SubQuery vs Bitquery: Custom Indexing vs Pre-built Analytics

Higher initial dev cost, lower marginal cost: Requires engineering resources to build and maintain indexers. However, running your own indexer or using the decentralized network can be more cost-effective at high query volumes (>10M/month) compared to metered API plans.

Lower initial cost, variable operational cost: Pay-as-you-go API pricing (starting ~$49/month) gets you instant access. This can become expensive at scale (>100M queries/month), but eliminates all DevOps and data engineering overhead.

Full control over indexing logic: Write custom mapping functions in TypeScript to transform on-chain data into your application's exact schema. This matters for protocols like Acala or Moonbeam needing bespoke DeFi analytics that pre-built APIs can't provide.

Deploy your own indexer: Run the open-source SubQuery Node on your infrastructure or use the decentralized SubQuery Network. This matters for enterprises with strict data sovereignty requirements or those building a data product that cannot rely on a third-party API's availability.

Engineering resource intensive: Requires developers to write, test, and maintain indexing logic. Initial setup and syncing can take days for complex chains. This is a trade-off for teams without dedicated data engineers who need insights this week, not next quarter.

You manage performance and uptime: While the SubQuery Network offers a decentralized option, self-hosting means you're responsible for node provisioning, scaling, and monitoring. This adds operational overhead compared to a fully managed service like Bitquery.

Pre-built analytics for 40+ blockchains: Query terabytes of normalized data via GraphQL immediately—no indexing setup. This matters for rapid prototyping, due diligence, or dashboards that need data from Ethereum, BSC, and Solana in a unified interface.

Advanced aggregated metrics: Access pre-computed data like DEX trades, token flows, and smart contract statistics without writing aggregation logic. This is critical for hedge funds or analysts who need to query cross-chain money flow or NFT marketplace volumes on-demand.

Constrained by the provided schema: You cannot index custom smart contract events or transform raw data into your proprietary format. This is a dealbreaker for protocols like dYdX or Uniswap that need highly specialized, real-time indexing of their own contracts.

Usage-based pricing model: Costs can scale unpredictably with high query volumes. You are locked into Bitquery's data models and availability. For applications requiring high-throughput, predictable costs, or data portability, this can become a significant long-term constraint.

Massive time-to-market advantage: Access over 40 blockchains with pre-indexed data via GraphQL APIs. This matters for trading desks, auditors, and market analysts who need immediate, cross-chain insights without infrastructure overhead.

Managed, SLA-backed service: Offers 99.9% uptime with dedicated support. This matters for financial institutions and production applications where data consistency and availability are non-negotiable, eliminating DevOps burden.

Fixed data schema: You query what Bitquery has pre-defined. This matters for protocols with novel logic or NFTs with unique attributes where you need to index custom on-chain events, which is not possible without their intervention.

Usage-based pricing model: Costs scale directly with query volume and complexity. This matters for high-throughput dApps or public-facing explorers where unpredictable costs can become prohibitive compared to running your own indexer.

Define your own data schema: Use a custom mapping function written in TypeScript to transform and index any on-chain data. This matters for developers building unique dApps, like a specialized NFT marketplace or a custom governance dashboard, who need tailored data structures.

Open-source and decentralized network: Indexer code is open-source, and you can run your own indexer or use the decentralized SubQuery Network. This matters for projects prioritizing data sovereignty, censorship resistance, and long-term cost predictability.