Indexing Co vs Bitquery
Bitquery offers GraphQL APIs and cloud streaming for blockchain data. Indexing Co delivers raw contract events directly to your own PostgreSQL or BigQuery with custom TypeScript transforms.
You're building a cross-chain DEX analytics tool. Bitquery's GraphQL API covers the queries you need out of the box: trade data, OHLCV prices, mempool events. You ship v1 fast. Then your data team asks to run cohort analysis directly in BigQuery against a custom schema that joins on-chain events with your internal user table. Bitquery has a cloud delivery option, but the schema is theirs. You can't reshape it, and you can't join across tables that don't exist in your warehouse yet.
That's the moment the distinction between a query API and a data pipeline matters.
Architecture
Bitquery exposes a GraphQL API across 40+ blockchains, with real-time WebSocket subscriptions (V2 API) for live data. Beyond the query layer, they offer cloud streaming integrations that push data to AWS, Snowflake, Google Cloud, Azure, and Kafka. The data model is Bitquery's: you query the shape they've defined, and their cloud delivery exports that same shape to your warehouse.
This is a strong fit if your queries map cleanly to their schema: DEX trades, token transfers, mempool data, price feeds. The WebSocket subscriptions give you real-time access without polling. Cloud delivery integrations reduce the work of getting data into Snowflake or BigQuery without building a pipeline yourself.
The constraint is schema ownership. You're querying their model of the blockchain. If you need a custom event schema, enriched fields, or a shape that maps to your internal data model, you're doing that transformation after the data lands, in your warehouse, with your own tooling.
Indexing Co doesn't expose a query API. It runs a pipeline: extract events from the chain, apply TypeScript transforms you write, and deliver structured rows directly to your PostgreSQL database, BigQuery dataset, or webhook endpoint. The schema is defined by you at pipeline configuration time. The data arrives in your database already shaped the way your application or analytics layer expects it.
There's no intermediate query step. You don't call Indexing Co servers to read your data: the data is in your database, indexed to your specification, ready for your queries.
Feature Comparison
| Feature | Bitquery | Indexing Co |
|---|---|---|
| Data delivery model | GraphQL API + WebSocket subscriptions | Direct to PostgreSQL, BigQuery, or webhook |
| Schema control | Bitquery-defined schema | You define the schema |
| Chain support | 40+ blockchains | 100+ chains |
| Custom contract events | Via GraphQL queries | Full raw event indexing with TypeScript transforms |
| Block-to-database delivery | WebSocket API (their servers) | sub-500ms (dedicated infra) |
| Cloud integrations | AWS, Snowflake, Google Cloud, Azure, Kafka | PostgreSQL, BigQuery, webhooks |
| DEX and price data | Strong, OHLCV, DEX trades, mempool data | Raw event delivery, no built-in price APIs |
| Transform language | Not applicable (you transform post-delivery) | TypeScript (run before data lands) |
| Data volume | Credit-based per plan | 1B+ events/day processed |
| Pricing model | Monthly credit allocations by plan tier | Contact for pipeline pricing |
| Managed infrastructure | Yes | Yes |
| Historical data | Yes | Yes |
When to Use Each
- You need a GraphQL API for on-demand queries across 40+ chains
- Real-time WebSocket subscriptions fit your application's data access pattern
- You want pre-built DEX trade data, OHLCV price feeds, or mempool data without writing indexing logic
- You're delivering to Snowflake, Kafka, or AWS and their schema fits your use case
- You're prototyping and want the shortest path from chain to result
- You need data in your own database on a schema you control
- You're indexing custom smart contract events that don't exist in Bitquery's model
- You need to apply TypeScript transforms before the data lands, enriching, filtering, reshaping at ingest
- You need 100+ chain coverage beyond Bitquery's 40
- Your analytics team runs queries directly against a database or data warehouse and needs schema consistency with internal tables