Caching is one of the most important aspects of building a highly scalable solution. At its core, caching involves storing a subset of data in such a way that it's either easier to retrieve or faster to retrieve. Some popular caching techniques are HTTP caching, CDN caching, and solutions like Redis and ElastiCache. As a backend engineer, it's very highly likely that at some point we'll work on AWS DynamoDB. And if for some reason caching is required, we might think between one or two options: ElastiCache or DAX. I'd like to cover a bit about DAX here, because the ease of operations it brings also comes with some limitations. So DAX stands for DynamoDB Accelerator. This caching solution has a strong connection to DynamoDB — the API it presents directly interacts with DDB. The way it achieves this is through a VPC, which helps directly instantiate a DAX cluster inside it. DAX supports almost all the same familiar DDB operations like GetItem, BatchGetItem (if you are wondering BatchGetItem is just a wrapper on multiple GetItem operation as mentioned in official documentation), Scan, Query, PutItem, with the exception being table modification APIs like CreateTable, UpdateTable, etc. The cache works in a read-through manner (similar to lazy loading but automated): it first checks whether the data is available in cache, and if not, it calls DDB and then updates the entry in cache. Consistency is maintained in a write-through manner by default — but the order is: write to DynamoDB first, then update the cache. For cases of high write throughput, a write-around cache is also available as an option where bulk writes go directly to DDB without touching the cache. Ideally, the access patterns make DAX a good fit for low-write / high-read throughput because of eventual consistency (another thing to note: stale data is a possibility, although the latency metric for eventual consistency is marked as 10–100ms). Some unique points about DAX: Negative caching: Upon a cache miss where the source of truth (DDB) also returns an empty record, DAX will cache that empty result for the configured TTL (default 5 minutes). This applies to both GetItem and Query/Scan. To avoid unexpected empty responses, you'd need manual code configuration (e.g., using a sentinel value). Region specificity: There's no cross-region replication available in DAX. So a write in one region might mean DDB replicates to another region (e.g., with Global Tables), but DAX in that other region still won't be updated. Important to note if cross-region reliability is critical. Also, DAX is supported in some AWS regions and not in all, so before planning integration, better check the region list. When to use DAX instead of ElastiCache You need microsecond latency for DDB reads: DAX is built for one job: making DynamoDB reads scream. It can reduce latency from milliseconds to microseconds for cached reads. Your read-to-write ratio is high: If your app does mostly reads, DAX is a perfect fit. It shines when you're hitting the same keys repeatedly. You want to reduce DDB read costs: Every cache hit on DAX is a read request that doesn't hit your DynamoDB table, potentially saving you money on read capacity units (RCUs) if your cache hit ratio is high enough. When to not use DAX (and use ElastiCache instead) Your data is highly dynamic or write-heavy: DAX works best for data that doesn't change often. If you're constantly updating items, your cache hit ratio plummets, and you're just adding extra overhead. You need complex data structures: DAX caches entire DynamoDB items. ElastiCache for Redis gives you advanced data types like lists, sets, sorted sets, and hashes — great for leaderboards, queues, and real-time analytics. You're caching data from multiple sources: DAX is a dedicated DynamoDB service. If your application also needs to cache data from RDS, an API, or your own backend, ElastiCache is the general-purpose solution. You need strong consistency for reads: DAX only supports eventually consistent reads for cached items (strongly consistent reads go straight to DDB). If your application can't tolerate even a few seconds of stale data, ElastiCache with its own invalidation logic might be a better fit. References: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/DAX.concepts.html