Amazon DynamoDB Accelerator (DAX)

Name: Amazon DynamoDB Accelerator (DAX) API
Brand: Amazon DynamoDB Accelerator (DAX)
Availability: InStock

★ Only Publicly Available OpenAPI DocumentStorageDatabasehmac21 EndpointsREST

For Agents

Provision and operate Amazon DynamoDB Accelerator (DAX) clusters, manage parameter and subnet groups, and scale replication factor for cached DynamoDB reads.

Quickstart

Get started with Amazon DynamoDB Accelerator (DAX) in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"create a DAX cluster"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Amazon DynamoDB Accelerator (DAX) API.

Provision DAX clusters with CreateCluster and tear them down with DeleteCluster when read-cache workloads change

Adjust read capacity by calling IncreaseReplicationFactor or DecreaseReplicationFactor on a running cluster

Manage cluster configuration through CreateParameterGroup, UpdateParameterGroup, and DescribeParameters

Place clusters in target VPCs with CreateSubnetGroup and UpdateSubnetGroup

GET STARTED

Start building with Amazon DynamoDB Accelerator (DAX) API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Amazon DynamoDB Accelerator (DAX), keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Amazon DynamoDB Accelerator (DAX), keeping it validated and agent-ready. Amazon DynamoDB Accelerator (DAX) is a managed in-memory caching service that sits in front of Amazon DynamoDB to deliver sub-millisecond read latency for cached items. The DAX control plane API lets you provision and operate clusters, manage parameter groups and subnet groups, scale replication factor, describe events, and tag resources. It is intended for read-heavy workloads where DynamoDB latency or cost-per-read becomes a bottleneck and a write-through cache makes economic sense.

Use Cases

Patterns agents use Amazon DynamoDB Accelerator (DAX) API for, with concrete tasks.

★ Add a Read Cache in Front of DynamoDB

Provision a DAX cluster to absorb hot-key read traffic from a DynamoDB table, cutting read latency from single-digit milliseconds to microseconds for cached items. The DAX control plane handles cluster creation, node placement across subnets, and parameter group binding so the application only needs to point its DAX client at the cluster endpoint. Useful when scaling read-heavy workloads where provisioned read capacity costs grow faster than the application can tolerate.

Call CreateCluster with a node type of dax.r4.large, replication factor 3, and a target subnet group, then poll DescribeClusters until the cluster status is available.

Scale a DAX Cluster for Traffic Spikes

Increase or decrease the number of read replicas in a DAX cluster as traffic patterns change. IncreaseReplicationFactor adds nodes for a marketing event or product launch, and DecreaseReplicationFactor removes them once the spike subsides, keeping the cluster sized to actual demand without re-creating it.

Call IncreaseReplicationFactor on cluster my-dax-cluster with NewReplicationFactor 5 and verify the cluster returns to status available.

Manage Cluster Parameter Groups

Create, update, and inspect DAX parameter groups to control TTL, query cache size, and engine-level tuning. Parameter groups let multiple clusters share a tested configuration and roll out changes via DescribeParameters and parameter group updates rather than per-cluster edits.

Create a parameter group named dax-prod-defaults, update query-ttl-millis to 600000, and attach it to two existing DAX clusters.

Agent-Driven Cluster Lifecycle Management

An AI agent connected via Jentic can provision DAX clusters from a natural-language request, scale them in response to monitoring alerts, and decommission them on schedule. Jentic returns the matching DAX operation and its input schema so the agent can call CreateCluster, IncreaseReplicationFactor, or DeleteCluster with structured arguments and recover cleanly from validation errors.

Search Jentic for 'create a DAX cluster', load the CreateCluster schema, execute with the requested cluster name and node type, then poll DescribeClusters until status is available.

