DynamoDB vs ElastiCache

DynamoDB and Amazon ElastiCache both store data in AWS and both get called fast, but they answer different questions. DynamoDB is a fully managed, serverless database of record: writes are persisted to disk and replicated across Availability Zones. ElastiCache is, in AWS's own words, "a web service that makes it easy to set up, manage, and scale a distributed in-memory data store or cache environment in the cloud." For most systems the useful comparison is not DynamoDB or ElastiCache — it is which cache, if any, belongs in front of DynamoDB.

Should you use DynamoDB or ElastiCache?

Use DynamoDB for data you cannot afford to lose: durable items read and written by key at any scale. Use ElastiCache for an in-memory layer — caching, session state, rate limiting, leaderboards, pub/sub — where microsecond reads matter more than durability guarantees. If you are adding a cache specifically to speed up DynamoDB, the real decision is ElastiCache versus DAX, DynamoDB's own cache; that comparison is below.

DynamoDB vs ElastiCache at a glance

Characteristic	DynamoDB	ElastiCache
Role	Durable database of record	Managed in-memory data store or cache
Engines	One managed engine (DynamoDB itself)	Valkey, Memcached, and Redis OSS
Data model	NoSQL key-value and document; typed items up to 400 KB	Engine-dependent — strings, hashes, lists, sets, sorted sets and streams on Valkey/Redis OSS; plain key-value on Memcached
Durability	Every write persisted to disk and replicated across Availability Zones	In-memory by default; node-based Valkey clusters can enable durability via a distributed Multi-AZ transactional log
Consistency	Eventually consistent by default; strongly consistent reads available per request	Strongly consistent on a primary node for its own keys; replica reads can lag
Access	Native API (`GetItem`, `Query`, `Scan`, …) plus PartiQL	Engine commands over a cache endpoint; no cross-key query language
Capacity model	Storage on disk; scales with data volume	Bounded by provisioned memory — serverless scales it for you, node-based clusters you size yourself
Operational model	Serverless; nothing to provision or patch	Serverless cache or node-based cluster; AWS manages provisioning, monitoring, node replacement and patching
Typical use	Records that must survive	Cache-aside layers, session stores, rate limits, queues and pub/sub

When DynamoDB is the better choice

The data must survive. DynamoDB persists and replicates every write by default. An ElastiCache cache is in-memory first; durability is something you enable on node-based Valkey clusters, not the default posture.
Your working set exceeds memory. DynamoDB cost tracks storage. ElastiCache capacity is bounded by the RAM you provision or the memory the serverless cache scales to.
You need strongly consistent reads. DynamoDB offers them per request. A cache in front of a database is eventually consistent with it by construction.
You want the AWS control plane. Point-in-time recovery, backups, Streams, IAM and Lambda triggers are configuration on a DynamoDB table.

When ElastiCache is the better choice

You need microsecond reads. Data in RAM answers faster than durable storage, whatever the database behind it.
You need rich in-memory data structures. Sorted sets, counters, streams and pub/sub are first-class on Valkey and Redis OSS, and modelling them in a durable store is work.
The data is genuinely ephemeral. Sessions, rate-limit windows and recomputable results fit a cache's lifecycle.
You are caching more than DynamoDB. ElastiCache sits in front of anything — RDS, Aurora, an API, a search index. DAX only accelerates DynamoDB.

Using them together

The usual production shape is both: DynamoDB holds the durable records, and an in-memory layer absorbs the hot reads. ElastiCache does this as a general cache-aside tier you write code for — your application checks the cache, falls back to DynamoDB, and populates the cache on a miss. DynamoDB also ships its own alternative, DAX, which does this without the cache-aside code.

ElastiCache or DAX in front of DynamoDB

This is the decision most teams are actually making, and the AWS documentation answers it more sharply than the marketing pages do.

DAX is a drop-in; ElastiCache is a code change. DAX is "API-compatible with DynamoDB. Therefore, it requires only minimal functional changes to use with an existing application." It reduces eventually consistent reads "by an order of magnitude from single-digit milliseconds to microseconds." With ElastiCache you write and own the cache-aside logic, including invalidation.

