If you're a cloud architect, SRE, or DBA, you already know DynamoDB delivers great performance at very large scales. It runs some of the world's most demanding applications for a reason.
But even before your tables reach hundreds of terabytes, a different set of challenges emerges around protection, cost, and what your backup data can actually do for you. A DynamoDB backup becomes a complex, expensive part of your job, and a real source of anxiety when you think about what a restore would actually cost.
The sections below walk through the real challenges of backing up large DynamoDB tables with native tooling, how a posture-based approach from Eon changes the math, and why that math now matters to your AI budget.
Why DynamoDB Backup Costs Became an AI Budget Problem
Every company has an AI roadmap now. Few have the budget headroom to run it because cloud spend keeps climbing as AI workloads go into production and teams ship apps faster than finance can keep up with.
So the cost conversation has widened. CFOs and CIOs want control over total cloud spend before they green-light the next AI initiative, and they want to know exactly where the recovered budget will come from.
Governance sits inside the same conversation. You can't control spending on resources you can't see, which is why visibility and posture come up in the same meetings as cost.
At 300 TB of DynamoDB, backup storage is one of the cleanest places to recover that budget. Eon typically cuts that infrastructure cost by 40% or more, so the platform pays for itself through savings, with AI readiness as the strategic payoff. In practice, the CIO's cost-reduction project and the CDO's AI-readiness project turn out to be the same project.
What Makes Backing Up Huge DynamoDB Tables So Difficult?
Well before reaching >100 TB, native tools like AWS Backup show limitations across four key areas: cost, restore predictability, operational overhead, and data accessibility. Smaller tables are straightforward, but as data volumes grow, the dynamics shift, and each operational decision gets more complex.
Runaway Costs with Native Tools
AWS Backup pricing scales with the full size of your table. At hundreds of terabytes with realistic retention, the annual bill can run into the seven figures.
Teams facing that invoice often do one of two things: cut retention to stay within budget or skip DynamoDB backups altogether and accept the risk. Either way, money and risk pile up in a corner of the cloud bill nobody reviews.
The Restore-Cost Nightmare
The part that keeps teams up at night is the unknown cost of a full DynamoDB restore. At hundreds of terabytes, a single restore can run hundreds of thousands of dollars, sometimes more. The bill is unpredictable, and you only find out the exact number after you've already committed to the recovery.
Operational Drag
Figuring out how to back up DynamoDB tables at scale adds real operational overhead. You're managing plans, retention policies, cross-region copies, and tagging drift, and you're doing it per account.
Compliance and Resilience Gaps
Modern enterprises need more than a backup. They need air-gapped, immutable copies for ransomware protection and recovery plans that work when a region goes dark.
Native tooling gets thin here. Cross-region recovery in particular depends on metadata and plan configurations that may not be readily accessible if your primary region is the one that went down.
Lost Historical Insight
Your backup data holds real analytical and AI value, but a native DynamoDB backup is an opaque blob. You can't query it to spot trends or investigate what changed without performing a full, costly restore.
So while your CDO hunts for historical data to feed AI projects, older versions of your largest tables sit locked away, useful only in a worst-case scenario.
How Much Can Enterprises Save on DynamoDB Backups?
For example, at 300 TB, enterprises save roughly $500,000 per year, about 50% of their AWS Backup spend, by moving to Eon.
Based on standard pricing, the projected annual cost to back up a 300 TB DynamoDB table with AWS Backup is about $1.4 million. With Eon, the same 300 TB workload, at identical retention and frequency, runs about $652,000 per year.
The savings don't come from cutting retention, slowing restores, or weakening compliance. They come from a different pricing and storage model: Eon's proprietary compaction and cross-dataset deduplication engine continuously trims the storage layer, and the bill follows that compacted, deduplicated footprint rather than raw table size.
The 50% reduction for this workload also tracks with what Eon customers see across EC2, RDS, S3, and other workloads: 40-50%+ storage cost savings. And half a million dollars a year recovered from one table is the kind of line item that gets an AI initiative funded.
