DynoTable vs Dynomate

Dynomate is a fast, actively maintained DynamoDB client built around request collections, workflows and Git-friendly files. DynoTable is a DynamoDB client built around a SQL Workbench that runs JOINs, GROUP BY and aggregates within DynamoDB's access-pattern rules (see SQL for DynamoDB). This page compares the two on the thing that actually separates them: what each one does when a question spans two tables.

Feature	DynoTable	Dynomate
Purpose-built for DynamoDB	Yes	Yes
Browse & edit DynamoDB items	Yes	Yes
PartiQL query editor	Yes	Yes
SQL JOINs, GROUP BY & aggregates	Yes	No
Query plan preview (Scan/Query + RCU when available)	Yes	No
Smart Tables (visual joined views)	Yes	No
AI agent on your own AWS Bedrock keys	Yes	No
Connect external AI agents (MCP), staged review	Yes	No
Table management (create/delete, GSIs, TTL, capacity)	Yes	No
Export to CSV / JSON	Yes	Yes
Works offline (DynamoDB Local)	Yes	Yes
Pricing	Free read-only tier, from $9/mo billed annually	From $10/mo

What Dynomate does well

Dynomate is a good client and it is fair to say so. It ships often, the latest release on its changelog is 1.19.0 on 30 June 2026, and its collections are plain directories marked by a collection.toml file, so they diff and review in Git alongside the code that uses them. It supports AWS SSO, multi-region and multi-account browsing, DynamoDB Local, import from JSON/CSV/S3/another table, and since 1.18.0 it queries Amazon Athena next to your DynamoDB tabs. Pricing is a subscription: Community is free, and the paid tiers run $10, $18 and $29 a month billed annually, with a 14-day trial (dynomate.io, checked 2026-07-28). If a reviewable, Git-native query library is the feature you are shopping for, Dynomate is the honest recommendation.

What a workflow does instead of a JOIN

Dynomate's answer to a multi-table question is the request chain it shipped in its first public preview: "Combine query, put, update or delete steps; reference previous outputs (${step1.items[0].email}) to build powerful workflows" (dynomate.io changelog 0.17.0, 6 April 2025).

The reference syntax carries the constraint. items[0] selects one item from the previous step, so a chain is a pipeline of single values. It expresses "look up this user, then write that order" perfectly, and "for every shipment in the scan, fetch its warehouse, then average by region" not at all. There is no fan-out step and no aggregating step, so a join and a GROUP BY stay outside the tool.

Dynomate's second answer is SQL, and it is honest about the shape of it: "From any table, click SQL → Export & Open SQL. Dynomate runs a background full-table scan, creates a local timestamped snapshot, and drops you straight into a SQL editor" (changelog 1.11.0, 5 February 2026). Two consequences follow from that sentence.

The first is cost. A full-table scan is not a cheap way to open a query editor: "A Scan operation in Amazon DynamoDB reads every item in a table or a secondary index", and DynamoDB bills read capacity "based on the number of items and the size of those items, not on the amount of data that is returned to an application" (AWS Scan reference). You pay for the whole table each time you refresh the snapshot.

The second is freshness. Every result you get back is as old as the snapshot. For the use case Dynomate names, comparing a table across two points in time while debugging a deployment, that is exactly right and a live query would be worse. For "what does production look like now", it is a copy.

DynoTable's SQL Workbench answers the same question against live tables, with no export step:

SELECT w.region, AVG(s.transitHours) AS avgTransit, COUNT(*) AS shipments
FROM shipments s
JOIN warehouses w ON s.warehouseId = w.warehouseId
WHERE s.status = 'delivered'
GROUP BY w.region

DynoTable SQL Workbench running a JOIN with GROUP BY over live DynamoDB tables — no full-table export snapshot.

DynoTable staged-commit diff before anything is written to DynamoDB.

The statement compiles to native Query and Scan calls planned against your keys and indexes, and the join and the AVG run on the client, so there is no export to trigger and no snapshot to keep fresh.

When a plan is available, the Workbench preview identifies Scan versus Query and the index; the RCU estimate appears only when table metadata supports it. An ordinary partition Query shows no RCU.

DynamoDB gains no server-side join from any of this. DynamoDB GROUP BY walks through the plan. Neither client asks you to trust a vendor with your table data; the difference on AI is that DynoTable's assistant runs on your own AWS Bedrock credentials and stages every write for approval, as the AI chat docs describe.

Which one should you pick?

Pick Dynomate if a Git-native library of saved, chained requests is the core of how you and your team work with DynamoDB, or if you want Athena and DynamoDB in the same window.
Pick DynoTable if the questions you keep failing to ask are relational ones: a JOIN, a GROUP BY or an aggregate across tables, answered against live data rather than an export.

Both are maintained, both read your AWS credential chain, and neither routes your table data through a third party. The deciding question is whether your bottleneck is sharing queries or asking relational ones.

How to try DynoTable

Nothing about trying DynoTable disturbs a Dynomate setup: your collections stay where they are on disk, and both apps read the same ~/.aws profiles. Download DynoTable for macOS, Windows or Linux, connect the profile you already use, and run the query above against two of your live tables.

The trial is free for 30 days, no credit card required, and DynoTable stays on the Free plan after that — browsing, PartiQL SELECT, exports and MCP schema/item reads, with no time limit. See pricing for the current plans.

FAQ

Is DynoTable a Dynomate alternative?

Yes. Dynomate is an actively maintained DynamoDB client built around request collections and Git-based sharing; DynoTable adds a SQL Workbench that runs JOINs, GROUP BY and aggregates across your tables, plus an AI agent that runs on your own AWS Bedrock account.

Can DynoTable run SQL against DynamoDB?

Yes. DynoTable's SQL Workbench compiles SQL — including INNER/LEFT JOIN, GROUP BY and aggregates — down to DynamoDB's real Query/Scan operations, so it stays within DynamoDB's access-pattern rules.

Does Dynomate's SQL console query live DynamoDB tables?

Not directly. Its changelog describes the flow as a background full-table scan that writes a local timestamped snapshot, which the SQL editor then queries (dynomate.io changelog 1.11.0, 5 February 2026). That is a good fit for comparing a table across points in time, and it means each refresh reads and bills the whole table, and results are as fresh as the last snapshot.

Browse the full comparison hub for every DynoTable alternative.
Weighing the incumbent too? See the Dynobase comparison.
See also DynoTable as a DynamoDB GUI and the NoSQL Workbench comparison.
Build queries fast with the free DynamoDB Expression Builder.

Last verified 2026-08-28. Dynomate is a trademark of its respective owner; referenced here for identification only.