Unused DynamoDB Capacity: Our $2,300 Monthly Ghost Bill

That was the question that landed in my inbox, bright and early Monday morning. Our monthly AWS bill had just landed, and staring back at us was a line item for DynamoDB that had inexplicably jumped by $2,300 from the previous month, with no corresponding increase in application usage. My stomach churned. This wasn't a one-off spike; it was a persistent, silent drain on our cloud budget, a 'ghost bill' that nobody could explain or justify.

Our teams, like many, relied heavily on DynamoDB for its scalability and performance. When new projects kicked off, developers would often provision capacity generously – better safe than sorry, right? They'd set high Read Capacity Units (RCUs) and Write Capacity Units (WCUs) with the expectation of future growth or burst traffic. The problem was, for many tables, that growth never materialized, or the burst traffic was infrequent, leaving expensive, pre-allocated capacity sitting idle, burning cash 24/7.

We were dealing with dozens, if not hundreds, of DynamoDB tables across multiple AWS accounts. Manually sifting through CloudWatch metrics for each table, comparing provisioned vs. consumed capacity, was a Sisyphean task. It was like trying to find a needle in a haystack, only the needle was made of money, and the haystack was constantly growing. The sheer volume and dynamic nature of our environment made this seemingly simple problem a persistent headache for our FinOps and engineering teams.

Chasing Ghosts with Manual Tweaks

Initially, our approach was reactive and manual. When we'd spot a DynamoDB cost anomaly, we'd dive into the individual table's CloudWatch metrics. We'd see a table provisioned for 1,000 RCUs and 500 WCUs, but its actual consumption might hover around 50 RCUs and 10 WCUs for weeks on end. The solution seemed obvious: lower the provisioned capacity.

However, this was easier said than done. First, it required significant manual effort for each identified table, taking engineers away from developing new features. Second, there was always the fear of 'breaking production.' If we accidentally set the capacity too low for a table that *did* experience bursts, it could lead to throttling, application errors, and a flurry of angry alerts. This risk often led to conservative adjustments, or worse, no adjustments at all, simply pushing the problem down the road.

We tried implementing better tagging and cost allocation strategies, but while that helped us identify *which* tables were costing money, it didn't solve *why* they were costing so much for so little usage. The core issue wasn't knowing who owned the cost; it was knowing that the cost itself was unnecessary, and having a safe, scalable way to fix it. We also experimented with basic autoscaling policies, but they often reacted slowly or over-provisioned during low periods, failing to truly optimize for the sporadic, unpredictable nature of many of our workloads.

EazyOps: Automating the DynamoDB Cost Clean-Up

This is where EazyOps stepped in, transforming our reactive firefighting into proactive, intelligent cost optimization. We needed a solution that could not only identify these hidden pockets of waste but also recommend and, critically, *safely implement* the necessary changes at scale.

EazyOps began by ingesting our AWS billing data and CloudWatch metrics across all accounts. Its algorithms immediately started flagging DynamoDB tables where the provisioned capacity significantly dwarfed the actual consumed RCUs and WCUs over a sustained period. It didn't just point out high costs; it analyzed usage patterns, distinguishing between genuinely high-traffic tables, those with predictable but lower usage, and the 'ghost bill' tables with sporadic or negligible activity.

For tables exhibiting highly variable or consistently low request rates, EazyOps provided clear recommendations: convert to On-Demand billing mode. The platform would present the projected savings, mitigating the 'fear of change' by providing data-driven confidence. For tables that truly benefited from provisioned capacity but still had headroom, EazyOps analyzed historical spikes and troughs to fine-tune Auto Scaling policies, ensuring they scaled efficiently without over-provisioning during idle times or causing throttling during peak loads. The beauty was in the automation and the data-backed recommendations, turning weeks of manual review into a few clicks.