Subgraphs vs Substreams: Choosing The Graph’s Data Tools

Blockchain developers working within The Graph ecosystem face a crucial decision when it comes to handling on-chain data: should they opt for Subgraphs or Substreams? Both tools are designed to extract, process, and deliver blockchain data efficiently, but their use cases differ significantly. Understanding these differences is vital for selecting the right solution as projects scale.

Subgraphs: The Backbone for Frontend Applications

Subgraphs, a cornerstone of The Graph since its inception, allow developers to define on-chain event handlers and expose structured data via GraphQL APIs. This makes them an ideal solution for dApps, analytics dashboards, and any application requiring real-time state queries. Subgraphs aggregate historical and current blockchain data into a persistent indexed state, simplifying complex relational queries such as token ownership or liquidity pool compositions.

Their architecture relies on JSON-RPC nodes for data extraction and uses AssemblyScript to filter and transform the data before loading it into a PostgreSQL database. With over 15,000 Subgraphs already published, developers can leverage a rich ecosystem of templates and tooling to accelerate deployment.

For most small-to-medium projects, Subgraphs are the go-to solution, offering a low-cost and user-friendly path to ship blockchain-based applications.

Substreams: Built for High-Throughput and Scalability

Substreams, introduced more recently, represent a shift toward high-performance, parallelized data pipelines. Unlike Subgraphs, which process data sequentially, Substreams enable the extraction and transformation of blockchain data in parallel, dramatically reducing indexing times. For instance, syncing historical data on Substreams can be up to 100 times faster than with Subgraphs, making them particularly suited for high-throughput environments such as Solana, AI workflows, and trading engines.

Written in Rust and compiled to WebAssembly (WASM), Substreams offer flexibility in output destinations, allowing data to be “sinked” into various databases like PostgreSQL or ClickHouse, or even streamed directly into analytics platforms like Kafka. This makes them an excellent choice for developers working on real-time data analytics, machine learning applications, or any use case requiring heavy server-side transformations.

When to Upgrade to Substreams

Subgraphs work well until projects hit certain scaling thresholds. Developers should consider migrating to Substreams when:

• Sync times for Subgraphs become prohibitive, measuring days instead of hours.
• They are indexing high-throughput chains like Solana or Binance Smart Chain.
• Data needs to be integrated into systems beyond a GraphQL API, such as a data warehouse or trading engine.
• Heavy server-side transformations or precise historical backfills are required.

Thanks to tools like substreams-convert, migrating from Subgraphs to Substreams no longer means starting from scratch. This open-source tool automates much of the translation process, allowing developers to carry forward their existing Subgraph investments with minimal overhead.

The Graph’s Role in Blockchain Data Infrastructure

The Graph has positioned itself as a leader in blockchain data services, evidenced by its ability to serve over 1.27 trillion queries across 60+ networks as of early 2026. Its 2026 technical roadmap emphasizes Subgraphs’ compatibility with AI and Substreams’ growth in institutional-grade use cases, reflecting its dual focus on accessibility and scalability.

The native GRT token, priced at $0.0178 as of July 7, 2026, underpins this decentralized data ecosystem, incentivizing independent Indexers who power the network. As demand for efficient blockchain data solutions grows, both Subgraphs and Substreams are positioned to play key roles in enabling the next generation of decentralized applications and analytics.

Choosing the Right Tool

In most cases, the choice between Subgraphs and Substreams will depend on the specific needs of your project:

• Use Subgraphs: For frontend applications, relational data, and smaller projects with real-time state queries.
• Use Substreams: For high-throughput indexing, real-time analytics, AI/ML pipelines, and large-scale data transformations.

Far from being competing technologies, these tools are complementary components of The Graph’s ecosystem. Developers can start with Subgraphs for simplicity and scale into Substreams as their data needs grow, ensuring performance and flexibility at every stage of development.

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