Goldsky vs Ormi

Both platforms run 100% Graph-spec-compatible subgraphs, so your subgraph.yaml, schema, and AssemblyScript mappings work on either with no rewrite. This is the cleanest part of the comparison. The platform differences show up in what happens around your subgraph.

Ormi 0xGraph serves your subgraph through a managed GraphQL endpoint backed by a hybrid architecture that pairs cloud elasticity with a bare-metal network for redundancy. If the cloud layer degrades, Ormi says its bare-metal tier takes over.^[5] It supports grafting, no-code template subgraphs, and webhooks,^[6] and it advertises staying at the tip of the chain with automatic reorg handling.^[5]

Goldsky Subgraphs is also fully managed and spec-compatible. It adds:

• Instant Subgraphs deployed straight from a contract address with no mapping code.^[19]
• Tags for versioning and zero-downtime deploys.
• Webhooks for real-time notifications.

It also reports detailed metrics, including handler timing and eth_call counts,^[18] and runs multiple load-balanced node pools.^[17] The structural difference comes after the GraphQL endpoint. Goldsky can also stream the same indexed data into your own database via Turbo, which Ormi’s subgraph product does not do.

Ormi is the official successor Chainstack chose for its sunset subgraph service. Chainstack ended its own Subgraphs product (final date February 1, 2026)^[9] and moved existing subgraphs to Ormi, where they’re branded as 0xGraph.

Goldsky is the official migration partner for Alchemy’s sunset subgraph service. Alchemy Subgraphs was sunset on December 8, 2025.^[11] Goldsky is Alchemy’s official migration partner.^[12]

This is good news if you’re a Chainstack subgraph user. The migration was handled for you. Your subgraphs were synced and queryable on Ormi from day one, and Chainstack customers received a month of free Ormi access.^[10] If that’s your situation, Ormi is the path of least resistance.

Chainstack migrated its own production subgraphs to Ormi and is leaving the subgraph business. Ormi’s performance figures come from Ormi’s own site and we found no independent benchmark.^[5]

Ormi lists Dolomite, Pendle, Usual, and Ostium as customers.^[1]

Ormi optimizes the read path: how fast a GraphQL query returns from a synced subgraph. Goldsky optimizes the whole pipeline: from chain ingestion through transformation to delivery into your database. These are different numbers, and which one matters depends on your architecture.

Ormi reports sub-second (under 30ms) query latency at 1,000+ RPS, up to 4,000+ RPS, on a hybrid bare-metal/cloud stack. It reports zero throttling and full chain sync from day one.^[5] If your bottleneck is serving a high volume of GraphQL reads to a dashboard or trading frontend, that focus is exactly what you want.

Goldsky’s Turbo engine is written in Rust with real-time vectorized execution and inter-batch parallelism.^[14] Goldsky’s published figures measure decoding and filtering throughput. A single-worker Turbo pipeline executes complex decoding and filtering on 10,000 logs in milliseconds. It also processes 1,000 Ethereum logs in roughly 3-4ms on average.^[14] These figures measure something different from Ormi’s query latency and are not an independent head-to-head benchmark. Goldsky also propagates reorg deletes and updates all the way through to downstream sinks.^[29] It also enforces block-range integrity checks to prevent stale or partial data.^[30]

This is Goldsky’s clearest structural differentiator. Ormi gives you a GraphQL endpoint to your subgraph. Goldsky can also push that data into systems you own. If your design ends at querying a GraphQL API, Ormi covers it. If your design needs the data inside your own Postgres, ClickHouse, Kafka, or object storage, that’s where the platforms diverge.

Goldsky Turbo streams indexed and decoded data into 11 managed sinks. These include Postgres, ClickHouse, MySQL, Kafka, S3-compatible object storage, SQS, Amazon EventBridge, Google Cloud Pub/Sub, S2, and webhooks.^[15] (Goldsky’s earlier Mirror product is now legacy, superseded by Turbo.^[16]) Pipelines support SQL and TypeScript transforms and dynamic lookup tables. You can decode, filter, join onchain with offchain data, and route across chains before the data lands. One caveat: Turbo’s managed sinks don’t include a native Snowflake, BigQuery, or Databricks destination.^[15] Reach Snowflake through the Snowflake Kafka connector and BigQuery through a Pub/Sub subscription. Reach Databricks through Auto Loader on the S3 sink.

Ormi’s subgraph product is delivery-via-GraphQL: you query the endpoint, with webhook support for notifications and direct database replicas available to Enterprise users.^[6][8] To get Ormi subgraph data into a warehouse or message queue, you’d build that pipeline yourself.^[6]

Goldsky covers more chains and goes deeper on non-EVM. Ormi’s 0xGraph is broad on EVM and Cosmos but doesn’t center on full non-EVM ecosystems. If your roadmap is EVM-and-Cosmos, both fit. If it touches Solana, Bitcoin, NEAR, or Arweave, the gap matters.

Goldsky supports 150+ chains, including first-class non-EVM: Solana with full historical data from genesis, plus Bitcoin, NEAR, Arweave, Stellar, and Sui, alongside major EVM networks.^[13] Goldsky is also embedded as a default or recommended indexer in several chain ecosystems.

Ormi’s 0xGraph advertises 45+ networks spanning EVM-compatible chains, rollups, Cosmos SDK chains, and emerging high-throughput networks.^[3] Non-EVM depth (full Solana genesis history, Bitcoin, NEAR, Arweave) is a Goldsky strength, not an Ormi one.

Goldsky extends past subgraphs into areas Ormi’s subgraph product doesn’t cover. These aren’t head-to-head comparison points. They’re scope differences that matter if you want one platform instead of several.

• Turbo: real-time streaming into 11 managed destinations you own, with managed delivery, retries, reorg propagation, and scaling.
• Edge RPC: multi-region, low-latency JSON-RPC read access at a flat $5 per million requests, all methods priced equally.^[21]
• Boost: automatically optimizes your existing RPC endpoints without changing providers, which lowers your billable RPC calls.^[22]
• Compose: durable onchain execution. Write TypeScript that moves data and events between offchain and onchain systems, running in an isolated sandbox with full execution tracing.

For wallet balances and transfer history, Goldsky Feeds is a prebuilt REST API.^[23] For other data, Goldsky’s route is custom indexing with Subgraphs or Turbo.^[17][14]