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Database ⇄ AI

Amazon DynamoDB to Anthropic integration — real-time data sync

Keep Amazon DynamoDB and Anthropic in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Amazon DynamoDB and Anthropic

Sync the records in Amazon DynamoDB into Anthropic and land its embeddings, classifications, and generated fields back on the same rows, in real time and without a pipeline to maintain.

Anthropic is a read-only source: Stacksync reads its data in real time and delivers it into Amazon DynamoDB, so Amazon DynamoDB always reflects the current state of Anthropic — without exports, scripts, or schedulers.

AI systems do not hold customers or invoices the way business apps do. What they hold is derived from your data: the vectors and metadata in a vector store, or the classifications, extracted fields, and generated text a model produces over records it was given. Amazon DynamoDB is where those source records actually live. The bridge between the two is the row itself, since an item in Anthropic and the record in Amazon DynamoDB it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Time to Live (TTL), Tables, Items, Global secondary indexes (GSIs) in Amazon DynamoDB with Organization Members, API Keys, Invites, Message Batches in Anthropic in real time. Rows created or changed in Amazon DynamoDB flow into Anthropic so inference and embedding run on current data, and the scores, labels, and generated fields Anthropic produces flow back onto the matching rows in Amazon DynamoDB, mapped field by field. A change on either side appears on the other within seconds, with no extraction job or webhook plumbing to keep alive.

Because matching is by a stable identifier, every row in Amazon DynamoDB stays tied to its AI-side counterpart in Anthropic. Retrieval, enrichment, and generated content always resolve back to the record they came from, so there are no orphaned vectors and no labels describing a version of a row that no longer exists.

Common use cases

  • 01 Mirror Organization Members and their roles into an IdP or HR database to audit who can access which workspace.
  • 02 Load Message Batches status and results into a database so downstream jobs consume completed batch output by custom_id.
  • 03 Use DynamoDB Streams as a change-data-capture source to push item INSERT, MODIFY, and REMOVE events into a CRM, search index, or operational database in near-real-time.
  • 04 Backfill or migrate records into DynamoDB from another database using BatchWriteItem, then keep the two stores in continuous sync.

Common sync patterns

One record, one identifier

Each item in Anthropic carries the key of the row in Amazon DynamoDB it came from, so results resolve back to the exact record with nothing orphaned or duplicated.

Run the AI on current data

Rows created or changed in Amazon DynamoDB flow into Anthropic as they happen, so embeddings, classifications, and prompts run on the latest records instead of a nightly snapshot.

Write results back onto the record

Scores, labels, extracted fields, or generated text produced in Anthropic land on the matching row in Amazon DynamoDB, next to the source data your applications already query.

What you can sync between Amazon DynamoDB and Anthropic

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Amazon DynamoDB objects Anthropic objects How this pairing syncs
Time to Live (TTL) Per-item expiry timestamps; DynamoDB deletes expired items in the background and emits a Streams REMOVE record for each deletion. Organization Members Users in the organization with their role from /v1/organizations/users; read into an IdP or HR database for access auditing rather than written back. Time to Live (TTL) is specific to Amazon DynamoDB and Organization Members to Anthropic — each maps to any object or custom field on the other side.
Tables Top-level containers, each with a partition key and optional sort key; Stacksync syncs a table as a stream of items with full read and write via PutItem, UpdateItem, and DeleteItem. API Keys Key metadata — name, owning workspace, status, creator — from /v1/organizations/api_keys; the secret value is never returned. Read-only, useful for a security key inventory. Tables is specific to Amazon DynamoDB and API Keys to Anthropic — each maps to any object or custom field on the other side.
Items Individual schemaless records (attributes up to 400 KB each); read with GetItem, Query, and Scan and written with PutItem or BatchWriteItem, so write is supported here. Invites Pending organization invitations from /v1/organizations/invites; read to track who has been invited to the org but has not yet accepted. Items is specific to Amazon DynamoDB and Invites to Anthropic — each maps to any object or custom field on the other side.
Global secondary indexes (GSIs) Alternate key projections that let you Query by non-key attributes without a full table Scan; read-only views maintained automatically by DynamoDB. Message Batches Asynchronous batch jobs at /v1/messages/batches; the connector polls processing_status and reads per-request results keyed by custom_id once a batch has ended. Global secondary indexes (GSIs) is specific to Amazon DynamoDB and Message Batches to Anthropic — each maps to any object or custom field on the other side.
Local secondary indexes (LSIs) Extra sort keys within the same partition key, defined at table creation; queried like the base table for alternate access patterns. Models Claude model catalog from /v1/models with model IDs, context window, max output, and capability flags; snapshotted into a config table so applications avoid hardcoding model IDs. Local secondary indexes (LSIs) is specific to Amazon DynamoDB and Models to Anthropic — each maps to any object or custom field on the other side.
DynamoDB Streams Ordered item-level change records (INSERT, MODIFY, REMOVE) with old/new image views and 24-hour retention; the native change-data-capture source Stacksync reads for near-real-time sync. Usage Report (messages) Time-bucketed token usage (uncached input, cached input, cache creation, output) grouped by workspace, model, API key, and service tier from /v1/organizations/usage_report/messages; read-only, queried by date range at 1m/1h/1d bucket width. DynamoDB Streams is specific to Amazon DynamoDB and Usage Report (messages) to Anthropic — each maps to any object or custom field on the other side.

