Okay, hear me out on this one. I've been diving deep into our AWS bills while learning FinOps, and I'm starting to think the conventional wisdom around Savings Plans might be wrong for a lot of teams.
Everyone says SPs are the flexible upgrade to Reserved Instances (RIs). But the more I look at it, the more I feel like the "flexibility" is an illusion. With an RI, I knew exactly what instance type I was committing to in a specific region. If my needs changed, I could still sell it on the Marketplace (admittedly, a hassle). With Savings Plans, I'm committing a dollar amount to a *family* of services. It feels like I'm giving AWS a blank check for my flexibility, and they're giving me a discount that's often less than what a well-chosen RI could get.
I'm still new to this, so I'd love a detailed walkthrough from someone who's managed both. Specifically:
- How do you accurately forecast that steady-state compute usage a year out, especially when projects can pivot?
- Once you're locked into a Savings Plan, doesn't it discourage exploring other instance types or even other services because you want to "use up" your commitment?
- For a team heavy on R&D and prototyping, are we just better off with a smaller set of targeted RIs for baseline workloads and using Spot/On-Demand for the rest?
I've seen the savings numbers AWS shows, but I'm worried about the hidden cost of lost agility. Would love to hear your real-world experiences and maybe some before/after breakdowns if you have them!
You're touching on something important, but I think you're understating the RI problem. The Marketplace is largely theoretical for most organizations. Selling requires finding a buyer with identical, non-flexible needs at the exact moment you need liquidity. It's a massive operational friction.
Your real question about forecasting is the key. Savings Plans demand a prediction of your *aggregate* compute dollars, which is arguably harder than predicting specific instance usage. If you prototype heavily, your dollar-weighted usage profile is chaotic. An RI for a known, stable workload like a database is a precise tool. A Savings Plan for variable R&D is a bet on your own budget accuracy.
It doesn't just discourage exploring other instance types, it actively penalizes you. Every dollar of compute that falls outside your committed family (e.g., moving some batch jobs to Spot, trying a Graviton instance) feels like wasting the plan. You've traded the rigidity of a specific instance for the rigidity of a spending corridor.
James K.
You're asking the right questions, especially about forecasting and R&D. That's where the rubber meets the road.
For your second point: yes, that's a real psychological trap. Once you've bought a large Compute Savings Plan, there's an invisible pressure to stick with EC2, even if Fargate or Lambda might be a better architectural fit for a new prototype. You start optimizing for cost coverage instead of the right tool.
Your final question hits it: for a heavy R&D team, pure SPs can be a bad fit. The strategy I've seen work is a mix - RIs for your known, stable foundations (databases, core services) and a smaller, flexible SP layer to cover your variable baseline. It lets you prototype without feeling like you're wasting a commitment.
Keep it real