Every agent turn is carrying tool_count=28 even after the router narrows the task to 4 relevant tools, so 24 schemas stay in the context window for no reason. We’re a 3 person team running 1800 sessions a week and each turn logs input_tokens=7600 from the schema block. The average session lasts 9 turns, which leaves 68400 schema tokens riding through each session. Our average session costs < $1 and the weekly < $1000. I checked the provider accounting and our request logs twice before blaming tool schema inflation, the numbers kept pointing back to the same block. 

The pruning estimate gets us to about $0.19 per session and $342 per week, but I don’t want to save money by making tool selection less reliable. We’re comparing Datadog and Braintrust for span level token attribution and trace filtering on tool_count, plus a clean task quality comparison before and after pruning so the input token cost doesn’t become the only metric that matters.

p95 cost should tell me whether the long sessions improve too and I’m testing whether the router can expose only the 4 selected schemas without breaking recovery when the first choice is wrong. Any suggestions on pruning tool definitions without making the agent worse at selecting a fallback appreciated

Hey i am starting cloud computing ik basic like linux, networking but i want to know the main cloud computing thing now.. I asked ai it suggested me aws, Microsoft and google free courses or something like that but I want some free video-oriented course where I can see what's going on and how to do this thing... Please help me thank you

Hey everyone! 😊 I’m a beginner learning Linux and currently struggling a little with How Linux Works by Brian Ward (3rd Edition). I’m looking for a study partner who’s also learning Linux or someone experienced who wouldn’t mind helping me understand the basics. It would be nice to learn together!

My local community college in collaboration with AWS has launched a B.S. in Cloud Computing and I was wondering if I were obtain it, would it be possible to pivot into Finance/Cloud/ERP roles? I have two years experience as a Financial Analyst. I’ve done some research in the field and find it interesting. Thank you.

25, graduated 2 years ago, 3 years at my current company.

Current developer job:

-Fully remote, 8–16, no on-call

-Recently got a raise, but no real growth path

-Full Home Office

-Great colleagues

Cloud engineer offer:

-Could learn more and grow career wise

-27% higher base, but no bonuses so about +10% total comp

-Yearly raises of ~10% + inflation through a step system

-2 days WFH, 3 in the office (40 minutes commute each way)

-Morning and afternoon shifts

-On-call rotation including weekends (in every 6-8 weekd)

Questions:

Would you take it?

What on-call setup is fine for you, and what are the red flags?

Has anyone left full remote for hybrid + shifts early in their career? Did you regret it?

I would like to have a career in cloud engineering and then hopefully one day become a solutions architect. For now, I have zero IT experience but I'm pretty nifty with tech and I can learn at a decent rate.

I just got my Network+ certification after studying for a month, and my plan is:

• Get AWS CLF-C02 (sort of speedrun it a bit, since it's mostly superficial material),
• Get AWS SAA-C04,
• Learn Linux basics while I'm learning for C04,
• Build a couple of projects as I'm learning and have them on Github,
• Get Terrarform Associate

Somewhere during that timeline, I would like to apply for entry IT jobs just to have some experience and not have an empty resume.

Would you tweak anything about my plan? My goal is to achieve a cloud role in 2-3 years from now, with 7 years being the goal for a Solutions Architect position.

I appreciate any advice that you might have for me, and I would love to get your input. Thank you.

Hey r/Cloud,

Reading docs and watching videos taught me what a NAT gateway or a route table does. It didn't build intuition for why a design breaks. So I built Tracesleuth, a browser game where you learn cloud design by breaking things safely.

How it works:

- Each stage is a realistic scenario, such as "the private app can't reach the internet" or "this pipeline costs too much".

- You build the design in 3D. The Region is the ground, Availability Zones are islands, the VPC is a glass enclosure, and subnets light up when they become public.

- You press Send traffic and watch packets travel hop by hop.

- When a packet is dropped, it stops at the exact hop that dropped it and explains why, with the concept behind it.

It currently covers AWS in six areas: networking, security, core architecture, data, AI and operations. It's also mapped to the AWS certification exams, if you're studying for one. A deterministic simulator decides pass or fail, so there's no AI guessing and no cloud account or bill.

Try it (free, no sign-up): https://beta.tracesleuth.com

The preview opens the first stage of each area. It's desktop only for now.

I'd really value your take on:

1. Does seeing the failure hop by hop help you understand the design, or is it just eye candy?
2. Were the scenarios realistic compared with what you see at work?
3. Anything confusing, clunky or broken? Screenshots help a lot.
4. Would you want Azure or GCP versions, and which one first?

Harsh feedback is welcome. It's far more useful than polite feedback. There's a feedback button in the game, or comment here. I'll reply to everyone.

Thanks!


I’d like to move into cloud engineering. My background is mainly on-prem enterprise IT infrastructure, with 15+ years of experience across Windows, RHEL, Active Directory, DNS, DHCP, PKI, VMware, MSSQL, networking, hardening, RMF, vulnerability management, PowerShell, Python, Jenkins, Bamboo, Docker, basic Kubernetes deployment, CI/CD pipelines, automation, and troubleshooting broken enterprise environments.

