When delivering AI-powered applications, a critical development consideration is data storage and processing. Fast, reliable data access is crucial for AI-powered features, and the key to a great user experience. A cloud-only database is great for fixed-location apps with fast, reliable internet connectivity. But for apps at the edge, such as mobile or IoT apps, cloud-only databases are problematic; internet latency and outages can hamper AI responses, ruin the user experience, and lead to business downtime.
Embedded Database to the Rescue
Using an embedded database, operating directly within an app on device, solves internet dependency issues. Apps access and save data locally versus in the cloud. The app becomes less reliant on the internet, faster, and more available.
SQLite is the most well-known embedded database, and might seem an obvious choice for mobile or IoT app developers. But being well-known does not make it the best option for AI applications at the edge. Read on to learn why.
What Is SQLite?
SQLite is a lightweight, self-contained SQL database engine that operates as an in-process library embedded directly within the host application. It is open source and easy to use, making it a good option for freestanding or “air-gapped” apps where data is saved and accessed locally without requiring a network connected database. This is what makes SQLite a popular choice for standalone applications that need to operate without internet connectivity or direct access to a cloud backend database.
Where Does SQLite Shine?
SQLite is great for apps designed to work in isolation – such as personal health monitors, notes organizers, or sketching apps – where there is a single user, data does not need to be shared, and the user experience begins with a relatively blank slate where data is collected.
When the above criteria apply, developers can use SQLite in their mobile, desktop, and IoT applications to store personal information, logs, device readings, user-created content, and other sorts of data.
Because it eliminates the need for a separate connected database, SQLite also eliminates the latency and downtime that are common with internet-connected apps.
With all this upside, it might seem that SQLite is an ideal solution for any edge computing app – including those with AI features – but it has limitations that developers need to know, as well as better alternatives.
AI at the Edge
Enterprise-scale mobile and IoT apps are global in scope, support many users who share information, and require high guarantees of speed and uptime. Edge computing is an alternative architecture to cloud computing for apps where ultra-low latency and high availability are paramount requirements. The overarching goal of edge computing is to eliminate internet dependencies by moving data processing closer to applications by using edge data centers and embedded data storage directly on devices. It’s important to note that edge computing does not replace the cloud for running apps; more accurately, it’s an architecture that extends data processing from cloud to edge.
“Edge AI” is a concept that includes AI processing into edge computing architectures. This involves running LLMs alongside the database in each architectural layer from cloud to edge, including SLMs on-device. These sorts of topologies bring the same guarantees of speed and uptime for apps to the AI models that power their features.
SQLite, an embedded database, can run on edge devices and may seem like a good fit for an edge AI architecture. However, if your app requirements go beyond the simple ones described earlier, you may want to reconsider.
Top 5 Reasons SQLite Is Wrong for Edge AI
Achieving the edge AI architecture requires a database that can run in isolation and, critically, securely synchronize data between the cloud and other app clients, while integrating seamlessly with AI models wherever they are hosted.
Here are five reasons SQLite struggles in an edge AI architecture:
- Inflexible data model: SQLite is relational and follows a rigid schema, which can make it a challenge to articulate the model requirements for your apps in an efficient and appropriate way. Because of this rigidity, doing something as simple as adding a new field to the database can require an update of the entire schema. This means a new release of your mobile app with an updated data schema will require expensive database schema migrations to be performed on app launch, adding to your app startup costs. SQLite’s relational model also limits the data formats it can support, which in turn limits AI accuracy and context. GenAI requires a broad range of data formats, so the more the database can support, the better. An alternative is to use a NoSQL database that supports JSON document data storage, which can handle massive amounts of data in multiple formats – ideal for AI.
- No built-in data synchronization: For multi-user apps that go in and out of connectivity, data synchronization is what provides consistency for a great user experience – as well as the fastest, most accurate AI responses. Data sync also enhances security; if a user permission changes, data sync instantly reflects the change across the app ecosystem to ensure that no one accesses something they shouldn’t. SQLite does not support data synchronization out of the box, developers must build their own solution or integrate with third-party solutions, complicating the architecture and stealing development focus away from the core app functionality.
