Local-first software, embedded devices, and AI-on-the-edge workloads rely on in-process local databases where concurrency is low but performance, portability, and capability at the edge are critical.
However, most database comparisons focus on _server performance_. While handling thousands of concurrent connections, cloud clusters, and distributed replication are important for large-scale cloud-first databases, these comparisons miss the point when you're dealing with local-first databases.
Instead of measuring how many thousands of users can simultaneously query a remote server, local-first databases need to efficiently handle complex queries on a single device with limited resources. Rather than worrying about network latency between distributed nodes, you're concerned with memory footprint and battery consumption. You're asking questions like: Can this database run reliably on a smartphone? Does it gracefully handle intermittent connectivity? Can it sync efficiently when the network returns? And does it rely on cloud services to perform syncs?
Both SQLite and DefraDB are in-process databases designed to run on-device or in the app itself, not over the network, and both prioritize portability and developer ergonomics. However, their goals and capabilities differ significantly for modern use cases like offline-first applications that must sync data elsewhere, real-time collaboration, edge inference and AI, and secure and verifiable local storage.
This article explores how these databases stack up across read/write throughput, data models, collaboration capabilities, AI readiness, trust mechanisms, and runtime integration to help you understand when to use one over the other.
Read/Write Throughput
Even though read-write throughput isn't as big a focus as with centralized databases benchmarks, it's still an important consideration, because aspects like local concurrency can play a big role in applications that write to the database often.
Also, keep in mind that throughput is heavily influenced by the hardware on which the database is running, so any available benchmarks will vary wildly depending on hardware use.
SQLite
If you're running SQLite on a high-end Android or iOS device from the last few years, it can be expected to handle somewhere in the range of 200k+ inserts per second and 400k+ reads per second, depending on query complexity, schema complexity, row size, and other such variables.
If throughput is an important factor in your application, you can optimize for it by enabling Write-Ahead Logging (WAL). Compared to the default configuration, you get faster read/write operations and improved concurrency, since readers and writers will not block each other.
DefraDB
DefraDB's performance characteristics reflect different optimization priorities than traditional single-node databases.
DefraDB's multi-writer architecture enables concurrent writers across distributed nodes without requiring coordination protocols or locks. This means multiple users can simultaneously modify the same documents across different nodes, with conflicts automatically resolved through deterministic CRDT algorithms. While individual operations may have higher latency due to cryptographic processing (generating content identifiers, maintaining Merkle DAG history, and peer-to-peer networking overhead), the system eliminates the coordination bottlenecks that impact traditional multi-master setups.
This performance trade-off is fundamental to DefraDB's value proposition: instead of optimizing for raw single-node throughput, it prioritizes conflict-free collaboration and cryptographic verifiability.
Data Model and Schema
The way you define, evolve, and query your data structures impacts developer ergonomics and long-term maintainability, especially when dealing with complex or evolving application requirements.
SQLite
SQLite implements a relational model that pairs the familiarity of SQL with dynamic typing capabilities. Unlike traditional SQL databases that enforce strict column types, SQLite allows values of any type in any column while still supporting optional type enforcement through STRICT tables. This gives you the convenience of flexible data entry and the safety of rigid constraints when needed.
Schema definition follows standard SQL patterns with CREATE TABLE statements, foreign key relationships, and check constraints. SQLite supports referential integrity through CASCADE and RESTRICT actions, enabling the sophisticated data modeling patterns you'd expect from a SQL-based system. However, schema evolution requires careful planning because of SQLite's limited ALTER TABLE capabilities. While you can add columns and rename tables, complex schema changes necessitate a twelve-step procedure involving creating new tables, copying data, and dropping old structures.
DefraDB
DefraDB uses a document-oriented model built on Merkle CRDTs with schema definition through DQL (DefraDB Query Language), which maintains GraphQL compatibility while adding database-specific extensions. Documents contain flexible JSON-like structures that naturally handle nested data and complex relationships without requiring normalization.
