Ceramic ComposeDB vs Federated SQL Databases

User-owned data streams: Data is stored in IPFS and anchored to a blockchain (e.g., Ethereum, Polygon). Users control their data via DIDs (Decentralized Identifiers). This matters for building applications where portability, censorship-resistance, and user sovereignty are non-negotiable, like creator platforms or decentralized social graphs (e.g., Lens Protocol).

Composable data models: Built on GraphQL with a graph-based data model. Protocols can define and reuse interoperable data schemas, enabling cross-application data composability. This matters for ecosystems where multiple dApps need to read/write to a shared, verifiable data layer, such as on-chain reputation systems or DAO tooling.

Sub-millisecond reads & complex joins: Leverages decades of optimization in engines like PostgreSQL or MySQL. Supports ACID transactions and complex analytical queries across federated instances. This matters for high-throughput financial applications, real-time dashboards, or enterprise reporting where predictable, low-latency query performance is critical.

Established DevOps pipeline: Integrates seamlessly with Kubernetes, Terraform, Datadog, and any SQL-based BI tool. Offers robust backup, replication, and point-in-time recovery. This matters for engineering teams with $500K+ budgets who need to minimize operational risk, ensure compliance (SOC2, GDPR), and leverage existing talent pools.

GraphQL-native with composable models: Data is defined using GraphQL schemas that are publicly discoverable on the Ceramic network. This creates a shared data layer where any app can read and write to the same data models (e.g., a 'Profile' or 'Post' model). This matters for building open data ecosystems like decentralized social (Farcaster) or credentialing (Disco) where interoperability is non-negotiable.

Predictable, high-throughput OLTP: Systems like PostgreSQL or CockroachDB offer sub-millisecond reads, ACID transactions, and proven scalability to 100k+ QPS with proper sharding. This matters for high-frequency financial applications, real-time analytics dashboards, or any use case requiring strict consistency and complex joins.

Consensus overhead: Writes require network consensus (~2-5 seconds) and incur gas fees (in testnet tokens). Reads are fast from a local cache, but syncing a new node has overhead. This matters for latency-sensitive applications (sub-second UI updates) or high-volume write scenarios where per-operation cost is prohibitive.

Single point of failure & control: The database operator (your team or cloud provider) controls access, can censor data, and becomes a scalability/availability bottleneck. Data is siloed within your application. This matters negatively for censorship-resistant applications or projects aiming for permissionless innovation on a shared data layer.

Built-in data integrity: All updates are signed and stored on a cryptographically verifiable stream. This creates a composable data graph where any app can read and write to the same user-centric data with permission. This matters for ecosystem interoperability, like a user's social graph being reusable across multiple applications.

Predictable, high-throughput queries: Leverages decades of optimization in engines like PostgreSQL and MySQL. Supports complex JOINs, ACID transactions, and sub-second analytical queries at scale. This matters for high-volume financial applications, real-time dashboards, and complex reporting where latency and complex relational logic are non-negotiable.

Limited relational joins: As a graph database optimized for decentralized writes, complex multi-stream aggregations and ad-hoc analytical queries are not its primary strength. You must design your ComposeDB model carefully and may need indexing services. Choose this if your app logic is event-driven and centered on individual entity streams, not large-scale relational analysis.

Operational burden and scaling limits: Requires managing database clusters, sharding, and replication. The federation layer becomes a single point of coordination and potential failure. Vertical scaling has a ceiling. This matters for globally distributed applications where you want to avoid operational overhead and regional latency issues inherent in a centralized architecture.