DynamoDB vs Redis
DynamoDB and Redis are both called NoSQL, but they sit in different tiers of the stack. DynamoDB is a fully managed, serverless database of record that keeps data durable on disk across an AWS Region. Redis is an in-memory data-structure store — most often deployed as a cache, session store, or message broker in front of a durable database. For many systems the answer is not either/or but DynamoDB as the store, Redis (or DAX) as the cache.
Should you use DynamoDB or Redis?
Use DynamoDB as your durable system of record: structured items you must not lose, read and written by key at scale. Use Redis as an in-memory layer for microsecond reads, rich data structures (sorted sets, streams, counters), caching, rate limiting, or pub/sub. They are commonly paired — Redis (or DynamoDB's own DAX cache) sits in front of DynamoDB rather than replacing it.
DynamoDB vs Redis at a glance
| Characteristic | DynamoDB | Redis |
|---|---|---|
| Data model | NoSQL key-value and document; typed items up to 400 KB, grouped in tables | In-memory key-value with typed values — strings, hashes, lists, sets, sorted sets, streams, and more |
| Primary role | Durable database of record | In-memory cache, session/rate-limit store, message broker; can be a primary store with persistence enabled |
| Query language / API | Native API (GetItem, Query, Scan, PutItem, …) plus PartiQL, a SQL-compatible language | Per-data-structure commands (GET, HSET, ZADD, XADD, …); no general query language or ad-hoc joins |
| Durability | Data persisted to disk and replicated across Availability Zones in a Region | In-memory by default; durability is optional via RDB snapshots and/or AOF append-only logging |
| Consistency | Eventually consistent by default; strongly consistent reads available per request | A single node is strongly consistent for its keys; replicas are asynchronous, so replica reads can lag |
| Scaling model | Automatic partitioning managed by AWS; serverless, scales throughput and storage | Vertical (RAM-bound) plus Redis Cluster for horizontal sharding across nodes; capacity bounded by memory |
| Latency profile | Single-digit-millisecond reads/writes at any scale | Microsecond operations because data lives in RAM |
| Pricing / ops model | Serverless pay-per-request or provisioned capacity plus storage; AWS-only, no servers to run | Open-source (self-hosted) or managed (e.g. Redis Cloud, ElastiCache); typically billed by node memory/throughput |
| Best-fit workloads | Durable records with predictable key access needing consistent latency at scale | Caching, leaderboards, counters, rate limiting, queues, pub/sub, ephemeral session data |
When DynamoDB is the better choice
- The data must survive. DynamoDB persists every write to disk and replicates it across Availability Zones. Redis is in-memory first; without RDB/AOF persistence a restart loses data, and even with persistence it is tuned for speed over guaranteed durability.
- You need a system of record on AWS. DynamoDB integrates natively with IAM, Lambda, and Streams, and offers point-in-time recovery and backups as configuration.
- Your working set is larger than memory. DynamoDB stores data on disk, so cost scales with storage rather than RAM. Redis capacity is bounded by the memory you provision.
- You want serverless scaling. On-demand capacity scales to traffic with nothing to size or patch.
When Redis is the better choice
- You need microsecond latency. Redis keeps data in RAM, so operations complete in microseconds — a step below DynamoDB's single-digit-millisecond profile.
- You need rich in-memory data structures. Sorted sets for leaderboards, atomic counters, lists for queues, and streams for event fan-out are first-class Redis operations, not something you model in a durable store.
- The data is ephemeral or cache-like. Session tokens, rate-limit windows, and computed results that can be regenerated fit Redis's in-memory model, often with a short TTL.
- You need pub/sub or a lightweight message broker. Redis provides publish/subscribe and stream primitives out of the box.
Using them together
The most common production pattern is not choosing one — it is layering them:
- Keep the durable records in DynamoDB as the source of truth.
- Put Redis in front as a read cache for hot keys, or use it for leaderboards, counters, and rate limiting alongside DynamoDB.
- If you want caching without running Redis yourself, DynamoDB offers DynamoDB Accelerator (DAX) — a fully managed, DynamoDB-API-compatible in-memory cache that reduces eventually consistent read latency from milliseconds to microseconds without application changes. DAX is read-through/write-through and caches items by primary key.
DAX versus Redis is itself a trade-off: DAX is DynamoDB-specific and drop-in, while Redis is a general-purpose data-structure store you can use across many data sources but must operate or pay a managed provider to run.
Working with DynamoDB
Once DynamoDB is your durable store, DynoTable is a native desktop client for browsing, editing, and querying your tables across macOS, Windows, and Linux. It reads your standard AWS credential chain, so there is nothing to migrate — point it at your Region and tables and your data stays in DynamoDB. Its SQL Workbench expresses relational-shaped queries within DynamoDB's access-pattern rules, and its AI assistant runs on your own AWS Bedrock credentials.
For building the key conditions, filters, and update expressions your caching and record-keeping 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 Redis replace DynamoDB?
Usually not as a system of record. Redis is in-memory first, so unless you enable and tune its persistence it is designed as a cache or ephemeral store, not a durable database. DynamoDB persists and replicates every write across Availability Zones. Many teams use both: DynamoDB for durable data, Redis (or DAX) as the fast in-memory layer in front of it.
Is DynamoDB or Redis faster?
Redis is faster per operation because it serves data from RAM in microseconds, while DynamoDB targets single-digit-millisecond reads and writes from durable storage. If you need DynamoDB with cache-like latency, DAX brings eventually consistent reads down to microseconds without leaving the DynamoDB API.
What is DynamoDB's equivalent of Redis caching?
DynamoDB Accelerator (DAX) is AWS's built-in, fully managed in-memory cache for DynamoDB. It is API-compatible, so existing DynamoDB calls work unchanged, and it caches items by primary key as a read-through/write-through layer. It is DynamoDB-specific, whereas Redis is a general-purpose data-structure store.
Related
- Learn when to use DynamoDB and how DynamoDB TTL expires ephemeral, cache-like items.
- Model durable access patterns with single-table design.
- Build expressions with the free DynamoDB Expression Builder.
- Download DynoTable to browse, query, and edit your DynamoDB tables.
Last verified 2026-07-12 against the official AWS DynamoDB Developer Guide and Redis documentation. Redis is a trademark of its respective owner; referenced here for identification only.