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The database industry has undergone a quiet revolution over the past decade.
Traditional databases required administrators to provision fixed capacity, including both compute and storage resources. Even in the cloud, with database-as-a-service options, organizations were essentially paying for server capacity that sits idle most of the time but can handle peak loads. Serverless databases flip this model. They automatically scale compute resources up and down based on actual demand and charge only for what gets used.
Amazon Web Services (AWS) pioneered this approach over a decade ago with its DynamoDB and has expanded it to relational databases with Aurora Serverless. Now, AWS is taking the next step in the serverless transformation of its database portfolio with the general availability of Amazon DocumentDB Serverless. This brings automatic scaling to MongoDB-compatible document databases.
The timing reflects a fundamental shift in how applications consume database resources, particularly with the rise of AI agents. Serverless is ideal for unpredictable demand scenarios, which is precisely how agentic AI workloads behave.
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“We are seeing that more of the agentic AI workloads fall into the elastic and less-predictable end,” Ganapathy (G2) Krishnamoorthy, VP of AWS Databases, told VentureBeat.”So actually agents and serverless just really go hand in hand.”
Serverless vs Database-as-a-Service compared
The economic case for serverless databases becomes compelling when examining how traditional provisioning works. Organizations typically provision database capacity for peak loads, then pay for that capacity 24/7 regardless of actual usage. This means paying for idle resources during off-peak hours, weekends and seasonal lulls.
“If your workload demand is actually just more dynamic or less predictable, then serverless actually fits best because it gives you capacity and scale headroom, without actually having to pay for the peak at all times,” Krishnamoorthy explained.
AWS claims Amazon DocumentDB Serverless can reduce costs by up to 90% compared to traditional provisioned databases for variable workload …