DynamoDB

AWS-only managed key-value and document database with fixed per-partition throughput limits and no ad hoc queries.

As of 30 August 2026, DynamoDB has a free plan and paid plans start at $0.625/per million writes. DynamoDB gives predictable single-digit millisecond reads at any scale, provided every access pattern was designed into the key schema before launch. Softwr lists it under Databases. DynamoDB is made by Amazon Web Services, launched in 2006.

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Overview

What DynamoDB does

DynamoDB is Amazon's managed NoSQL database. Items are stored in tables and located by a partition key, optionally with a sort key, and DynamoDB spreads partitions across storage nodes automatically as data and traffic grow. Reads are eventually consistent by default with strongly consistent reads available at twice the cost, writes are atomic per item, and transactions can span up to 100 items. Secondary indexes come in two forms: global secondary indexes, which are separate replicated copies with their own throughput and their own eventual consistency, and local secondary indexes, which must be created with the table. It includes point-in-time recovery, on-demand backups, DynamoDB Streams for change data capture, global tables for multi-region replication, TTL-based expiry and an in-memory accelerator, DAX. Capacity is either provisioned with autoscaling or on-demand. The distinguishing property is that performance does not degrade with data volume, because every operation is a hash lookup into a partition rather than a query against an index whose depth grows. A table with ten billion items answers a keyed read as fast as one with ten thousand. That is what makes DynamoDB the right answer for workloads where the traffic profile is unknowable in advance, and it is why so much of AWS's own infrastructure sits on it. The price of that guarantee is that you must trade query flexibility for it up front: the key schema is the query plan, and it is chosen before the first row is written. The buyers are teams already committed to AWS with well-understood, high-volume access patterns: sessions, carts, device state, event ingestion, user profiles. The trade-off is twofold. Operationally, access patterns discovered later require a new global secondary index, which is a full extra copy of the projected attributes billed as both storage and writes, or a migration. Commercially, DynamoDB runs only on AWS and its API is proprietary, so the data layer of your application cannot be moved without rewriting it; ScyllaDB's Alternator interface is the only meaningful compatible target, and it is a smaller ecosystem. That is a deep dependency to accept for a component as central as the primary datastore.

What people use it for

High-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and known
Traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercise
Serverless applications on Lambda, where an HTTP-based datastore avoids the connection pooling problem relational databases have
Event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifier

Where it falls short

Concrete and checkable, so you can decide whether any of them matter to you. This is the half of a review a vendor will not write about DynamoDB.

Access patterns must be designed into the key schema before launch; a query nobody anticipated needs a new global secondary index, which is a full extra copy of the projected attributes billed as storage and as writes, or an offline migration.
Global secondary indexes are eventually consistent and cannot be read strongly, so a read-after-write against an index can legitimately miss the item that was just written, and application code must be written to tolerate that.
Per-partition throughput is capped at roughly 3,000 read and 1,000 write units, so a hot key throttles even when the table has spare capacity overall, and the only real fix is changing the key design to spread the load.
Items are limited to 400 KB and query results paginate at 1 MB, so large or list-shaped data has to be split, offloaded to S3 with a pointer, or read through pagination loops that complicate every consumer.
It runs only on AWS and the API is proprietary rather than a standard, so moving the data layer means rewriting it; ScyllaDB's Alternator is the only meaningfully compatible target and it brings a much smaller ecosystem.

Cross-shopped

What people choose instead of DynamoDB

Each pairing was judged by two reviewers asking whether a buyer would genuinely weigh the two against each other. The ones that failed were deleted rather than published.

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Airtable
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Amazon Aurora
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Pricing

What DynamoDB costs

Taken from the vendor's own pricing page. Prices move, so check before you buy.

On-Demand Capacity

On request

Pay-per-request pricing with automatic scaling
Read: 0.5 RRU per 4 KB (eventually consistent), 1 RRU per 4 KB (strongly consistent), 2 RRU per 4 KB (transactional)
Write: 1 WRU per 1 KB
$0.125 per million reads (Standard table)
$0.625 per million writes (Standard table)
Best for unpredictable traffic and serverless preference

Provisioned Capacity

On request

Fixed hourly charges based on reserved capacity
RCU rate: $0.00013 per hour (Standard)
WCU rate: $0.00065 per hour (Standard)
Reserved capacity savings: 54% (1-year) or 77% (3-year)
Best for predictable, steady throughput workloads

Standard Table Class Storage

$0.25 /per GB/month

$0.25 per GB/month after free tier
First 25 GB free per month (free tier)

Standard-Infrequent Access Table Class

$0.1 /per GB/month

$0.10 per GB/month

Capabilities

Features

Managed and serverless
No instances to size or patch; storage and throughput scale without an operator
Predictable latency
Single-digit millisecond keyed reads that do not degrade as the table grows
On-demand or provisioned capacity
Pay per request, or provision capacity with autoscaling for steadier and cheaper high volume
Global secondary indexes
Alternative key schemas maintained as replicated copies with their own capacity
Transactions
Atomic operations across as many as 100 items, with condition checks for optimistic concurrency
DynamoDB Streams
An ordered change log per partition, commonly consumed by Lambda for downstream processing
Global tables
Multi-region active-active replication with last-writer-wins conflict resolution
Point-in-time recovery
Continuous backups allowing restore to any second within the retention window
Time to live
Automatic expiry and deletion of items past a timestamp attribute, at no throughput cost
DAX
An in-memory cache in front of the table for read-heavy workloads needing microsecond responses

Answered, with sources

Questions people ask

Each answer names the page it came from, so you can check it rather than take our word for it.

On-demand or provisioned capacity?

On-demand suits unpredictable or spiky traffic and removes capacity planning. Provisioned with autoscaling is considerably cheaper for steady high-volume workloads. Tables can be switched between them, though not arbitrarily often.

Can I run DynamoDB outside AWS?

No. DynamoDB Local exists for development and testing only. For a production-compatible alternative elsewhere, ScyllaDB's Alternator implements the DynamoDB API, but it is a different system with a different ecosystem.

Can I run ad hoc queries or analytics?

Not on the table itself. Scans are slow and expensive at scale. The usual pattern is to export to S3 or stream changes out and query them in Athena, Redshift or another analytical engine.

Is single-table design necessary?

It is the pattern that gets the most from DynamoDB when access patterns are well known, because it lets related items be retrieved in one query. It also makes the model harder to evolve, so many teams reasonably choose multiple simpler tables and accept extra requests.

What are the real limits I should design around?

400 KB per item, 1 MB per query or scan page, 100 items per transaction, roughly 3,000 read and 1,000 write units per partition, and eventual consistency on global secondary indexes.

Behind it

Who makes DynamoDB

Company: Amazon Web Services
Based in: Seattle, Washington

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Softwr does not host reviews and shows no star rating for DynamoDB, because a rating we did not collect is not ours to publish. What is here is the pricing and platform detail from the vendor’s own pages, limitations we could state concretely, and alternatives a reviewer confirmed people weigh against it. Tell us if any of it is wrong.

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