StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. FaunaDB vs Google Cloud Spanner

FaunaDB vs Google Cloud Spanner

OverviewComparisonAlternatives

Overview

Google Cloud Spanner
Google Cloud Spanner
Stacks57
Followers117
Votes3
GitHub Stars2.0K
Forks1.1K
Fauna
Fauna
Stacks112
Followers153
Votes27

FaunaDB vs Google Cloud Spanner: What are the differences?

## Introduction
FaunaDB and Google Cloud Spanner are two popular distributed databases that offer various features for developers. Below, we highlight the key differences between FaunaDB and Google Cloud Spanner.

1. **Consistency Models**: FaunaDB offers strong consistency by default, ensuring that all operations are immediately consistent across the database. On the other hand, Google Cloud Spanner provides external consistency, allowing developers to choose between strong or eventual consistency based on their requirements.
2. **Multi-Region Support**: FaunaDB is designed for global distribution out of the box, providing multi-region support without any additional configuration. In contrast, Google Cloud Spanner requires manual configuration to enable multi-region deployments, which can add complexity for developers.
3. **Pricing Structure**: FaunaDB offers a consumption-based pricing model, where users pay for resources used rather than pre-provisioned capacity. Google Cloud Spanner, on the other hand, follows a more traditional pricing model based on pre-allocated capacity, which can lead to over-provisioning in some cases.
4. **Query Language**: FaunaDB uses its own native query language called FQL (Fauna Query Language), which is a functional, SQL-like language optimized for distributed data operations. Google Cloud Spanner, on the other hand, supports SQL queries, making it more familiar for developers already comfortable with SQL.
5. **Data Modeling**: FaunaDB is schemaless, allowing developers to define data structures on the fly without needing predefined schemas. In contrast, Google Cloud Spanner requires schemas to be defined upfront, which can provide more control and structure but may be more rigid for certain use cases.
6. **ACID Transactions**: Both FaunaDB and Google Cloud Spanner support ACID transactions, but FaunaDB's transaction model is more granular, allowing for selective, fine-grained transaction operations across distributed data sets, while Google Cloud Spanner's transactions are more coarse-grained due to its distributed architecture.

In Summary, FaunaDB and Google Cloud Spanner differ in their consistency models, multi-region support, pricing structures, query languages, data modeling approaches, and transaction granularity, offering developers a choice based on their specific requirements and preferences.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Google Cloud Spanner
Google Cloud Spanner
Fauna
Fauna

It is a globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover.

Escape the boundaries imposed by legacy databases with a data API that is simple to adopt, highly productive to use, and offers the capabilities that your business needs, without the operational pain typically associated with databases.

Global transactions; Strongly consistent reads; Automatic multi-site replication; Failover.
Native support for GraphQL and others. Easily access any data with any API. No middleware necessary.; Access all data via a data model that best suits your needs - relational, document, graph or composite.; A unique approach to indexing makes it simpler to write efficient queries that scale with your application.; Build SaaS apps more easily with native multi-tenancy and query-level QoS controls to prevent workload collisions.; Eliminate data anomalies with multi-region ACID transactions that don't limit number of keys or documents.; Data-driven RBAC that combines with SSL to offers reliable protection, and yet is simple to understand and codify.; Travel back in time with temporal querying. Run queries at a point-in-time or as change feeds. Track how your data evolved.; Dynamically replicates your data to global locations, so that your queries run fast no matter where your users are.; Easily deploy a FaunaDB cluster on your workstation accompanied by a powerful shell and tools to simplify your workflow.;
Statistics
GitHub Stars
2.0K
GitHub Stars
-
GitHub Forks
1.1K
GitHub Forks
-
Stacks
57
Stacks
112
Followers
117
Followers
153
Votes
3
Votes
27
Pros & Cons
Pros
  • 1
    Scalable
  • 1
    Horizontal scaling
  • 1
    Strongly consistent
Pros
  • 5
    100% ACID
  • 4
    Generous free tier
  • 4
    Removes server provisioning or maintenance
  • 3
    Works well with GraphQL
  • 3
    Low latency global CDN's
Cons
  • 1
    Susceptible to DDoS (& others) use timeouts throttling
  • 1
    Log stack traces to avoid improper exception handling
  • 1
    Must keep app secrets encrypted
Integrations
MySQL
MySQL
PostgreSQL
PostgreSQL
MongoDB
MongoDB
SQLite
SQLite
No integrations available

What are some alternatives to Google Cloud Spanner, Fauna?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase