Avoid Cloud Storage Pricing Surprises with Rabata

Here's a quick AWS cost-saving tip: a single m5.xlarge EC2 instance running 24/7 costs roughly $140/month. But if your dev/staging environments only need to run during business hours (9am-6pm, Mon-Fri), that's just ~45 hours/week instead of 168. That one change alone saves you over $90/month per instance. Multiply that across your environments and the numbers add up fast. At CostGuard, we built automated scheduling to handle this for you — tag your resources, set your schedules, and stop paying for idle cloud. What's your biggest surprise when reviewing your AWS bill? Drop it in the comments. 👇 #CloudCost #AWS #FinOps #DevOps #CostOptimization

Most teams overpay for cloud storage without realising it. Not because they made bad decisions - because the default is expensive. $0.09/GB egress. Request fees on top of storage fees. Bills that grow every time your product gets used. We built Rabata specifically to break that model. Same S3-compatible API. Same SDKs. Swap the endpoint and you're done. What you actually get: → $0.01/GB storage → $0.01/GB egress — flat, no tiers → Zero request fees → No lock-in. 10 TB workload costs $153/month on Rabata. The same workload on AWS S3 costs $746. That's $7,116 back in your budget every year. 👉 See the full breakdown: https://lnkd.in/eiFHj77x #CloudStorage #FinOps #AWS #DevOps

Did 8 AWS audits yesterday. One finding was absolutely wild. Company: Series A SaaS Monthly AWS spend: $9,200 I found $4,100/month in waste. That's 45% of their entire cloud bill. The breakdown: - $1,800/month on over-provisioned EC2 instances (m5.2xlarge running at 8% CPU) - $1,200/month on duplicate data stored across 3 regions (should be 1) - $740/month on idle RDS databases from old staging environments - $360/month on unattached EBS volumes nobody knew existed They implemented Rackwyse recommendations this morning. Time to implement: 3 hours Annual savings: $49,200 Here's what mentally drowning: This isn't a technical problem. This isn't a skill problem. This is a TIME problem. CTOs know how to optimize infrastructure. They just don't have 6 hours to audit bills every month. So it doesn't happen. And $50k/year evaporates. I still have 4 audit slots open this week. Want yours analyzed? DM me your latest AWS/GCP/Azure bill. I'll turn it around in 24 hours. Completely free. #CloudOptimization #AWSCosts #TechCFO #SaaS

Concept: AWS Well-Architected Framework’s “Cost Optimization” pillar explained like fixing a leaky tap in your own home. 💸 Stop paying for cloud services you’re not using. Seriously. Last month, I helped a friend check their AWS bill. They were shocked. 😮 One tiny test server → left running for 6 months. No one used it. No one remembered it. But they paid for it. Every. Single. Day. That’s like leaving every light and AC on in your house… while you’re on vacation. 🌴 The fix? 🔍 Look for idle resources (EC2, RDS, Load Balancers) ⏰ Schedule start/stop times for non-production 🧹 Delete unattached storage (EBS snapshots pile up fast) We turned off that forgotten server → saved $47/month instantly. Took 3 minutes. That’s the #AWS Well-Architected way: Don’t pay for what you don’t need. Ever. Companies lose thousands this way. Not because cloud is expensive. Because no one looked. 👀 Look. Save. Repeat. #AWSCloudPractitioner #CostOptimization #CloudSavings #NoWastedMoney

Today felt like one of those “click” moments. I tried setting up basic load balancing using Nginx with a Mac and a Google Cloud instance. Everything seemed fine… until I added a limit: max_conns=300 I expected traffic to just flow to the next server. Instead: → Nginx stopped at 300 connections ✅ → Extra requests were simply rejected ❌ That’s when it hit me: Load balancing doesn’t mean scaling. It only distributes traffic across what already exists. So naturally, the next question was — what handles growth? That’s where Kubernetes comes in. Moments like this remind me—learning isn’t linear. It’s a chain. One limitation pushes you to the next concept 🚀 #Backend #SystemDesign #Nginx #Kubernetes #LearningJourney

