Free Kubernetes beginner course. Learn K8s in an interactive web based terminal with lessons, labs, quizzes, and flash cards. [Free / No email required / 100% mobile friendly]
https://redd.it/1v0us6z
@r_devops

How do your teams manage planning?

For those on platform or DevOps teams, what does your planning process look like?

We have no problem writing docs and roadmaps for product features, but it doesn’t always feel like it aligns with the rest of the business.

Do you have Product Design Docs? Technical design docs? Quarterly roadmaps? Or is it mostly just tickets? Curious how other teams handle it.

I feel like every time I try to do structured planning it just gets blown up in numerous ways.

https://redd.it/1v0vscq
@r_devops

NAT Gateway is somehow 80% of our AWS bill, how are you all keeping this in check?

We’re on AWS Activate credits and just realized NAT Gateway (hourly plus data processing charges) is eating up about 80% of our total AWS spend. Didn’t expect it to be anywhere near that high until we actually looked at Cost Explorer.

We know some of the usual fixes exist, like VPC endpoints for S3/DynamoDB, consolidating NAT Gateways in non-HA environments, maybe switching to NAT instances for low-traffic dev setups, but haven’t touched any of it yet and want to understand what actually works before we start ripping things apart.

For anyone who’s dealt with this:

\- Did VPC endpoints actually move the needle for you, or was it a small dent?

\- Has anyone swapped NAT Gateway for NAT instances and found it worth the extra ops work?

\- Is 80% just absurdly high, or is this more common than it feels right now?

Trying to figure out if we’re missing something obvious in our architecture or if this is just what NAT Gateway costs look like at our traffic level. Any pointers appreciated.

https://redd.it/1v0xgjh
@r_devops

How is CI/CD managed in enterprise environments?

Hi everyone,

I'm learning CI/CD and would love to understand how it's managed in enterprise environments.

A few questions:

Which CI/CD tool(s) do you use (GitHub Actions, Jenkins, GitLab CI, Azure DevOps, CircleCI, etc.), and why did your team choose them?
How do you structure your CI/CD pipelines across dev, QA, stage, and production environments?
Do you use separate CI and CD pipelines, or a single end-to-end pipeline? Why?
How do you promote artifacts between environments (rebuild vs. promote the same artifact/image)?
How do multiple engineers collaborate on pipeline changes (branching strategy, PRs, code reviews, approvals)?
What are the typical stages in your CI and CD pipelines (build, test, security scans, deployment, approvals, etc.)?
How do you manage environment-specific configuration and secrets?
What are the biggest lessons or best practices you've learned from running CI/CD in production?

I'm especially interested in real-world workflows using GitHub Actions, Jenkins, GitLab CI, Azure DevOps, or similar tools.

Thanks!

https://redd.it/1v0v1a4
@r_devops

The cost of completing a task dropped by roughly 10x after switching to an AI pipeline. Here's how I calculated it

I see a lot of posts along the lines of AI saved us a ton of money, but they almost never include actual numbers or explain how those numbers were derived. So I decided to break down my approach and be honest about where it gets shaky, because a 10x improvement means nothing without a baseline.



A quick disclaimer: this isn’t audited accounting. It’s a rough unit-economics estimate for a single task. The numbers are rounded, the logic is what matters.



For task cost, I used an average feature from our backlog: human time × hourly rate + AI expenses.



Before AI: 



planning \~1.5 hours, 



coding \~4 hours, 



review and fixes \~2 hours, 



plus the invisible tax of dragging context between Jira, Slack, and docs \~1 hour. 



Total: \~8.5 hours. At €60/hour, that comes to about €510.



With the AI pipeline: the human shifts from being the executor to being the controller. Agents handle planning → execution → review, and I only step in at the plan-review and code-review checkpoints. My actual time drops to \~1-1.5 hours. 



Total: \~€70. It’s not hard to calculate how much the cost decreased, right?



By the way, when I built this pipeline with roles and checkpoints in BridgeApp, the biggest savings came not from code generation itself, but from eliminating the manual work of moving context around.



A question for those already working this way: what happens when an agent makes a mistake? Does it get caught at the next checkpoint, or does it surface in production three weeks later?



For me, the entire 10x savings depends on errors being caught between stages rather than at the very end, but I’m not sure real life is ever that clean.

https://redd.it/1v0wdtm
@r_devops

What's your actual workflow when a test fails in CI but passes locally

Just curious, standard procedure, you have something working locally, you push your code and your CI/CD pipeline fails. Locally everything seems OK and you fail to reproduce the error.

