Redis vs DynamoDB for Solo Developers

Quick Comparison

Redis Overview

Redis is the universal speed layer for web applications. In-memory data storage with sub-millisecond latency for caching, sessions, rate limiting, job queues, and real-time messaging. It works with any tech stack, any cloud provider, and any deployment model. Self-host it for free, or use a managed service for a few dollars per month.

The beauty of Redis is its simplicity. Connect, store data, retrieve it instantly. The learning curve is gentle, the documentation is excellent, and every programming language has mature client libraries. I can add Redis to any project in under an hour, and the performance improvement is immediate and measurable.

Data structures like sorted sets, streams, and hashes solve specific problems without workarounds. Need a real-time leaderboard? Sorted set. Need a message queue? Redis Stream or List. Need to cache complex objects? Hash. Each structure is optimized for its use case.

DynamoDB Overview

DynamoDB is AWS's fully managed NoSQL database. Single-digit millisecond reads and writes at any scale. Zero server management, automatic scaling, and integration with the broader AWS ecosystem. It's designed for applications that need consistent performance regardless of data volume.

The free tier includes 25GB of storage, 25 write capacity units, and 25 read capacity units. For low-traffic applications, this covers basic CRUD operations. Pay-per-request pricing eliminates the need to provision capacity, but costs can escalate quickly if your access patterns are inefficient.

The challenge with DynamoDB is data modeling. It forces you to think about access patterns before writing any code. No joins, limited querying without secondary indexes, and a partition key design that determines your application's scalability. Coming from SQL or even MongoDB, DynamoDB's data modeling feels alien. You're designing your table structure around how you'll query it, not around what your data looks like.

Key Differences

Operational model. DynamoDB is fully managed by AWS. No servers, no upgrades, no maintenance. Redis requires either self-hosting (you manage the server) or a managed service (someone else manages it). DynamoDB wins on zero-ops convenience, but ties you to AWS.

Data modeling complexity. Redis has a simple model: choose a data structure, assign a key, store your data. DynamoDB requires careful upfront design of partition keys, sort keys, and access patterns. Get the data model wrong, and you'll hit hot partitions, expensive scans, and poor query performance. Redis is forgiving. DynamoDB punishes poor design.

Query flexibility. Neither Redis nor DynamoDB offers the flexible querying of SQL databases. But Redis doesn't pretend to. It's designed for key-based lookups and data structure operations. DynamoDB lets you query on partition key plus sort key, and secondary indexes add more access patterns. But if you need to query your data in ways you didn't plan for upfront, both tools will frustrate you.

Vendor lock-in. DynamoDB is AWS-only. Your data model, SDK calls, and infrastructure are bound to Amazon. Migrating away means rewriting your data layer entirely. Redis is open source with multiple cloud providers and self-hosting options. Switching Redis providers means updating a connection string.

Latency. Redis delivers sub-millisecond reads from memory. DynamoDB delivers single-digit millisecond reads from SSDs. For most applications, both are fast enough. But when you need microsecond performance (rate limiting at high throughput, real-time game state), Redis is the faster option.

Cost predictability. Redis has predictable costs: you pay for the instance size or per-request on serverless plans. DynamoDB's costs depend on read/write patterns, data size, indexes, and whether you use provisioned or on-demand capacity. Solo developers have been surprised by DynamoDB bills when access patterns change unexpectedly.

Use case scope. DynamoDB is a primary database for storing persistent application data. Redis is a supplementary data store for caching, sessions, and real-time features. A well-architected AWS application might use DynamoDB for persistence and ElastiCache (managed Redis) for caching.

When to Choose Redis

• You need caching, sessions, rate limiting, or background job queues
• Sub-millisecond data access is critical for hot paths
• You want portability across cloud providers without vendor lock-in
• Real-time pub/sub messaging is a requirement
• Simple, predictable pricing matters to your budget

When to Choose DynamoDB

• You're building on AWS and want a fully managed, zero-ops database
• Your access patterns are well-defined and won't change frequently
• You need unlimited scale without capacity planning
• The AWS free tier aligns with your early-stage usage
• Your application is event-driven with Lambda and API Gateway

The Verdict

For solo developers, Redis is the more practical choice in almost every scenario.

Redis is simpler to learn, cheaper to run, portable across providers, and solves universal application needs (caching, sessions, queues). You can be productive with Redis in an afternoon.

DynamoDB is a capable primary database, but its steep learning curve, inflexible data modeling, and AWS lock-in make it a tough recommendation for solo developers. The 5/10 rating reflects the reality that simpler alternatives exist. PostgreSQL (via Neon or Supabase) gives you flexible querying, standard SQL, and no vendor lock-in. Redis gives you the speed layer.

If you're deeply invested in the AWS ecosystem and your access patterns are well-defined, DynamoDB works. But most solo developers are better served by PostgreSQL as a primary database plus Redis as a cache, session store, and utility layer. That combination is more flexible, more portable, and easier to reason about than DynamoDB alone.

The one scenario where DynamoDB shines for solo developers: serverless architectures with Lambda. DynamoDB integrates natively with Lambda triggers, API Gateway, and the broader AWS serverless stack. If that's your world, DynamoDB fits naturally. For everyone else, PostgreSQL plus Redis is the better path.