- No enterprise-grade backend database: Even with local data storage, a mobile app still needs a backend database as a central aggregation point for data; this is how a distributed mobile app ecosystem remains scalable and performant. As such, a backend database server – often deployed in the cloud – is an important part of the edge AI architecture. SQLite does not offer a freestanding scalable database, it is solely an embedded database. In order to gain a scalable backend for apps using SQLite you must integrate with a third-party database server technology. This makes deployments more complex and maintenance and upgrades more time-consuming for developers.
- No enterprise-level security: When using synchronized and decentralized data storage, it’s important to access, transmit, and store data securely. To cover this completely, you need to address authentication, data at rest, data in motion, and read/write access control. SQLite does not natively support role-based access or data encryption. If stringent data security is important – as it certainly is for AI – developers must build their own security integrations leveraging third-party security extensions.
- No vector search: For GenAI features such as conversational chatbots, recommenders, or AI-assistants, vector search enables easy integration with LLMs (large language models) through techniques such as retrieval-augmented generation (RAG) where the current local vector data is passed along with prompts to provide better precision and context for LLM responses. SQLite does not support vector search, meaning it cannot be used for RAG-based features or semantic search on-device, completely collapsing the edge AI benefits.
In short, when building and deploying enterprise-class, high-scale AI-powered applications at the edge, you will arguably face many development hurdles if you go with SQLite.
Couchbase Mobile: The Right Database for Edge AI
Don’t lose time trying to develop around SQLite’s shortcomings when building and deploying AI-powered apps at the edge. Instead, use an off-the-shelf cloud-to-edge sync solution and free your team up to work on making the app the best it can be!
Couchbase Mobile embeds data processing and vector search directly into applications, and synchronizes data from cloud to edge and between devices – even without an internet connection – to deliver the fastest, most reliable AI-powered apps. The product stack includes:
Enterprise-scale backend cloud database
Couchbase is a high-scale, multipurpose NoSQL JSON document database platform for building and deploying GenAI applications and agentic systems. It is memory-first, distributed, and natively supports vector search at massive scale. Use Couchbase Capella, our hosted Database-as-a-Service, or install and manage Couchbase Server on your own public or private cloud.
Embedded mobile database
Couchbase Lite is an embeddable version of Couchbase for mobile and IoT apps that stores data locally on the device. Like its server counterpart, Couchbase Lite stores data as JSON documents, supports vector search – critical for edge AI – and provides built-in security and granular data access control. It also includes a Predictive Query feature specifically designed for calling AI models such as image classifiers.
Secure mobile data sync
Couchbase Mobile provides data synchronization out-of-the-box, both peer-to-peer and cloud to edge. Choose to use hosted data sync with Capella App Services, or install and manage Couchbase Sync Gateway yourself.
Conclusion
By offering the combination of a high-scale backend database, a powerful embedded database, vector search from cloud to edge, and comprehensive data synchronization, Couchbase Mobile is the only choice for building and deploying secure, resilient, offline-first edge AI applications that deliver sub-second responsiveness and 100% uptime.
Enterprise customers using Couchbase Mobile for their own mission-critical apps at the edge include:
PepsiCo: PepsiCo’s 30,000 field sales reps use a Couchbase Mobile powered app to perform sales operations in the field, including placing orders, merchandising stores, and managing sales in stores without disruption, even without an internet connection. Learn more about the PepsiCo use case here.
United: United’s 41,000+ pilots, flight attendants, and flight schedulers use a mobile crew scheduling application built with Couchbase Mobile to streamline work processes and simplify data management. Learn more about the United use case here.
PG&E: PG&E relies on a Couchbase Mobile powered app to provide its gas and electric power inspectors with real-time data in the field, even when they’re offline, improving incident response and safety. Learn more about the PG&E use case here.
Learn more about Couchbase Mobile at www.couchbase.com/mobile, and sign up for the Capella App Services Free Tier at cloud.couchbase.com/sign-up.