How Eon Handles 300 TB DynamoDB Table Backups
We recently worked with a company that runs multiple DynamoDB tables in the 180-300 TB range. Backup costs were climbing fast, and the operational load on their team was becoming untenable. Their requirements were strict: 30-day retention, with a backup every four hours.
Eon handled the workload as part of its broader Cloud Backup Posture Management (CBPM) platform, which continuously discovers, classifies, and applies policy to cloud resources across AWS, Azure, and Google Cloud. For DynamoDB specifically, the relevant mechanics are:
- Incremental backups via DynamoDB streams. After the initial backup, Eon only captures what changed, using the underlying DynamoDB streams mechanism. Incremental capture slashes the volume of data stored for each subsequent backup.
- Frequent backup cadence with PITR. Eon supports backup frequencies down to every 15 minutes for DynamoDB, with built-in point-in-time recovery, so you can dial in the right RPO without paying to store full snapshots every cycle.
- Air-gapped, immutable vault by default. Every backup lands in a logically air-gapped, immutable vault, so ransomware, insider threats, and accidental deletions can't touch it.
- Compression is built into the pipeline. Eon compresses data on the way into the vault, and pricing is based on post-compression size, so the bill reflects what you actually store.
- Queryable backups in open formats. Eon stores backups in Iceberg and Parquet; they work together in layers, so you can mount any point-in-time backup and run SQL or analytics directly against it, with no full restore and no ETL pipeline to build. Engines such as Databricks, Snowflake, BigQuery, and Athena can read tables natively. The same schema-level visibility that Innago validated on PostgreSQL during their POC applies to DynamoDB as well.
- Granular, record-level restores. Restore a single record or a scoped dataset rather than an entire table, and avoid the budget-destroying full-table restore. The same granularity applies to AI incident recovery: when a coding agent deletes records or a runaway write corrupts a dataset, you can roll back the affected slice in minutes.
- Cross-region policy enforcement. Applying backup rules to another region is a policy setting, not a scripting exercise.
- 100% agentless deployment. Eon connects through cloud-native APIs, with zero sidecars, daemons, or per-account configuration.
What the Customer Achieved
Shifting to Eon turned their DynamoDB backup workload from a cost problem into a useful asset:
- Incremental backups of their 300 TB environment, running every four hours without manual intervention.
- Up to 50% lower annual backup spend compared with AWS Backup.
- No more manual scripting, thanks to Eon's policy engine.
- Automated retention and immutable, air-gapped storage that supports compliance frameworks like SOC 2, PCI, and GDPR.
- Confidence that they can recover what they need quickly, without triggering a seven-figure restore event.
How to Simplify Your DynamoDB Backups Today
If you're backing up DynamoDB tables above 100 TB, the current approach probably isn't holding up. The cost is volatile, the restore is a black box, and the backup data itself stays frozen until a disaster forces you to thaw it.
Eon's posture-based, agentless platform can cut DynamoDB backup costs roughly in half while eliminating most of the manual overhead. And the backups themselves become data your AI and analytics teams can query from day one, which is the idea behind AI-Ready Infrastructure: protect the entire data estate and make it instantly queryable for AI, in the same deployment.
Request an Eon demo to see what the numbers look like in your environment.
Frequently Asked Questions
Is AWS Backup good for DynamoDB?
For smaller DynamoDB tables, AWS Backup is a reasonable default. It's native to AWS, straightforward to configure, and the costs stay manageable.
Once you're above 100 TB, the story changes. Pricing scales with full table size, restores are all-or-nothing, and there's no native way to query or investigate the data inside a backup. The economics and the operational model both break down at scale.
Can Eon replace AWS Backup for DynamoDB at scale?
Yes, and for large DynamoDB environments, it's a material upgrade over AWS Backup.
Eon's pricing is usage-based on post-compression storage rather than fixed against full table size, and usage-based pricing accounts for most of the cost gap at scale.
Beyond pricing:
- 100% agentless, with zero appliances and zero hidden restore fees.
- Granular restore and queryable visibility are built into the core, never sold as add-ons.
- Cross-region policy enforcement comes standard.
- Backup-as-a-data-lake: your historical data feeds AI and analytics instantly, never locked behind a restore.
