. ChatGPT kind of confused me a little. I'm seeing I can become one from being a help desk tech, but I'm unsure how accurate that is. What do you guys think?

Hi all, I wanted to drop my website in this subreddit as a way to publicize my work and services.

I'm offering consulting services to offer a post-training step that optimizes models for deployment by avoiding expensive inference, feature lookup, executing kernels, etc. when not necessary.

The underlying tech is essentially advanced analytics that cross-correlates high dimensional data with predictions, and finds regions of the data space that don't require compute-heavy resources.

I can help with systems where the objective is to reduce cost, increase throughput, reduce latency, and reduce energy usage.

If you're interested in a pilot or have questions, please do reach out here or book a meeting through the website. I love working on technical problems so I'm very committed to solving yours.

https://compressmodels.github.io

Been working on something called nable. It's a local MCP server that connects your billing APIs (AWS, Azure, GCP, plus Datadog, Snowflake, Stripe and a few others) to Claude or Cursor so you can ask questions about your spend in plain English.

But it's not just a connector. Here's what it actually does on top of the raw data:

Anomaly detection that compares same weekday baselines, not flat rolling averages. So it knows the difference between a Friday deploy spike and a Tuesday something-is-wrong spike.

Tag-based attribution : map your resource tags to teams in a YAML file, get spend ranked by team across every provider in one query.

Budget enforcement with a CI gate : set limits in a budget.yml, the CI step exits non-zero when you're over. No more end-of-month surprises.

Rightsizing that actually files the ticket : reads CloudWatch CPU metrics, finds the idle resources, calculates the savings, opens the Jira or Linear or GitHub issue for you.

RBAC for teams : viewer, analyst, admin roles with per key team scoping. The platform team sees platform costs. That's it.

Runs locally, credentials never leave your machine, no cloud sync, nothing to breach on our end.

Genuinely want to know:

• Is this solving a real pain or is the answer just "hire a FinOps analyst"
• What does your current workflow actually look like when someone asks why the bill went up
• What would make you never use something like this
• What would you expect to work on day one
• Anything else or things not working let me know: this is meant to be a free tool for pure visibility

Free to use, no account needed: nable.sh

Not trying to sell anyone anything, just want to know if this is useful before I build more of it.

I’ve been seeing so many people getting hit with these giant AI bills so I built a SaaS tool to track everything from different providers in one place with daily syncs and budget control/notifications. It’s called CostGuard (https://www.costguard-ai.com) and I’m actually still looking for alpha testers if anyone is interested!

Our setup is a mix of microservices glued together with ad hoc scripts and some half baked event driven pieces across aws and a few on prem holdouts. every week there’s some outage from a service failing silently or cascading because nothing has proper retries or isolation. the team spends more time firefighting than actually building anything new.

we do have monitoring and alerts, but they mostly tell us after the fact, and runbooks are outdated. tried refactoring one service to make it more resilient but leadership keeps pushing features over fixing underlying issues. budget is tight too, so big rewrites aren’t really an option.

how are you stabilizing things long term without doing a full rip and replace?

Hello Everyone,

Been doing trade/manual labor style work for years and honestly my body already feels the wear and tear from it. I respect the work, but I know I can’t keep doing physically demanding jobs forever.

Lately I’ve been looking into cloud computing and FinOps because it seems like an interesting mix of tech, business, problem solving, and potentially a better long-term lifestyle physically and financially.

Problem is… I have basically zero tech background.

No coding experience.
No IT experience.
No degree yet.
Most of my experience is aircraft maintenance and military life.

Right now I’m looking at maybe starting on getting Google IT Support cert? then take AWS practioner course, I am looking to apply to WGU (online univ) to get my degree as well.

I can’t carry much over from my military life to this transition, but hopefully my security clearance and work ethic can help me a bit.

Any suggestions, recommendations and tips would help.

Transitioned from embedded (5 yoe), to platform engineering 2 months ago.

Recently, the principal engineer on our team was showing me our cloud cost dashboards, and complaining about how much of a headache it is to justify the huge o11y costs to Finance every month. I said that I'd like to take on that responsibility, and that it sounds like something finops would do.

He said great, and gave me the first official "finops champion" title of the team. I realize this may be a huge mistake given how little professional experience I have with cloud, but I feel it's worth a shot and will be a good learning experience regardless of what happens. Also the principal engineer said he will still be at the meetings to help out if I'm really floundering.

My first meeting with finance will be in 6 weeks. I know I won't be expected to contribute much if anything, but would like to get started.

Any general advice? Or recommended resources or certs (like from FinOps Foundation) worth starting over the next few weeks?

I am building Zyvoq, and it can delete all your idle resources in just a few steps with simple UI interactions across multiple clouds, including AWS, GCP, and Azure for now.

I read a lot about how deleting resources after getting recommendations becomes messy, and it becomes even more difficult when you are managing multiple clouds.

So, to solve this problem, I am introducing zyvoq.moamir.cloud.

Please give your feedback and opinions, does this solve a real problem or not?

How is your team currently tracking LLM API spend?

We're cobbling together spreadsheets and the OpenAI

dashboard, but it feels broken. Curious what others do.

I'm nervous! I come from a non-traditional background (Communications degree over 10 years ago), but I recently finished the AWS reStart program & got my CCP. Since finishing the program I've been working on some personal projects and building more on the labs we did in class. From what I've been researching (seeing on Reddit) cloud roles are not entry-level, yet I see internships for cloud (how sway?) I guess I'm posting for some encouraging words for this interview (1st with the company's recruiter) and also some advice for someone without a CS degree but has been learning on their own (finishing CS50p, did FreeCodeCamp back in 2016) how to get into tech?

Inherited this cloud setup tha'ts a mess across aws and some azure. multiple accounts with overlapping resources, stuff spun up over the years, no real tagging, and costs creeping up because no one really knows what owns what.

trying to clean it up incrementally without tearing everything down. full rebuild isn't realistic right now.

main things i am focusing on:

1. finding unused or duplicate resources

2. standardizing naming and tagging

3. consolidating where it makes sense without breaking stuff

4. cutting cost on things nobody actually needs

Tried a few inventory tools but they mostly just dump everything without telling you what to actually do next.

What worked for you in situations like this, any scripts or just process that helped move things forward without causing downtime?

Everyone talks about processing fees, but honestly the bigger cost for us increasingly feels like the operational/admin side around it.

Support tickets, failed collections, reconciliation issues, chasing references, “did this go through?”, manual reviews, refund confusion, finance follow-ups, etc.

Feels like the actual transfer cost is sometimes the smallest part of the problem.

Curious what other teams see as the biggest hidden operational cost once transaction volume starts scaling.

I’ve been speaking with cloud and enterprise architecture teams, and one common theme keeps coming up: architects are no longer just designing systems.

They are expected to handle WAF-aligned designs, architecture documents, PRDs, Infrastructure-as-Code, cost estimates, cloud comparisons, security reviews, and stakeholder explanations — often across multiple clouds.

For Azure teams especially, the workload seems to sit across landing zones, governance, identity, networking, security, cost control, and documentation.

Curious how others are handling this.

Are architects in your organisation still focused mainly on design, or are they now expected to produce the full delivery package as well?

Full disclosure: we are building an AI agents to help cloud architects produce WAF-aligned designs, architecture documents, PRDs, IaC, and costing plans. Not posting this as a sales pitch — genuinely interested in how teams are handling this workload today.