I spent $600 a year on n8n cloud before I bothered to check the math. Then I ran the numbers properly. A VPS at $8 a month handles the same dozen workflows plus tens of thousands of executions. The gap was wide enough that I built a calculator so I could stop arguing with myself about it. Here's the rough framework I landed on: 1. If you're under 100 executions a month forever, stay on cloud. The VPS still costs $5+ when idle. 2. If your time is worth more than the setup hours, stay on cloud. Four hours at $50/hour is $200 of your time before you save a cent. 3. If you're at 1,000+ executions a month and comfortable with a terminal, self-hosting pays back in two to four months. The hidden upside nobody puts in the comparison: unlimited workflows, no execution caps, community nodes, full data control. The hidden downside: you're the sysadmin now. You will learn what an SSH key is. I plugged my real numbers in and the answer was obvious. Most people I talk to assume cloud is cheaper because it feels easier. The feel and the math don't always agree. What's your monthly execution count looking like, and have you actually priced the alternative? Read More ==> https://lnkd.in/gZYcEcG7 #n8n #automation #selfhosting #nocode #devops

I used to think AWS subnets were just "where my EC2 instances live." Turns out, I was *wildly* underestimating their power (and complexity!). Here's the super-short, need-to-know: Subnets are like digital neighborhoods within your AWS Virtual Private Cloud (VPC). They segment your network. * **Public Subnets:** Have a direct route to the internet. Great for web servers. * **Private Subnets:** Don't have a direct internet connection. Essential for databases, backend apps, anything sensitive. You choose which Availability Zone (AZ) a subnet lives in. This is HUGE for high availability. If one AZ goes down, your app in another AZ stays up. ✅ Think of it like building a house: you need different rooms (subnets) for different purposes, and you want them spread across stable ground (AZs). Want to dive deeper into route tables or NAT gateways? Let me know! \#AWS #CloudComputing #VPC #Networking #TechExplained

A great example of bringing together multiple inherited cloud environments that each operated differently. NHS England aimed to improve visibility into spend and usage, establish consistent tagging and reporting practices, and build a scalable FinOps model to support a much larger estate. The result? ✅ Significant commercial cost savings delivered within the first months of the new contract. ✅ Strong, scalable FinOps culture with clear team-level cost ownership. ✅ Unified AWS and Azure contracts offering clarity, stability and value for taxpayers. Read the full case study to see how Softcat helped: https://okt.to/ReIKAj

A great example of bringing together multiple inherited cloud environments that each operated differently. NHS England aimed to improve visibility into spend and usage, establish consistent tagging and reporting practices, and build a scalable FinOps model to support a much larger estate. The result? ✅ Significant commercial cost savings delivered within the first months of the new contract. ✅ Strong, scalable FinOps culture with clear team-level cost ownership. ✅ Unified AWS and Azure contracts offering clarity, stability and value for taxpayers. Read the full case study to see how Softcat helped: https://okt.to/pyh9La

Hey Folks !! In my last post, I spoke about why FinOps is becoming essential and how many of us are using cloud… without really understanding the cost behind it. But a question stayed with me: 💭 Even if we understand the problem… how do we actually deal with it? Because in reality: Costs don’t spike suddenly they grow quietly and often… without a clear reason. That’s exactly what I experienced — and it pushed me to explore how FinOps actually works beyond theory. 👉 From visibility to optimization 👉 From reactive fixes to structured thinking 👉 From Crawl → Walk → Run I’ve shared this through a real scenario and practical breakdown here: 📖 Latest blog: https://lnkd.in/dQGM9rNG If you haven’t read the previous one, I’d recommend starting there — it connects well with this. I’ll continue sharing more around FinOps, cloud cost optimization, and practical learnings from real scenarios. Also, exploring opportunities where I can contribute and grow further in this space. 💬 Curious to hear your experience: Have you ever seen cloud costs increase without any obvious change? What did you do about it? #FinOps #CloudComputing #CloudCostOptimization #Google #AWS