Subsquid vs The Graph: Indexing Stack Flexibility

Full-stack control: Write custom ETL pipelines in TypeScript, directly query raw chain data, and deploy as a single Docker container. This matters for bespoke analytics or protocols with complex on-chain logic (e.g., Aave, Lido) where you need to shape the data model precisely.

Self-hosted efficiency: Run your indexer on your own infrastructure, avoiding recurring query fees. With ~10x faster sync times for historical data via the Subsquid Network's decentralized data lakes, this matters for capital-efficient startups or applications requiring deep historical analysis without vendor lock-in.

Managed reliability: Leverage The Graph's ~800+ Indexers and 28+ supported chains for a fully decentralized, fault-tolerant query layer. This matters for production dApps (like Uniswap or Livepeer) that require guaranteed uptime and censorship resistance without managing infrastructure.

Standardized subgraphs: Access a public registry of 40,000+ deployed subgraphs, enabling instant composability with other protocols. This matters for rapid prototyping or applications that aggregate data from multiple sources (e.g., DeFi dashboards, NFT explorers) using a well-known query standard (GraphQL).

Self-hosted, customizable pipeline: You control the entire indexing stack—from the processor to the GraphQL API. This enables custom ETL logic, direct database access for complex analytics, and integration with any backend service (e.g., Hasura, Metabase). This matters for protocols requiring bespoke data transformations or real-time analytics dashboards.

Optimized for batch processing: The Squid SDK's architecture is designed for fast historical syncs, often 10-100x faster than event-by-event processing for initial indexing. Combined with self-hosting, this leads to predictable, infrastructure-only costs versus query-based pricing. This matters for applications needing to index large historical datasets quickly or with high-volume, low-margin queries.

Managed, decentralized service: Indexers, Curators, and Delegators operate The Graph Network, providing censorship resistance and uptime SLAs without you managing servers. Subgraphs are deployed to a global network, not a single database. This matters for dApps requiring production-grade reliability, hands-off maintenance, and alignment with web3 decentralization principles.

Standardized subgraph model: With 1,000+ deployed subgraphs and deep integration across major chains (Ethereum, Arbitrum, Polygon), the tooling and community support are vast. The hosted service offers a free tier, and the GraphQL endpoint is instantly consumable. This matters for teams launching quickly, building consumer dApps, or relying on established community subgraphs.

You are the DevOps team: Requires provisioning and maintaining your own infrastructure (Postgres, Redis, API servers), monitoring, and handling scaling. This adds significant operational complexity and fixed costs compared to a pay-as-you-go query model. This is a trade-off for teams without dedicated SRE/DevOps resources.

Costs scale with usage: On the decentralized network, you pay per query via GRT, which can become expensive for high-throughput applications. The standardized subgraph model can be limiting for complex aggregations or data joins that fall outside its schema. This matters for applications with unpredictable query volumes or needing highly specialized data pipelines.

Specific advantage: Over 40,000 subgraphs deployed and $2B+ in secured value. This matters for protocols requiring battle-tested infrastructure and immediate access to a large pool of indexers (e.g., Uniswap, Aave). The established network effects reduce operational risk.

Specific advantage: GRT token-based curation via bonding curves signals high-quality data. This matters for dApps that prioritize censorship resistance and data verifiability over pure speed. The economic model aligns indexer incentives with data demand.

Specific disadvantage: Subgraph mappings are written in AssemblyScript/TypeScript, which can be restrictive for complex data transformations. This matters for teams needing low-level control, multi-chain logic, or custom ETL pipelines that Subsquid's flexible processor model provides.

Specific disadvantage: Requires managing GRT for query fees and navigating the decentralized network's pricing. This matters for engineering teams with tight budgets or who want predictable, fixed-cost infrastructure without token economics complexity.

Specific advantage: Write data handlers in TypeScript with full access to any external API or library. This matters for complex indexing logic, integrating off-chain data, or migrating from a centralized database. The stack is self-hosted, offering complete control.

Specific advantage: No protocol fees; costs are your infrastructure spend (e.g., AWS). Batch processing enables high throughput (1000+ TPS indexing). This matters for high-volume applications like NFT marketplaces or gaming protocols where query cost predictability is critical.