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If you use Moralis Streams, you receive webhooks: you define a stream (addresses, topics, chains), Moralis watches the chain, and decoded events are pushed to your server as they happen. Data Feeds deliver the same decoded onchain data with the direction reversed: instead of receiving pushes and storing what you need, the data flows directly into a database you own: historical backfill and live tail through one pipe. Your webhook handler, retry logic, and ingestion queue disappear; what remains is a table you query.

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How the concepts map

The practical difference: a webhook tells you something happened, and building state from webhooks (balances, holder counts, volumes) is your accounting problem. A Data Feed hands you the rows already in SQL: state is a query, not a pipeline you maintain.

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Why teams move

  • No delivery infrastructure. No public webhook endpoint, no signature checks, no retry handling, no ingestion queue. Data lands in your database directly.
  • History and live in one pipe. A new stream starts at “now”; a feed backfills as deep as you need, then stays live.
  • Consume it your way. The same feed can flow into Postgres today and your own Kafka, AMQP, or SQS consumers as the platform’s export options roll out; see What are Data Feeds? for the consumption model.

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Try the model now

You can evaluate Data Feeds against your current Streams setup today:
  1. Run the quickstart: live decoded data in your own Postgres in about ten minutes.
  2. Find the recipe closest to what your stream watches (token transfers, contract logs, swaps, NFT activity).
  3. Keep your stream running in parallel while you compare; nothing about trying Data Feeds disturbs your existing Streams setup.