DynamoDB vs Google Firestore: Which should you choose?

Choose DynamoDB for AWS applications built around predictable key-based reads and writes, with managed throughput scaling and event processing. Choose Firestore in Native mode when Firebase client SDKs, real-time listeners, and offline synchronization are central to your web or mobile application. Both support transactions and automatic scaling; the right comparison depends on Firestore's edition and access mode, not a blanket claim that one database is faster.

Amazon DynamoDB and Google Firestore: an overview

DynamoDB is a managed key-value and document database. You define table and index keys, and AWS handles the underlying servers and storage distribution. It integrates with IAM and services such as Lambda through change streams.

Firestore is Google's managed document database. In Native mode, its data model uses documents in collections, with subcollections for hierarchical organization. Documents can hold different fields, but indexes and application validation still need deliberate design.

Both remove database-server provisioning. They do not remove the need to manage query costs, access control, hot data, backups, and application-level conflict handling.

First choose the Firestore edition and mode

The Firestore edition documentation now distinguishes Standard and Enterprise. An edition and an access mode are separate choices:

Enterprise Native mode offers Core operations and Pipeline operations. Core operations provide document CRUD, real-time listeners, and offline support. Pipeline operations add richer query composition, including relational-style joins through sub-pipelines. Do not assume that a Pipeline query has every listener or offline behavior of a Core query.

Enterprise with MongoDB compatibility exposes MongoDB-compatible operations and drivers. It is still Firestore, not a self-managed MongoDB cluster. Check the compatibility documentation for the commands and behavior your application uses before treating it as a migration target.

The comparison below focuses on Native mode, with edition differences called out. Firestore in Datastore mode has a different API and does not provide the Firebase mobile/web experience described here.

DynamoDB vs Firestore at a glance

See DynamoDB constraints and Firestore quotas for limits. The Native-mode API request limit is 10 MiB, and transactions have a 270-second time limit with a 60-second idle expiration.

Shared attributes and query design

Both databases store flexible documents and maintain indexes, but they expose different query models. DynamoDB's efficient reads start from table or index keys. Firestore Core queries target documents, collections, or collection groups and use its query/index rules.

For an order history, DynamoDB might use a customer partition key and a time-based sort key. Firestore might store order documents with customer and creation-time fields and index the desired query. Neither design automatically answers every future reporting requirement.

Standard Firestore requires indexes for queries and automatically indexes individual fields. Enterprise has more configurable indexing and can execute unindexed queries, but a collection scan can still cost time and money. Verify the exact query engine before carrying Standard-edition advice into an Enterprise design.

Performance

DynamoDB

DynamoDB is designed for low-latency access using well-distributed keys. That does not guarantee identical response time for every table size or operation. Item size, number of requests, network distance, hot keys, retries, and index behavior affect latency.

DAX can cache suitable reads. Its cache-hit latency is not a promise for uncached reads, writes, or whole application requests. Start with a sound data model and measure the actual workload before adding a cache.

Firestore

Firestore's reads and writes architecture uses replicated storage and transactions. A successful server read and a fast local-cache read have different freshness properties, so benchmark them separately.

The old claim that Firestore cannot serve responses below a fixed latency threshold is not a useful product guarantee. Nor is a daily free allowance a measure of maximum throughput. Choose a location close to the application, measure tail latency, and test representative document sizes, query shapes, and update contention.

Real-time listeners and offline applications

Firestore Core snapshot listeners deliver an initial result and subsequent changes. This is convenient for chat, shared task lists, dashboards, and user-visible state updates. Listener result size and update frequency affect both client work and billing; an unrestricted collection listener can become expensive.

Offline persistence lets supported clients read cached data and synchronize local changes after reconnecting. Availability and defaults differ by platform. Cached results can be incomplete or stale, and concurrent updates still need application rules for conflict handling.

Transactions are not an offline synchronization mechanism. Firestore's transaction guide explains that transactions can retry and fail when the client is offline. Keep external side effects out of callbacks that may run again. Batched writes serve a different purpose: applying known writes atomically without a transaction's read-dependent logic.

DynamoDB can underpin a real-time application, but DynamoDB Streams is a server-side change log, not a browser listener or offline cache. You need a delivery and synchronization layer appropriate to the application.

Availability and durability

DynamoDB replicates across Availability Zones in a Region. Global tables support multi-Region eventual or strong consistency with different feature restrictions. Choose consistency based on the business invariant, not only the nearest replica.

