DynamoDB Accelerator (DAX): How It Works and When to Use It

Amazon DynamoDB Accelerator (DAX) is a managed in-memory cache for eventually consistent DynamoDB reads. It can serve repeated reads with microsecond latency, while misses and strongly consistent reads still reach DynamoDB. DAX is billed per node-hour, starting at $0.04/hour for a dax.t3.small node in us-east-1, so it is most useful when repeated reads justify both the cluster cost and cached-data semantics. For node prices in every Region and a break-even check, use the DAX pricing calculator.

What DAX does

DAX connects an application to its DynamoDB tables through a dedicated data client. For a supported eventually consistent read, it checks its cache first. A hit avoids a DynamoDB read; a miss retrieves data from DynamoDB and caches it for later requests.

DAX is not a replacement database or a general-purpose Redis-compatible server. DynamoDB remains the durable source of truth. You still need to understand when cached data can be stale, size the cluster and monitor failures. AWS manages the service infrastructure, but adding DAX does not remove those application decisions.

The AWS DAX overview describes its supported workloads and clients.

When to use DAX

DAX fits read-heavy applications that repeatedly retrieve the same items or identical query results. Examples include a popular product description, frequently accessed content and repeated reference-data lookups. These workloads can achieve a high hit rate without making cached results too stale for their purpose.

It can also reduce read pressure on hot DynamoDB keys. A popular item that otherwise receives repeated reads may be served from cache, freeing source capacity. This does not help a write hotspot or change the underlying DynamoDB limits.

Repeated analysis of a stable dataset can benefit if the working set fits the cache and the same requests recur. A one-time scan is a poor reason to add DAX: its first pass still reads DynamoDB and may evict more useful cached data.

When DAX is a poor fit

Skip DAX when most operations are writes, most reads are unique, or the application requires a strongly consistent result on every read. Strong reads pay for the DynamoDB operation and add the DAX hop without getting a cached response.

DAX also may not be worthwhile when direct DynamoDB latency already meets the requirement or an existing application cache handles the workload well. Low hit rates leave both the DAX bill and most of the DynamoDB read bill in place. Use measured hit rates and end-to-end latency instead of assuming every request becomes faster.

Write-heavy traffic consumes primary-node resources and replicates cache changes across the cluster. Adding read replicas is not a general solution to primary-node pressure from writes or cache misses.

Item-cache and query-cache consistency

The most important behavior is that the two caches are independent. See AWS's DAX consistency documentation.

Item cache

GetItem and BatchGetItem populate the item cache. Its default TTL is five minutes. An expired entry produces a miss on a subsequent read, and DAX reads DynamoDB again. LRU eviction can remove an entry earlier when memory is needed.

An ordinary write through DAX first succeeds in DynamoDB, then updates the item cache before returning success. Replication to DAX read replicas is asynchronous, so cached reads are still eventually consistent. A direct DynamoDB write does not immediately update DAX. That includes administrative edits or application paths that bypass the DAX client.

Query cache

Query and Scan populate result sets identified by their request parameters. They do not warm the item cache. Updating an item, even through DAX, does not invalidate a cached query or scan result. The result remains eligible for reuse until its query-cache TTL expires or it is evicted.

For example, querying available products, writing a new available product and repeating the query can return the old result set. Set the query-cache lifetime according to how long the application may tolerate that result. For a read that must observe the latest committed write, use a supported strongly consistent read against DynamoDB rather than assuming cached query results are current.

Negative entries, TTL and transactions

DAX can cache an absent item or empty query result. Creating the missing item directly in DynamoDB does not immediately invalidate the negative cache entry.

Item TTL and query TTL are distinct settings. An item-cache TTL of zero disables time-based expiration, while query-cache TTL of zero disables query caching. DAX TTL does not delete DynamoDB data; DynamoDB TTL is a separate asynchronous table cleanup feature.

TransactGetItems bypasses the cache. After TransactWriteItems succeeds, DAX performs background transactional reads to populate the item cache, which can add read capacity consumption. The cache is not an extension of the transaction's atomic boundary.

Current DAX clients by language

DAX needs a data-plane client, not just the AWS SDK used to create and describe clusters. Use the client generation matching your application SDK. The following are the current documented integration paths, checked October 23, 2026:

Official setup instructions are available for Node.js, Go, Java, Python and .NET. These are supported integration paths, not a claim that every SDK middleware, paginator or new DynamoDB operation works through DAX.

Avoid starting new integrations from examples tied to AWS SDK for JavaScript v2, Go v1, Java 1.x or .NET v3: those SDK generations have reached end of support. In particular, the JavaScript v3 DAX client does not implement the ordinary bare-bones client's send() interface or custom middleware stack. Keep a separate normal DynamoDB client for unsupported operations and table management.

Minimal Node.js read with the current DAX client

Install @amazon-dax-sdk/lib-dax in your application. Run this from an environment with network access to the cluster and appropriate IAM permissions. Set DAX_ENDPOINT to the cluster discovery URL and AWS_REGION to its Region. The existing Products table in this example has a String partition key named productId.

import { DaxDocument } from "@amazon-dax-sdk/lib-dax"

const endpoint = process.env.DAX_ENDPOINT
const region = process.env.AWS_REGION
if (!endpoint || !region) {
  throw new Error("Set DAX_ENDPOINT and AWS_REGION")
}

const dax = new DaxDocument({
  endpoints: [endpoint],
  region,
})

export async function getProduct(productId) {
  const result = await dax.get({
    TableName: "Products",
    Key: { productId },
    ConsistentRead: false,
  })
  return result.Item
}
import { DaxDocument } from "@amazon-dax-sdk/lib-dax"

const endpoint = process.env.DAX_ENDPOINT
const region = process.env.AWS_REGION
if (!endpoint || !region) {
  throw new Error("Set DAX_ENDPOINT and AWS_REGION")
}

const dax = new DaxDocument({
  endpoints: [endpoint],
  region,
})

export async function getProduct(productId) {
  const result = await dax.get({
    TableName: "Products",
    Key: { productId },
    ConsistentRead: false,
  })
  return result.Item
}

The discovery URL uses daxs:// for an encrypted cluster. Use the URL returned by AWS instead of constructing a node hostname. Reuse the client across requests rather than reconnecting for every item. A single read does not demonstrate a cache benefit: compare the initial miss with repeated reads under representative concurrency.