Certification-wise, I have A+, Network+, Security+, Project+, CySA+, ITIL, MCSA, SCCM, AZ-900, AWS Cloud Practitioner, ISC2 CCSP, and a bachelor’s degree in Cloud Computing.

There is no opportunities in my environment as cloud is handled by other teams and if you’re not a SWE they don’t see value.

I’ve applied

I’m confident I could finish the SAA, and I’ll probably do it, but I’m getting burned out on the idea that after 15+ years in infrastructure I’m somehow supposed to start over as a junior engineer just because my production environments have mostly been on-prem.
Sure, I could probably take a $50K–$70K pay cut and find someone willing to call me a cloud engineer, but at what point am I just massively underselling myself to satisfy an experience checkbox?

That’s really what I’m trying to figure out: how much more do I realistically need to prove before my existing infrastructure experience is considered transferable rather than irrelevant?

My plan is to get AWS Solutions Architect Associate, deepen my Linux skills and possibly get RHCSA, build out some cloud projects, and continue telling recruiters I’m “breaking into cloud” until somebody finally lets me get some actual production cloud experience.
At this point, how many more certifications do I need before I’m considered qualified to launch an EC2 instance?

If you're dealing with data sovereignty requirements or exploring how to run traditional VMs alongside AI workloads, this might be worth checking out. Topics include multi-tenant infrastructure with hard isolation, managing VMs and GPU workloads together, post-quantum crypto readiness, and GitOps-based day 2 operations. Focused on OpenStack Services on OpenShift, but the patterns are broadly applicable.

Hi everyone!

I’m feeling a little lost with my career lately, and I’d really appreciate some honest advice from people with more experience in the AWS ecosystem.

I’m 26, I have a degree in Systems Engineering, and around 5 years of general IT experience. With AI becoming increasingly common in our industry, I’m starting to worry about my future in IT. My current job is also becoming less secure, and I’ve realized that I’ve become way too dependent on AI tools.

Honestly, sometimes I feel like I’ve forgotten the fundamentals. I’m getting work done with AI’s help, but if I had to attend a junior-level technical interview tomorrow, I genuinely think I’d struggle to answer even some basic questions.

This has made me question my career path and whether I should continue in IT or consider a different direction.

One option I’ve been thinking about is going all in on AWS certifications, potentially getting several of them and building a stronger foundation in cloud computing. My current job involves some AWS, but my knowledge is still pretty limited, and I rely on Claude for a lot of the work.

My question is: Do you genuinely think pursuing AWS certifications in 2026 is still worth it? I’m not just talking about getting a certificate to put on my CV. I want to develop real skills, understand what I’m doing, and become employable without relying on AI for every other task.

If you were in my position, would you invest your time in AWS and cloud computing, focus on rebuilding your IT fundamentals first, or consider a completely different career path?

I’d really appreciate your honest opinions, especially from people who work with AWS professionally. I’m not looking for motivational quotes or someone to tell me everything will be fine. I genuinely want to make a smart decision about my future.

Thanks for taking the time to read this!

Hey! So I have joined cloud journey and have complete aws practitioner essential. The problem is I really don't know what should I do after that. Everyone saying make a github project of that and yes I did on server less so it wouldn't cost me anything. I am so confused like people saying do docker or some saying go for solution architect certification. I know network basics like ipv4 and ipv6, upd, tcp etc. I have also done one CI/CD deployment in my project. So guys help me please!!

Hey everyone!

​I’m a first-year data science student currently surviving on Python, Excel, analytics, and stats (goodbye basic calculus, you won't be missed lol).

​I just grabbed Cloud Computing: A Hands-on Approach by Arshdeep Bahga and Vijay Madisetti to figure out how all this backend magic actually works, but I could use a little direction from the community.

​As a total beginner to the cloud space, I’d love to know:

​Which cloud provider should I start with as a DS student? (AWS, Azure, or GCP?)

​What practical projects actually help bridge the gap between data science and cloud computing instead of just getting lost in documentation?

https://youtube.com/shorts/tKAbspOWU6o?si=vJ4Jp4qHNsegimmT

Hey everyone!

I have an upcoming interview with Snowflake for the \*\*Cloud Support Engineer – Application Security\*\* . My initial interview is scheduled with the Hiring Manager.

I'm trying to understand the interview process and prepare in the right direction.

For anyone who has interviewed with Snowflake for a Cloud Support Engineer, Application Security, or a similar support/security role, I'd really appreciate your insights.

1. How many interview rounds were there in your experience?

2. What topics should I focus on for the technical rounds?

3. Do they ask questions on IAM, SSO, SAML, OAuth 2.0, SCIM, MFA, and database authentication?

4. How much emphasis is placed on troubleshooting scenarios, incident management, and Root Cause Analysis (RCA)?

5. Should I expect questions on Snowflake architecture, SQL, AWS, Linux, networking, and security?

6. What should I expect during the initial Hiring Manager screening?

I've already started preparing authentication and authorization protocols, IAM concepts, Snowflake fundamentals, and troubleshooting scenarios. I want to make sure I'm focusing on the right areas rather than studying everything randomly.

\*\*If you've recently interviewed for a similar role, could you please share your experience and any preparation tips?\*\* Even a general idea of the interview structure would be extremely helpful.

Thanks in advance! I really appreciate any guidance