Firestore offers regional and multi-region database locations. Google's location documentation describes the available configurations and replica roles. A multi-region location improves resilience to regional failures but does not make every dependent application service immune to outages.

This existing diagram illustrates one multi-region layout. Check the location documentation for current region choices rather than treating the illustrated locations as the only deployment option.

Replication is separate from recovery after an accidental delete. Test application failover, client reconnection, and restore procedures for the selected deployment.

Security

DynamoDB encrypts stored data and supports TLS connections, IAM policies, resource-based policies, and AWS KMS key choices. Use narrowly scoped roles and table/index permissions. Transport encryption is not the same as client-side encryption that hides plaintext from the database service.

Firestore also encrypts data before writing it to disk, with Google-managed or customer-managed key options. For direct mobile and web access, use Firebase Authentication with Firestore Security Rules to validate requests and restrict document access.

Server client libraries bypass those Security Rules and use IAM-based authorization. A rule that protects a mobile client therefore does not constrain a broadly privileged server service account. Review both paths and test access-denied cases, including cross-user and cross-tenant requests.

Backups

DynamoDB offers on-demand backups and PITR with a configurable 1-to-35-day recovery window. A restore creates a new table; recovery includes reconnecting the application and checking table settings. AWS Backup can provide centralized policy management.

Firestore now has managed backup schedules and restores, so a custom scheduled export is not its only backup option. Point-in-time recovery retains document versions for up to seven days when enabled. Backups, PITR, exports, and restores are distinct features with their own costs and recovery paths.

Configure protection before an incident and verify a restore into a separate environment. Keeping data in a highly available service does not protect against valid but incorrect writes from your own application.

Scalability and limits

DynamoDB on-demand mode automatically adjusts throughput within its scaling behavior and quotas. Provisioned mode can use auto scaling. Warm throughput helps prepare for known spikes, but a single hot key or poorly distributed index can still throttle. An adjustable table quota is not the maximum the service can ever support.

Firestore's scaling best practices focus on avoiding hotspots, reducing unnecessary index fanout, and gradually increasing traffic to new collections. A high write rate to one document is different from the same rate distributed across independent documents.

Do not size either database from a universal requests-per-day threshold. Test the busiest key or document, transaction retry rate, index overhead, and traffic ramp. Those often reveal a design problem sooner than a service-wide throughput comparison.

Pricing

DynamoDB pricing offers on-demand read/write requests or provisioned capacity. Include item-size rounding, index maintenance, replicated writes, storage, backups, and optional DAX. The pricing calculator helps compare capacity modes for a representative workload.

Firestore Standard pricing includes document reads, writes, deletes, applicable index-entry reads, storage, and network usage. Aggregation costs depend on index entries read. Listeners can generate billable reads as results change and when queries reconnect under the documented rules.

The Standard free allowance includes 50,000 document reads per day for an eligible database. That is a billing allowance, not a performance ceiling. Backup, restore, and PITR charges also need separate consideration.

Enterprise pricing uses read and write units based on bytes processed, with separate storage and other charges. Do not multiply a Standard per-document price by Enterprise traffic. Model query work, document sizes, listener behavior, and indexes for the selected edition.

Use cases: which database should you choose?

Choose DynamoDB for AWS-local sessions, carts, game state, device state, and content metadata with defined access patterns. Both small and large applications can fit. Inventory and payment workflows need conditional writes or transactions designed around their correctness requirements.

Choose Firestore Native mode when your product benefits from the Firebase client model: offline field applications, chat, collaborative interfaces, or live user-specific views. Enterprise can add query flexibility, but check feature-specific SDK and mode support before making it a requirement.

There is no single DynamoDB equivalent in GCP. Firestore is often the relevant document-database comparison; Spanner is relevant when distributed SQL transactions are the central requirement. Compare the actual query and client experience you need, not just the cloud vendor.

Can you use both together?

An application can keep authoritative backend records in DynamoDB and publish a limited Firestore view for a client experience, but that adds a second database and a cross-cloud synchronization path. Treat this as an architectural choice with a clear benefit, not a default integration.

Use one writer of record for each entity, idempotent updates, version checks, and reconciliation. Define what users see during replication lag or an outage. A transaction in one database does not atomically commit a write in the other, and copying sensitive data creates another access-control boundary.