Ceramic Streams vs Textile ThreadDB

Global, verifiable data: Streams are anchored to the Ceramic mainnet, providing a single source of truth. This matters for decentralized identity (DID), portable user profiles, and composable application data where verifiability across apps is critical.

Private, application-specific databases: Threads are encrypted, user-owned collections that never touch a public ledger. This matters for private messaging, personal data lockers, and enterprise use cases requiring GDPR/CCPA compliance and data isolation.

Specific advantage: Universal Data Model. Streams use CIPs (Ceramic Improvement Proposals) like CIP-11 (TileDocument) and CIP-79 (DAG-JOSE) for standardized, interoperable data. This matters for building cross-app ecosystems (e.g., IDX for identity) where data schemas must be shared and understood by multiple clients.

Specific advantage: End-to-End Encryption & Local-First Architecture. Data is encrypted with user-held keys and syncs via IPFS/libp2p pubsub. This matters for user-centric applications where data sovereignty and offline-first capabilities are non-negotiable, avoiding vendor lock-in.

Specific consideration: Requires managing a stream ID, DID, and anchoring transactions. Public data has associated gas costs (on mainnet) or requires a Ceramic node. This matters for projects needing simple, cost-free prototyping or handling purely private data.

Specific consideration: Threads are private by default, making public discovery and global composability challenging. It lacks a native, standardized schema registry like Ceramic's. This matters for developers prioritizing network effects and public data interoperability over absolute privacy.

Streams are globally addressable and composable assets on a unified network. A single stream ID (e.g., kjzl6k...) can be referenced and updated by any application, enabling true data portability. This matters for building cross-application user profiles (like a portable social graph) or shared datasets where multiple protocols need a single source of truth.

Native support for versioned, upgradeable data schemas via the Tile Document stream type. Developers define a JSON Schema (e.g., for a Profile or Article), and the network enforces structure while allowing controlled schema migrations. This matters for enterprise-grade applications that require strict data governance, audit trails, and backward compatibility without manual migration scripts.

Threads are encrypted, private databases scoped to a single application or user. Data is not globally discoverable; access is controlled by the app's keys. This matters for sensitive use cases like private messaging, medical records, or internal business logic where data isolation and privacy are non-negotiable requirements.

MongoDB-like query API with indexing on any field. ThreadDB provides find(), sort(), and complex filtering out-of-the-box on decentralized data. This matters for interactive applications like dashboards, marketplaces, or social feeds that require real-time filtering and pagination, reducing complex client-side logic.

No native rich query layer. Ceramic streams are accessed by ID; discovering or filtering streams requires an external indexing service (like Ceramic's ComposeDB or a custom indexer). This adds complexity for use cases needing real-time aggregated views or search across many records.

Threads are siloed by design. Data is not easily composable across different applications because threads are anchored to specific encryption keys. This matters negatively for open data ecosystems where you want other developers to easily read and build upon your protocol's public data state.

Stream-based data model: Each piece of content (e.g., a user profile, a blog post) is a mutable, versioned stream with its own DID. This enables fine-grained access control and composable data portability across apps. This matters for building user-centric applications where data ownership and interoperability are paramount, such as identity protocols (IDX) or social graphs.

Strong protocol-level integrations: Native support for DID methods (3ID, Key DID) and a mature JavaScript/TypeScript client. The ecosystem includes ComposeDB (graph database) and tools like Glaze for rapid development. This matters for teams needing production-ready SDKs and a clear path from prototype to scale, reducing integration overhead.

No built-in query layer: While streams handle state, complex queries (e.g., "fetch all posts by user X") require an external indexer or the use of ComposeDB, adding architectural complexity. This matters for applications requiring real-time, multi-stream aggregations or complex filtering, as you must manage indexing infrastructure yourself.

Anchor-to-Ceramic consensus: Stream updates require anchoring to the Ceramic mainnet for finality, introducing latency (seconds to minutes) and a dependency on the Ceramic network's liveness. This matters for high-frequency update use cases (e.g., live collaborative editing, game state) where sub-second finality is critical.

Familiar NoSQL API: Provides collection- and document-based models with built-in querying, filtering, and indexing. Developers interact with a MongoDB-like interface, drastically reducing the learning curve. This matters for teams migrating Web2 applications or building complex decentralized apps (dApps) that require rich data queries without managing a separate indexer.

Native multi-writer threads: Built on libp2p PubSub and IPFS, ThreadDB is designed for real-time, permissioned collaboration within a shared data context. Threads have configurable access rules. This matters for building collaborative applications like decentralized task boards, group chats, or co-editing platforms where multiple users write to the same dataset.

Smaller active ecosystem: Compared to Ceramic's broad integration (e.g., with IDX, Self.ID, Orbis), ThreadDB has fewer live, large-scale production deployments and a smaller community of contributing developers. This matters for teams prioritizing battle-tested infrastructure and seeking extensive community support and third-party tooling.

Reliance on IPFS pinning: Data persistence depends on IPFS nodes (like Powergate or Textile Buckets) pinning the data, which can introduce cost and availability management concerns. The query performance is also tied to the underlying IPFS network. This matters for applications requiring guaranteed, high-performance data availability without managing a dedicated pinning infrastructure.