Schema definition resembles modern API development patterns, with schema types defined as they would be in GraphQL, like so:
Schema evolution uses LensVM transformations for sophisticated migrations through WebAssembly-based bidirectional transformations. Unlike SQLite's table recreation requirements, DefraDB supports gradual schema changes while maintaining backward compatibility through lens functions. Each schema change creates a new version while preserving access to historical data states, enabling time-traveling queries to retrieve documents as they existed at any point in their history using content identifiers (CIDs).
Because of the MerkleCRDT foundation, every document change creates an immutable commit in the Merkle DAG. You get complete history preservation and conflict-free replication across distributed nodes without needing coordination protocols.
Collaboration and Sync
Single-writer versus multi-writer architectures create fundamentally different constraints for applications that need to handle concurrent data modifications. These constraints can impact the kinds of applications that the underlying database can support effectively.
SQLite
SQLite's single-writer architecture fundamentally limits collaborative scenarios, as only one process can modify the database at any time. While Write-Ahead Logging (WAL) mode enables concurrent readers during write operations, this design creates inherent bottlenecks in multi-user environments where multiple users need simultaneous write access.
Third-party solutions can extend SQLite's collaborative capabilities through replication layers. Litestream provides streaming replication to cloud storage, rqlite implements Raft consensus for distributed SQLite clusters, and dqlite offers optimized clustering for edge computing.
However, these solutions maintain SQLite's fundamental single-writer model and require additional infrastructure like web servers, APIs, and application-level conflict resolution mechanisms for multi-user scenarios. This kind of sync is not a feature of SQLite but a pattern that can be implemented alongside other cloud-based backing technology. The additional overhead added by these requirements is non-trivial and needs to be maintained on an ongoing basis, making SQLite a less attractive option if sync and collaboration are significant focuses in your application.
DefraDB
DefraDB natively supports multi-writer collaboration through Merkle CRDTs, enabling unlimited concurrent writers across distributed nodes without additional coordination overhead. Multiple users can modify the same documents simultaneously across different nodes, with conflicts automatically resolved through deterministic merging algorithms based on content addressing and DAG structures.
Peer-to-peer synchronization operates through LibP2P networking with pubsub peering for passive synchronization and replicator peering for active targeted synchronization. The built-in Access Control Policy (ACP) system provides relationship-based authorization with enforcement distributed across the P2P network, while an offline-first design enables full operation during network partitions with automatic synchronization when connectivity resumes. Connectivity doesn’t necessarily mean the internet, either. With peer-to-peer syncing, you can sync with other devices on the local network, completely bypassing the internet and cloud services for syncing. This can potentially save significant time and energy when you consider the high round-trip cost of a cloud connection compared to a local network one.
Vector Search and AI Readiness
If AI functionality is important to you, vector support determines how seamlessly you can integrate embedding storage and similarity search into your application architecture.
SQLite
Though SQLite itself lacks vector search capabilities, you can add them through the sqlite-vec extension. This extension offers a pure C implementation that supports float32, int8, and binary vector types with dimensions reaching thousands of elements.
AI framework integrations exist for major platforms through sqlite-rembed, which connects to OpenAI, Nomic, Ollama, and HuggingFace APIs for embedding generation. LangChain provides native SQLite vector store support (using sqlite-vec), which is useful for recommendation systems, semantic search, and RAG implementations.
DefraDB
DefraDB offers native vector embedding support and a flexible document storage foundation that can accommodate AI workloads through its JSON-compatible schema.
DefraDB's architecture provides unique advantages for distributed AI applications, including content-addressable storage that enables cryptographic verification of training data integrity and model outputs. The immutable history through Merkle CRDTs provides complete audit trails for AI training data and model decisions.
Offline-first capabilities enable AI inference across edge devices with automatic synchronization, making it suitable for federated learning scenarios where data integrity and distributed coordination outweigh raw vector search and query performance.
Trust and Verifiability
Traditional security models versus cryptographic verification approaches offer different guarantees about data integrity and tamper detection.
SQLite
SQLite uses traditional database security mechanisms with optional encryption through SQLite Encryption Extension (SEE) or SQLCipher. AES-256 encryption provides data confidentiality, while ACID properties ensure transaction integrity, and pragma integrity_check can detect database corruption.
SQLite's security model relies heavily on file-based permissions and having a secure environment, as it lacks built-in user authentication or network security capabilities. The database assumes a trusted execution environment and requires external systems for access control and audit logging.