Key Endpoints

21 endpoints — jentic publishes the only available openapi specification for amazon dynamodb accelerator (dax), keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/#X-Amz-Target=AmazonDAXV3.CreateCluster

Create a new DAX cluster

POST

/#X-Amz-Target=AmazonDAXV3.DescribeClusters

Describe one or more DAX clusters

POST

/#X-Amz-Target=AmazonDAXV3.IncreaseReplicationFactor

Add read replicas to a cluster

POST

/#X-Amz-Target=AmazonDAXV3.DecreaseReplicationFactor

Remove read replicas from a cluster

POST

/#X-Amz-Target=AmazonDAXV3.DeleteCluster

Delete a DAX cluster

POST

/#X-Amz-Target=AmazonDAXV3.CreateSubnetGroup

Create a subnet group for cluster placement

POST

/#X-Amz-Target=AmazonDAXV3.DescribeEvents

List recent cluster events

POST

/#X-Amz-Target=AmazonDAXV3.CreateCluster

Create a new DAX cluster

POST

/#X-Amz-Target=AmazonDAXV3.DescribeClusters

Describe one or more DAX clusters

POST

/#X-Amz-Target=AmazonDAXV3.IncreaseReplicationFactor

Add read replicas to a cluster

POST

/#X-Amz-Target=AmazonDAXV3.DecreaseReplicationFactor

Remove read replicas from a cluster

POST

/#X-Amz-Target=AmazonDAXV3.DeleteCluster

Delete a DAX cluster

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

AWS access keys for DAX are stored encrypted in the Jentic vault. Jentic signs each DAX request with SigV4 at execution time and the agent only sees scoped, short-lived access — raw access keys never enter the agent's context.

Intent-based discovery

Agents search by intent (e.g., 'create a DAX cluster' or 'add read replicas to my cache') and Jentic returns the matching DAX operation with its input schema, so the agent can call the right endpoint without browsing AWS documentation.

Time to first call

Direct DAX integration: 1-3 days for SigV4 signing, IAM policy setup, and cluster lifecycle handling. Through Jentic: under 1 hour — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Amazon Data Lifecycle Manager

Schedules backups for AWS resources alongside DAX-fronted DynamoDB workloads.

Choose DLM when the agent needs to automate snapshot lifecycles for EBS volumes that back DAX cluster nodes or related stateful resources.

Alternative

AWS RDS Data API

HTTP-based access to relational databases when DynamoDB is not the right model.

Choose RDS Data API when the workload is relational and DAX's key-value caching of DynamoDB does not apply.

Alternative

Amazon DocumentDB

Document database control plane for MongoDB-compatible workloads.

Choose DocumentDB when the workload needs document semantics rather than DynamoDB's key-value model.

FAQs

Specific to using Amazon DynamoDB Accelerator (DAX) API through Jentic.

Why is there no official OpenAPI spec for Amazon DynamoDB Accelerator (DAX)?

AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon DynamoDB Accelerator (DAX) via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Amazon DAX API use?

The DAX control plane API uses AWS Signature Version 4 (HMAC) signing with an AWS access key ID and secret access key. Through Jentic, those credentials live encrypted in the Jentic vault and are injected into signed requests at execution time, so the agent never sees the raw secret access key.

Can I scale a DAX cluster up and down with the API?

Yes. Use IncreaseReplicationFactor to add read replicas and DecreaseReplicationFactor to remove them. The cluster remains available during scaling, so no recreation is needed for routine capacity changes.

What are the rate limits for the Amazon DAX API?

The DAX control plane shares standard AWS service quotas, which are managed per account and region rather than published as fixed per-second limits in the spec. Treat sustained CreateCluster or scaling calls as throttle-prone and back off on ThrottlingException responses.

How do I provision a DAX cluster through Jentic?

Search Jentic for 'create a DAX cluster', load the CreateCluster operation schema, then execute it with parameters like ClusterName, NodeType, ReplicationFactor, and SubnetGroupName. Run pip install jentic to get the SDK and use the async search and execute pattern.

Does this API serve cached data, or only manage clusters?

This API only manages clusters and configuration. Reading and writing cached items goes through the DAX data-plane client libraries (which speak the DAX wire protocol) against the cluster endpoint returned by DescribeClusters.