Four documented reasons DAX may not fit. AWS lists the cases where DAX is not ideal, and each maps to a real workload:

Strongly consistent reads. DAX serves eventually consistent data. If a read path requires ConsistentRead, DAX is not an option for it.
Write-intensive workloads. "High volume of writes lead to increased replication across DAX nodes in a cluster," raising resource use and availability risk.
Low repeat-read rates. "DAX performs best when cache hit rates exceed 90%." Below that, misses cost you resources without buying much latency.
Language support. "DAX supports applications written in Go, Java, Node.js, Python, and .NET, using AWS-provided clients." If your service is Rust, Ruby, PHP or Elixir, DAX is effectively closed to you and ElastiCache — reachable from any Valkey, Redis OSS or Memcached client — is the practical choice. This single line decides the question more often than any latency benchmark, and it is easy to miss.

DAX is also "only available for the EC2-VPC platform."

The DAX trap worth knowing before you model. AWS documents a limitation that collides with a common DynamoDB modelling habit:

DAX clusters maintain metadata about the attribute names of items they store. That metadata is maintained indefinitely (even after the item has expired or been evicted from the cache). Applications that use an unbounded number of attribute names can, over time, cause memory exhaustion in the DAX cluster. This limitation applies only to top-level attribute names, not nested attribute names.

Read that against how people build sparse or heterogeneous items. An item whose values are timestamps and UUIDs is fine. An item that uses a timestamp, session ID or tenant ID as a top-level attribute name — a shape that turns up when a map is flattened onto the item to keep it queryable — grows DAX's metadata forever. The cache does not recover the space when the item is evicted.

The mitigation is a modelling one, not a configuration one: keep the identifiers in attribute values and nest variable keys one level down inside a map, where the limitation explicitly does not apply. ElastiCache has no equivalent constraint, because it does not track your item schema at all.

Durability is no longer a clean dividing line. The familiar claim that ElastiCache cannot be durable is now out of date. AWS documents that "for node-based Valkey clusters, you can enable durability to persist your data in a distributed Multi-AZ transactional log," and that "with durability enabled, your data is protected even if all cache nodes fail." That does not make ElastiCache a system of record — it does mean "the cache loses everything on restart" is not an argument you can make without checking the engine and cluster type first.

Working with DynamoDB

Whichever cache you put in front of it, DynoTable is a native desktop client for browsing, editing, and querying the DynamoDB tables underneath, on macOS, Windows, and Linux. It reads your standard AWS credential chain, so there is nothing to migrate. Its grid decodes composite keys like USER#123 and marks TTL attributes, which makes it straightforward to see which item shapes — and which top-level attribute names — a cache in front of the table would be asked to hold.

For building the key conditions and filters your cache-population code needs, the free DynamoDB Expression Builder generates ready-to-paste SDK, CLI, and PartiQL output with no install. DynoTable is a closed-source commercial app; this page describes what it does, not how it is built.

FAQ

Can ElastiCache replace DynamoDB?

Not as a system of record. ElastiCache is an in-memory store; even with durability enabled on a node-based Valkey cluster it is designed as a caching tier, not the database your data lives in. DynamoDB persists and replicates every write across Availability Zones by default.

Is DAX or ElastiCache better for DynamoDB?

DAX if your application is written in Go, Java, Node.js, Python or .NET, your reads are eventually consistent, and your cache hit rate will exceed 90% — it is API-compatible, so you barely change code. ElastiCache if you need another language, need to cache more than DynamoDB, or want in-memory data structures DAX does not provide.

Does ElastiCache lose data when a node restarts?

By default it is in-memory, so treat it as volatile. AWS now documents optional durability for node-based Valkey clusters, persisting to a distributed Multi-AZ transactional log so data survives even if all cache nodes fail. Whether your cache is volatile depends on the engine and cluster type you chose.

Learn when to use DynamoDB and how DynamoDB TTL expires cache-like items.
Compare DynamoDB and Redis for the store-versus-cache question in general terms.
Model durable access patterns with single-table design.
Download DynoTable to browse, query, and edit your DynamoDB tables.

References

Last verified 2026-08-02 against the official AWS ElastiCache User Guide and DynamoDB Developer Guide. Valkey, Redis OSS and Memcached are trademarks of their respective owners; referenced here for identification only.