What do you actually do at that point? Genuinely curious what people's real routine looks like here — not the textbook answer, the actual "it's 5pm and CI fails" routine 😅

Also wondering: is this even still a big thing in 2026 now that lockfiles and containers have gotten so good, or is it basically a solved problem for most of you?

https://redd.it/1v119en
@r_devops

How are you actually tracking token spend across your LLM API calls?

We've got OpenAI and Anthropic calls scattered across a handful of services now, and nobody can tell me month to month what's driving the bill until the invoice shows up. No real attribution back to which service or team is burning the tokens. Started looking at logging and a proxy setup since the vendor dashboards alone aren't cutting it.

https://redd.it/1v1by4l
@r_devops

Weekly Self Promotion Thread

Hey r/devops, welcome to our weekly self-promotion thread!

Feel free to use this thread to promote any projects, ideas, or any repos you're wanting to share. Please keep in mind that we ask you to stay friendly, civil, and adhere to the subreddit rules!

https://redd.it/1v1dk0c
@r_devops

I don't trust AGENTS.md as a secret-redaction boundary

I am getting less comfortable with using repo instruction files as a safety boundary.

They are useful for preferences: commands to run, folders to avoid, how to format the final answer. But if an agent can read config files or logs, I do not think "please redact secrets" in a markdown file is enough.

In one small debug task, I asked the agent to inspect config safely, and I still felt nervous when it started summarizing values instead of just saying which setting was wrong. Nothing bad happened, but it made the boundary feel too soft.

A rule file can remind the agent to be careful, but it cannot be the thing that catches the secret after the agent already saw it.

For people running agents near CI, logs, or config: do you handle redaction at the tool/runtime layer, or do you rely on instructions and review?

https://redd.it/1v1fihb
@r_devops

How do you save your secrets?

I'm curious to know how different developers save their API keys, tokens and anything that is used in their web apps and projects, do you store them in notepad like me or have a more elegant way to do things? :)

https://redd.it/1v1mqwe
@r_devops

Supabase IOS Tool
https://apps.apple.com/us/app/supacom/id6788561799

https://redd.it/1v1qvar
@r_devops

Your CI fails but passes locally, would debugging/shelling inside the CI environment actually help?

I’m exploring a devtool idea around this and I’m looking for brutally honest feedback, not encouragement.


Say a test/build fails in CI, but passes locally. You can’t reproduce it immediately, probably, so you end up digging through logs, rerunning, adding debug output, tweaking workflow files, trying act, SSH/self-hosted runners, etc.

I’m trying to understand whether direct debugging inside the actual CI runner/container would genuinely help, or whether this is mostly solved well enough with logs, artifacts, containers, and better workflow design.

If you’ve had this happen recently:

1. What exactly failed?
2. How long did it take to debug? How much time did you lose?
3. What did you actually try?
4. Would being able to attach a debugger to the actual CI environment have helped in that specific case?
5. Would you try a rough prototype next time this happens, or is this not painful enough?

Please be blunt. “This is annoying but I wouldn’t use it” is a useful answer.



https://redd.it/1v1tl5z
@r_devops

[meme] IaC and docs matter folks!
https://redd.it/1v1w770
@r_devops

Cost attribution keeps finding waste that monitoring dashboards miss - how do others handle the gap?

I work on cost attribution for a large internal cloud fleet, and a pattern keeps repeating: our observability stack says everything is healthy, and the invoice says otherwise.

Recent example: a batch worker that was fully green in monitoring — no errors, no alerts, normal resource graphs - but was costing \~$4,200/month sitting mostly idle because it was provisioned for a peak workload that moved to a different pipeline two quarters ago. Nobody's dashboard is designed to surface "this thing is fine but shouldn't exist."

What I've landed on so far:

* Attribution before optimization. Tag/label hygiene and per-team allocation first, because until spend maps to an owner, "reduce costs" is nobody's job.
* Treating the invoice as a second architecture diagram. The design doc shows what the system is supposed to be; the bill shows what it actually is. Divergence between the two is where the waste lives.
* Idle detection as its own signal, separate from utilization alerts — low-but-nonzero usage is the expensive failure mode, because it never trips anything.

Where I still don't have a good answer: shared infrastructure. NAT, load balancers, control planes, observability itself - the stuff that serves everyone and therefore belongs to no one. We've tried proportional split by traffic and even split by team, and both create weird incentives.

For those doing FinOps/cost work at any scale: how do you attribute shared platform costs without either (a) burying teams in chargeback complexity or (b) making platform costs invisible again?

https://redd.it/1v1wa6m
@r_devops

Do devils use ai workflows in enterprise

I’m building one personal project, a platform where DevOps can create ai workflows without coding(drag and drop/ chatbot) , like n8n but for DevOps. But firstly I want to ask you guys who is working in the industry, some questions to get clarity what to build.
A example of the workflow can be taking data from the aws cloud watch for perticular project and if there’s any perticular event which needs attention then send email for summary of that to user.