Setting up a DAX cluster

1. Choose the network and node type. Select a supported Region and VPC, then a subnet group containing the Availability Zones you intend to use. Ensure application routing and security groups allow cluster access.
2. Choose node count and placement. For production availability, use at least three nodes spread across Availability Zones in the same Region. One node is useful for a small test but has no replica to promote on failure.
3. Create the DAX service role. Give it the required DynamoDB data permissions for the intended tables, including dynamodb:DescribeTable. Configure application IAM permissions for DAX separately.
4. Configure encryption and cache settings. Select encryption at rest and in transit as appropriate and set item/query TTLs to match freshness requirements.
5. Create the cluster and wait for availability. Update the application to use its discovery endpoint through the correct DAX client.
6. Test reads, writes and failure paths. Verify behavior after a write through DAX, a direct table write and a cached empty result. Configure monitoring and alarms before shifting production traffic.

The AWS cluster setup documentation covers the required role and network configuration. You can retrieve a cluster's discovery URL with AWS CLI v2:

aws dax describe-clusters \
  --cluster-names products-cache \
  --region us-east-1 \
  --query 'Clusters[0].ClusterDiscoveryEndpoint.URL' \
  --output text
aws dax describe-clusters \
  --cluster-names products-cache \
  --region us-east-1 \
  --query 'Clusters[0].ClusterDiscoveryEndpoint.URL' \
  --output text

DAX is a regional VPC service. Multi-AZ nodes are not replicas in separate AWS Regions. Use AWS's current DAX endpoint and Region list instead of an old fixed list of ten Regions. PrivateLink support for DAX management APIs does not turn the data endpoint into a public internet cache.

What does DAX cost?

DAX bills every node for each hour it runs, including idle time. Partial hours count as full hours, and there is no free DAX allowance. In us-east-1, dax.t3.small costs $0.04 per node-hour and dax.r7i.large $0.255, so a three-node r7i.large production cluster comes to $558.45 for a 730-hour month. Cache misses, writes and storage are still billed by DynamoDB.

DAX lowers the total bill only when the read charges it avoids exceed the cluster's cost. Since the November 2024 on-demand price cut, one million eventually consistent reads of items up to 4 KB cost $0.0625 in us-east-1, so even an $87.60 three-node t3.small cluster must avoid about 1.4 billion such reads a month to pay for itself. The DAX pricing calculator prices every node type in every Region and finds the break-even hit rate for your traffic. Latency, not cost, is often the better reason to add DAX.

Performance, scaling and monitoring

DAX accelerates cache hits, not every operation. Benchmark repeated reads, misses, writes, query variations and application network latency separately. Measure tail latency as well as averages, and include the cold-cache period after a new cluster or a workload change.

Horizontal scaling adds read replicas. Every node maintains a copy of the cached data, so adding replicas increases read-serving capacity rather than sharding the working set into a larger combined cache. Larger node types provide more memory and primary-node capacity. Changing node type requires a new cluster, so plan a controlled cutover. AWS's cluster-management guide describes both paths.

Monitor item-cache hits/misses and query-cache hits/misses separately. Also watch CPU, evictions, errors and ThrottledRequestCount, along with DynamoDB consumed capacity and application latency. Create alarms that reflect the application's tolerances and identify who responds to them. A DAX node can throttle when its own capacity is exhausted, independently of DynamoDB table throttling.

The console layout changes, but the monitoring goal stays the same: verify that useful reads hit the cache and that misses, writes and failures remain within capacity. See monitoring DAX.

Limits and operational best practices

DAX supports one primary and up to ten read replicas per cluster. The default account quota is 50 DAX nodes per Region and is adjustable. Other documented limits include 20 parameter groups, 50 subnet groups with up to 20 subnets each, and a maximum of 500 DynamoDB tables supported by one cluster.

Use stable top-level attribute names. DAX retains metadata about attribute names even after cached items expire or are evicted. Unbounded top-level names, such as a new timestamp or UUID as an attribute name for every record, can exhaust metadata memory. Put changing identifiers in values or nested structures instead.

Treat cached data as rebuildable. Define whether the application fails, retries or falls back to direct DynamoDB during a DAX outage. An unrestricted fallback can move the entire peak read load to the table, so test the source capacity and retry behavior. Review IAM, network access, encryption, cache lifetime and client compatibility whenever the workload changes.

DAX versus ElastiCache and Redis-compatible caches

ElastiCache for Valkey and Redis OSS supports encryption and authorization controls. It is incorrect to describe Redis-compatible caching as lacking native encryption or requiring an enterprise subscription for all access controls.

Choose DAX when repeated DynamoDB reads and its consistency model fit the application. Choose ElastiCache when you need derived responses, cross-source caching, custom invalidation or cache-specific data structures. Both add cost and failure behavior that need measurement. The DynamoDB caching guide compares the implementation patterns in more detail.