How changes propagate between Amazon DynamoDB and Anthropic

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Amazon DynamoDB Anthropic Sub-second propagation

DetectionChanges in Amazon DynamoDB are captured at the source via change data capture — no polling loop against its API. DynamoDB Streams emit ordered item-level change records (INSERT, MODIFY, REMOVE) with KEYS_ONLY, NEW_IMAGE, OLD_IMAGE, or NEW_AND_OLD_IMAGES views.

DeliveryAnthropic does not accept inbound record writes, so this direction carries requests rather than records: Anthropic's output flows back as field updates on the originating Amazon DynamoDB records.

Anthropic Amazon DynamoDB Interval-based propagation

DetectionStacksync polls Anthropic for changes on an incremental schedule, reading only records changed since the previous pass. Polling.

DeliveryEach detected change is applied to Amazon DynamoDB as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Amazon DynamoDB: Throughput is metered in read/write capacity units (provisioned or on-demand): 1 WCU = one 1 KB write per second, 1 RCU = one strongly-consistent 4 KB read per second. Exceeding capacity or the ~3,000 RCU / 1,000 WCU per-partition ceiling returns ProvisionedThroughputExceededException with throttling.
  • Anthropic: Messages API limits are per usage tier: requests-per-minute plus input- and output-tokens-per-minute, surfaced in anthropic-ratelimit-* response headers with retry-after on 429. Usage/cost reports recommend polling at most once per minute.
What ships with Amazon DynamoDB ⇄ Anthropic

Connect Amazon DynamoDB and Anthropic for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon DynamoDB–Anthropic connection.

Real-time

Real-time sync

Changes in Amazon DynamoDB or Anthropic instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon DynamoDB or Anthropic data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Amazon DynamoDB or Anthropic record.

Observability

Monitoring

Track your Amazon DynamoDB ⇄ Anthropic sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon DynamoDB and Anthropic.

How the Amazon DynamoDB and Anthropic connectors work

Amazon DynamoDB

Integration surface
AWS SDK / low-level HTTPS JSON API at dynamodb.<region>.amazonaws.com (PutItem, GetItem, UpdateItem, DeleteItem, Query, Scan, BatchWriteItem, TransactWriteItems), plus PartiQL (ExecuteStatement) for SQL-style access and DynamoDB Streams for change capture.
Authentication
AWS Signature Version 4 (SigV4) signed requests using an IAM access key ID and secret key, or temporary STS credentials from an assumed IAM role; IAM policies scope access down to table and item level.
Change detection
DynamoDB Streams emit ordered item-level change records (INSERT, MODIFY, REMOVE) with KEYS_ONLY, NEW_IMAGE, OLD_IMAGE, or NEW_AND_OLD_IMAGES views and 24-hour retention, read via shard iterators (or Kinesis Data Streams for longer retention). No native HTTP webhooks.
Capabilities
read · write · CDC
Rate limits
Throughput is metered in read/write capacity units (provisioned or on-demand): 1 WCU = one 1 KB write per second, 1 RCU = one strongly-consistent 4 KB read per second. Exceeding capacity or the ~3,000 RCU / 1,000 WCU per-partition ceiling returns ProvisionedThroughputExceededException with throttling.

Anthropic

Integration surface
REST — Messages API at api.anthropic.com/v1 plus the Admin API (/v1/organizations/*) for organization, usage, and cost data
Authentication
API key in the x-api-key header for Messages, Models, Files, and Batches endpoints; the Admin API requires a separate Admin API key (sk-ant-admin...) with organization-admin permission. Every request also sends an anthropic-version header.
Change detection
Polling. Usage and cost reports are queried by time bucket (1m/1h/1d) over a date range; list endpoints paginate with has_more/next_page (or after_id). No general-purpose data-change webhooks (webhooks exist only for Managed Agents session state).
Capabilities
read
Rate limits
Messages API limits are per usage tier: requests-per-minute plus input- and output-tokens-per-minute, surfaced in anthropic-ratelimit-* response headers with retry-after on 429. Usage/cost reports recommend polling at most once per minute.
How it works

How to connect Amazon DynamoDB to Anthropic — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Amazon DynamoDB and Anthropic with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Amazon DynamoDB connected
    Anthropic connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon DynamoDB and Anthropic objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Amazon DynamoDB ⇄ Anthropic
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Amazon DynamoDB Anthropic
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon DynamoDB and Anthropic integration FAQ

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Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

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Related integrations

Every pair below is a real-time, two-way sync. Search all 413 integrations available for Amazon DynamoDB and Anthropic.

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