DefraDB
A key benefit of DefraDB is having comprehensive cryptographic verification at its architectural foundation through content-addressable storage. Every data block receives a unique Content Identifier (CID) based on cryptographic hashing. Any data tampering immediately becomes detectable through hash comparison, eliminating the possibility of silent modifications and providing proof of data integrity.
The use of Merkle CRDTs creates tamper-evident audit trails through immutable Merkle DAGs where every document change generates a new commit that's linked to previous states. This structure makes historical revision impossible: data must be appended; it can never be modified retroactively.
The distributed trust model eliminates single points of failure through peer-to-peer verification. Each node can independently verify integrity without requiring trust in centralized authorities or other peers.
DefraDB’s access control capabilities also let you set permissions as granular as per-field permissions for fine-grain control over who is allowed to access what data.
Language and Runtime Integration
How well a database integrates with your application and programming language can have a notable impact on developer ergonomics
SQLite
SQLite offers excellent ecosystem maturity with native bindings available in virtually every programming language. Python includes SQLite3 in its standard library, while Node.js, Go, Rust, Java, C#, PHP, and Ruby all maintain actively developed SQLite drivers with extensive ORM support and community contributions.
Development productivity benefits from familiar SQL syntax, minimal configuration, and rich tooling ecosystems. Database files require no installation or setup procedures, in-memory databases enable fast testing cycles, and the single-file architecture makes production deployment as simple as copying individual files.
DefraDB
DefraDB provides comparable deployment flexibility without the typical ecosystem limitations you might assume would be present for newer database technologies. Written in Go, DefraDB compiles to single binary executables that run across platforms without external dependencies, matching SQLite's deployment simplicity while adding distributed capabilities.
DefraDB offers multiple integration options. If you want to, you can run it in-process as part of your application. If you'd prefer to run it as a dedicated process and connect via networking protocols, there is also support for HTTP/GraphQL APIs.
The cross-platform compatibility is made possible by Go's compilation capabilities, producing statically-linked binaries for a range of platforms. These include ARM processors and embedded systems, with more bindings being planned and delivered, including iOS, Android, and WASM. If you need a specific binding that's not yet available, you can request it via the official Discord.
When to Choose SQLite
SQLite's strength is in simple, resource-constrained environments like caching and config storage, logging and local record-keeping, and embedded firmware on systems with minimal CPU/RAM resources. It's still a good choice if your application is strictly local and requires minimal or no logic around syncing and sharing.
SQLite has also been around for a long time, and many developers are familiar with it. Historically, it's been a solid choice for local applications, so many developers will opt for it by default.
The risk of this familiarity is not considering options that are more suitable for your use case. For instance, while SQLite might work well enough for your app now, will it need to support sync, multi-device data, verifiability of data, or privacy-aware collaboration in the future? Trying to force SQLite to do things it wasn't designed for and having to switch solutions later typically comes at a higher cost than starting with a system that is built for these cases. It also comes with ongoing maintenance costs and is hard to predict at the outset.
When to Choose DefraDB
DefraDB excels in cases where your application must work with other instances and applications over the network, whether it is offline-first with sync, or always-online but with processing at the edge.
If your application would benefit from features like syncing across peers and environments, privacy-preserving logs, user-controlled data, and AI at the edge—now or in the future—DefraDB is an excellent fit.
DefraDB does have overhead that makes it excessive for the simple scenarios that SQLite excels at. So if you only need a local config cache, you probably don't need DefraDB.
Wrapping Up
If you're building a fully local application without any sync or strong data-trust requirements, SQLite is a simple choice to get you up and running. Applications that fall into this category might be IoT sensor logging on a single-board computer, data caching for a mobile application, or storing local config and settings for a desktop application.
However, if you're building edge-first or offline-first applications that need to sync data somewhere—either to peers or to a centralized store—or if you have strong data security and traceability concerns, DefraDB has the capabilities you need. Think: critical infrastructure, industrial IoT, or healthcare technology that stores personally identifiable patient data.
If this kind of functionality would add value to your app, check out DefraDB to see how it can help you.
