Goldsky vs Allium

Allium covers 150+ chains and Goldsky covers 150+ chains. Both support EVM and non-EVM chains, and both offer streaming products that deliver data in low single-digit seconds.

The platforms differ in how they deliver data, who they are built for, and what you do with the data.

Allium and Goldsky approach blockchain data from different starting points.

This is the most important distinction between these platforms.

Allium is built for teams that consume data in SQL. That includes analysts who run cross-chain queries. It also includes researchers who query Allium’s pre-decoded warehouse tables to build their own GAAP or compliance reports. The usual workflow is to open a SQL editor (or connect your warehouse), query decoded tables, and build dashboards. The data is already modeled, but you still write the SQL for each report. For wallet balances and transfers, Goldsky Feeds is a prebuilt REST API.

We build for teams that build applications on data. That includes engineers who use subgraphs to power a dApp frontend, and data engineers who stream data into a backend database. It also includes DevOps teams who manage RPC endpoints across regions. The usual workflow is to define a config or subgraph and deploy it with the CLI. Then you stream data to your own Postgres or ClickHouse and build on top of it.

If your team’s primary question is “what happened on chain X last quarter?” Allium is a natural fit. If your question is “how do I get this onchain data into my application’s database in under a second?” Goldsky is a natural fit.

Allium ships thousands of pre-decoded schemas, including token transfers, DEX trades, wallet balances, NFT activity, prices, and PnL. You query them with SQL right away, with no indexer to write and no pipeline to configure. For teams that want answers from blockchain data without building data infrastructure, this removes weeks or months of work.

We don’t ship pre-modeled analytics tables. We give you raw and enriched datasets (blocks, logs, transactions, traces, token data). You transform them with subgraph mappings, SQL, TypeScript, or HTTP handlers in your pipelines. You control the schema and the data model, and you build and maintain those models yourself.

For teams that need bespoke schemas tightly coupled to application logic, our approach is more flexible. For teams that want standardized analytics on decoded protocol data, Allium’s platform fits better. For that use case, their approach saves engineering time.

Allium delivers data through several channels:

• Its Developer (Realtime) APIs serve REST endpoints with p50 data freshness of 3-5 seconds and up to 1K+ requests per second during peak load.^[5]
• Datastreams delivers enriched events via Kafka, Google Pub/Sub, and Amazon SNS with P50 latency of about 5 seconds. Beam, a filtering layer on top of Datastreams, brings that to under 2 seconds.^[4]
• Datashares deliver directly into Snowflake, BigQuery, and Databricks for warehouse-native workflows.^[3]

Turbo is Goldsky’s current streaming engine, built in Rust:

• Turbo delivers data into 11 managed sinks: Postgres, ClickHouse, MySQL, Kafka, S3-compatible object storage, SQS, Amazon EventBridge, Google Cloud Pub/Sub, S2, and webhooks.^[26]
• Turbo also reaches Snowflake through the Snowflake Kafka connector, BigQuery through Pub/Sub, and Databricks through Auto Loader on S3.
• Turbo adds SIMD-vectorized processing, microsecond-scale batch handling, TypeScript transforms, and dynamic lookup tables.

Both platforms offer low-latency streaming. The difference is destination: Allium routes to its own APIs and customer warehouses. Goldsky routes to infrastructure you manage.

Each company has products in areas that the other doesn’t cover. These are not direct comparison points, but they may matter if you’re evaluating both platforms.

Allium advertises 150+ chain support.^[1] Goldsky supports 150+ chains across 270+ networks.^[19] Both cover major EVM networks (Ethereum, Base, Arbitrum, Optimism, Polygon) and non-EVM ecosystems. Many chain ecosystems list Goldsky as their recommended or default indexer. NEAR’s Infrastructure Committee funded Goldsky, The Graph, and Allium to strengthen indexing services and historical data access.^[27]

Allium positions itself as a data provider for enterprises that build on these chains. It also publishes select datasets through the Walrus decentralized storage protocol.^[8]

Goldsky publishes its pricing tables and calculators. Allium’s pricing requires a conversation with its sales team.

Allium is SQL and REST API-oriented. The Explorer provides a browser-based SQL editor where analysts can query, chart, and share insights without local tooling. To start, you sign up, generate an API key, and call endpoints. An MCP server lets AI agents run SQL queries and introspect schemas.^[10]

We’re CLI-centric. Install the CLI with a script or npm and log in with an API key. Then deploy and manage infrastructure with the goldsky subgraph, goldsky pipeline, or goldsky turbo commands. The dashboard includes Goldsky Flow, where you build pipelines with drag and drop. Turbo adds live data inspection and hot-reloadable pipelines. An MCP server and Agent Skills help AI coding assistants manage pipelines.

Goldsky runs subgraph indexing and query layers at 99.9%+ uptime.^[24] Data quality checks cross-validate responses across providers and automatically propagate deletes and updates to downstream sinks on reorgs.^[25]

Goldsky owns the stack end to end, from RPC node to indexing, so we build redundancy into every step:

• We maintain eRPC, the most trusted open-source RPC redundancy layer, collaborating with teams like Circle, Polygon, and Polymarket.^[15]
• We also built our own systems for redundancy and low latency. Prism, our geo-replicated data lake, stores each chain once and serves every product.^[16]
• Streamling, our stream processor, restarts a failed pipeline on a new node almost immediately.^[17]
• Turbo also lets you host the whole data stack on-prem, from indexing pipelines to warehouses, at prices comparable to hosted SQL services. That gives you full control over availability.^[18]

A KTH Royal Institute of Technology study found Allium’s data deviates 0.000011% from ground truth node data.^[9]

Yes. Some teams use both.

A common split is Goldsky for application-specific indexing and real-time streaming, such as a dApp backend, an operational database, or custom pipelines. Allium then covers ad hoc analytics, reporting, and cross-chain SQL research, such as historical queries or a data warehouse feed.

Teams can also run both for redundancy. Incidents still happen. In October 2026, Allium’s status page reported an outage lasting more than a day, followed by delayed data delivery.^[14] Goldsky can deliver the same underlying onchain data across 150+ chains.^[19] Turbo streams it into your own database, or into your warehouse through S3, Kafka, or Pub/Sub, which keeps you provider-agnostic.^[26]

The platforms don’t compete for the same slot in most architectures. Goldsky sits closer to the application layer. Allium sits closer to the analytics and business intelligence layer.