We live in the age of cloud-first startups. With just a credit card and a few hours, you can spin up infrastructure powerful enough to run a small nation. But the same flexibility that makes the cloud irresistible to startups can become a financial black hole especially when you’re moving fast and watching metrics, not bills.
That’s exactly what happened to us.
In our first 18 months, cloud costs went from being a rounding error to our second-largest operating expense, right after salaries. We were burning money on overprovisioned compute, idle volumes, and workloads running at full throttle 24/7 even when no one was using the app. That’s when we decided to take cloud cost optimization seriously.
The Fluence team recently published a https://fluence.network/blog/cloud-cost-optimization-best-practices/ It resonated with us because we’d lived it. What follows is a mix of Fluence’s expert insights and our own hard-earned lessons especially relevant for lean teams, bootstrappers, and anyone operating outside high-funding bubbles.
We used to treat cloud bills like electricity: something necessary, predictable, and a little boring.
But cloud spend isn’t passive. It reflects every single decision your team makes from how they deploy to how they test, from what they monitor to what they forget. That’s why Fluence’s first recommendation building a FinOps culture is so powerful. Here is what worked for us:
- Cost ownership: Every team had to “own” the cost of their environments.
- Tag everything: We added cost-center tags to every resource, linked to teams and projects.
- Surface costs: We embedded cost data into our engineering dashboards using AWS Cost Explorer APIs.
- Weekly reviews: Just 20 minutes each week to go over top offenders made a massive difference.
And the most impactful move?
Teaching engineers that performance and cost are two sides of the same coin.
If you’re in an emerging market like Africa or Southeast Asia, the dollar-denominated nature of cloud services means costs scale twice as painfully. Building cost discipline into your engineering culture early is one of the few levers that compound positively over time.
We used to size every server for worst-case traffic. you know, just in case we get on Product Hunt. Reality? 95% of the time, we were running at 15% CPU.
Auto-scaling wasn’t just a luxury. It was a lifeline.
We paired Kubernetes HPA (Horizontal Pod Autoscaler) with real-time metrics to dynamically scale microservices based on demand. In lower-stakes environments (dev, staging, QA), we went further and moved workloads to Fluence Virtual Servers. significantly cheaper and just as performant for non-critical use.
The result? A 40% drop in compute cost in 6 weeks.
Bonus Insight:
Auto-scaling isn’t just about saving on compute. It triggers secondary savings in storage, IOPS, networking, and managed services like databases. That’s the “hidden compounding” no one tells you about.
There are two kinds of startup storage:
1. The data you need to serve customers.
2. The junk you’ve accumulated from every test, backup, log, and misconfigured script since you launched.
Guess which one costs you more?
Fluence’s third best practice storage audits and lifecycle policies was a game-changer. We ran our first audit and found:
- EBS volumes no one had mounted in months
- Old container snapshots eating hundreds of gigabytes
- Gigabytes of CI/CD logs we hadn’t touched in 90 days
- We implemented a tiered storage strategy:
- Hot data (active): SSD-backed gp3 volumes.
- Warm data (used monthly): S3 Standard-IA.
- Cold/archive: Moved to S3 Glacier with lifecycle policies.
And most importantly, we wrote Terraform modules that delete unused volumes and obsolete buckets automatically.
Monthly savings? About 18%. But more importantly, we stopped the clutter from piling up again.
Many startups ignore spot instances because they seem risky. They can be interrupted with little notice and that’s scary if you’re running customer-facing systems.
But if you know when and how to use them, spot instances can cut costs by up to 90%.
We used them for:
- CI/CD pipelines
- Video transcoding
- Internal batch jobs
- Staging servers
For production systems, we took a hybrid approach:
- Reserved instances for APIs and databases with predictable load
- Spot capacity managed by tools like https://spot.io for flexible workloads
- Fluence infrastructure for services where cost and control mattered most (e.g., job queues, test agents, and task runners)
If you’re a startup with elastic demand, you must build a pricing model that includes all three strategies. Locking into one vendor’s full-price instances is not a business model — it’s a tax.
Here’s the part no one wants to hear: Your cloud costs aren’t just about usage. They’re about architecture.
Fluence’s fifth recommendation to periodically review and rethink your architecture is where you unlock step-change savings.
We learned this when we split our monolith into containerized microservices, each deployed to Kubernetes. This made it easier to:
- Move parts of our stack to edge locations
- Run job workers on cheaper infrastructure
- Use serverless functions (AWS Lambda, Google Cloud Functions) for spiky workloads like email and image uploads
- Replace PostgreSQL with DynamoDB for high-read/low-write services
Most impactful of all? We moved several workloads to Fluence’s decentralized compute platform, cutting infrastructure costs by 70 — 85% with no performance penalty.
Fluence is especially useful for:
- Web servers
- Staging environments
- Dev/test clusters
- Elastic job queues
- Compute-heavy scripts (AI training, image/video processing)
Their transparent pricing model and absence of lock-Web servers
in made it easy to test and adopt. And in our case, the ROI was immediate.
Final Thoughts: Cloud Waste Is a Choice
Startups operate on tight margins and tighter timelines. But that’s exactly why cloud cost optimization matters more for us than for enterprises.
It’s not just about saving money. It’s about:
- Shipping faster
- Staying lean
- Being financially sustainable
- Freeing up budget for product, growth, and talent
And perhaps most importantly, it’s about building a culture of operational excellence from the very beginning.
Start Saving Today
If you’re serious about optimizing your cloud setup, I strongly recommend starting with Fluence’s full post:
👉 https://www.fluence.network/blog/cloud-cost-optimization-best-practices/
And if you’re ready to take the next step, try Fl uence Virtual Servers for your dev, CI/CD, and elastic workloads. We’ve saved thousands of dollars already and we’re not going back.
💡 Pro tip: Test Fluence on non-critical workloads first. You’ll be surprised how much you saveand how quickly.
Want help applying these practices to your startup?
I’m happy to share what worked (and what didn’t) for us. Reach out via. (https://x.com/fluence_project?s=21 )or drop a comment below.👇🏽
Let’s stop burning budget and start building better infrastructure together.
#Depin #cloudcost #Flencenetwork