1. what are the tasks you have already automated ??

2. Do you guys trust ai for reasoning over any infrastructure related problems ??

3. What are the tasks that can be repeated and can be done by ai ? if you have any idea.

4. What are the platforms you use for these tasks.(Ex. Aws cloudwatch, Jenkins, Prometheus, Grafana,etc)

5. Can this project be helpful in the field

Open for discussion.
Thank you!!

https://redd.it/1v1tra1
@r_devops

If you had a 300M parameter model, what would you optimize it for?

Im working on AI infrastructure and have been thinking about where small language models actually make the most sense.

Suppose you had a 300M parameter model and your goal wasnt to compete with large frontier models at everything, but instead to consistently outperform much larger models (2B–20B) on one specific use case.

What would you optimize it for?

A few ideas that came to my mind:
Code generation for a narrow domain
Structured data extraction
Document classification
Workflow or agent planning
Log analysis
Something else entirely

I’m less interested in benchmark scores and more interested in real-world workflows where a small model could genuinely be the better choice because of specialization, latency, reliability, or deployment constraints(but ofc i also want benchmark scores to be good too lol).

If you had to pick one domain where a highly specialized 300M model could become the obvious choice over much larger models, what would it be, and why?

https://redd.it/1v1wrxd
@r_devops

I need a career

Last month, I sent a professional-grade DAM (Desarrollo de Aplicaciones Multiplataforma) in Spanish. But I don't know what to do now. I think I have to study more, but I don't know if I should pursue an engineering degree or just take courses and specializations.

https://redd.it/1v2eoyn
@r_devops

How are you setting up build systems in monorepos?

Former DevOps consultants, bootstrapped an AWS premier partner and exited to NTT Data, now we advise people building services companies.

After seeing how much easier current AI tools have made it to write code we started building tools for helping our customers and day to day work. Some of the decisions we've made so far:

1. Build out the code in separate services to keep each part well-contained. It feels AI does a far better job with smaller pieces than with larger.
2. Deploy them through lambda.
3. Use a monorepo so the AI can easily see how different things work together.
4. Migrated some apps built in Lovable into our repo.
5. All the building is done with Makefiles
6. Created a registry so it is easier for AI to add new tools in a consistent manner and so we can analyze our inventory of what exists in our development.
7. Using make for building cuz Claude made that decision when we barely had anything and I'm a dinosaur at heart.

Now, I have a monorepo with multiple different languages, a registry that I want to be the single source of truth for what I have, and a build system from the 80s. And I was curious what would people recommend for the build system rather than building a whole bunch of custom scripts?



https://redd.it/1v2ppv2
@r_devops

Good observability tool recommendation - Cloud Based

I work in two companies, one very big that have a giant budget to spend on Datadog, and another small one, that don't have that very big budget.

On the small company we are looking to migrate from Prometheus + Grafana + Alert Manager to something cloud based. Mostly because we are a small team (3 people only) and we don't have time to spend maintaining the infrastructure for it to run.

Now, is there some good alternative to Datadog? Datadog is the king, but is brutally expensive.

I've tried Signoz, looked promising, but is bad. They documentation is bad, there are just general ideas without details (they assume that you know a lot from I don't know where), they support is lame (they only have a chat to "Interact with a Human", that takes days to receive an answer, and their UI is buggy. Really bad experience.

But looking there, they are the only ones having like "close" experience to Datadog. We tried New Relic in the past, but they have all metrics and log scattered over the place without correlation. Also tried BetterStack but lacks a lot of features compared to other things.

So, is there some good observability platform cloud-based that I'm not aware of, outside Datadog? I'm not completely closed to self-hosting as long as it takes low effort to maintain.




https://redd.it/1v34gf2
@r_devops

DevOps Engineer with ~1 year of experience, but I feel like I'm not learning enough. What should I focus on?

Hi everyone,

I'm a DevOps Engineer with about 1 year of experience, but I feel like I'm not getting enough real-world exposure at work.

Most of my work is around CI/CD, AWS deployments, CloudFront, Docker, CloudFormation, Linux. I haven't had much experience with Kubernetes, production incidents, scaling, networking, or designing infrastructure.

What should I learn next to become a better DevOps/SRE engineer? Any roadmap, projects, or resources you'd recommend for gaining practical experience?

Thanks!

https://redd.it/1v37qh0
@r_devops