Comparison
datastore.sh vs building it in-house
Your own pipeline · reviewed September 2026
Short answer
Building in-house means operating archive nodes, writing decoders per program, reconciling against chain state, and re-running backfills whenever logic changes. datastore.sh sells the output of that work as documented Parquet. Build when coverage does not exist commercially, when data cannot leave your perimeter, or when the pipeline is your edge.
What Building it in-house is
An in-house pipeline is archive nodes, capture, per-program decoders, reconciliation, gap repair, storage, and the engineers who keep all of it running. The first version is usually achievable in a quarter, which is why the decision looks simple at the start.
The recurring cost is the real comparison. Decoders drift as programs upgrade, gaps surface in ranges already published, and backfills re-run. That work continues whether or not anyone is currently reading the data, and it competes with the analysis the pipeline was built to support.
How the two products differ
The comparison is between product models rather than feature lists. Prices, rate limits, and chain counts change without notice, so none are quoted here.
| Dimension | datastore.sh | Building it in-house |
|---|---|---|
| What you receive | Partitioned Parquet files with typed schemas, manifests, and checksums | Whatever your pipeline produces, on your own schedule |
| Time to first usable data | Delivery time | A quarter or more, then a backfill |
| Ongoing engineering | None on your side | Permanent, and it grows with coverage |
| Protocol upgrades | Absorbed upstream, published as new versions | Reopened work on every upgrade |
| Gap detection and repair | Reconciled before publication | Yours to detect, diagnose, and refill |
| Cost shape | Known price per dataset and window | Nodes, storage, and salaries, indefinitely |
| Control | Standard schemas, documented per table | Total control over every field and rule |
| Best fit | Coverage that already exists and is documented | Coverage nobody sells, or data that cannot leave |
When Building it in-house is the better choice
- The coverage you need is not available commercially on any terms.
- Regulatory or security constraints prevent the data from being sourced externally.
- The pipeline itself is a competitive advantage rather than a cost center.
When datastore.sh is the better choice
- The datasets you need are already documented in a catalog you can inspect before buying.
- Your engineers are more valuable working on analysis than on decoder upkeep.
- You need results this month rather than after a build and a backfill.
- You want reconciliation and gap repair to be someone else's standing obligation.
Using both together
Many teams do both. They buy history for the protocols that are already covered, and build only where coverage does not exist or cannot be sourced externally. That keeps in-house engineering pointed at the part that is genuinely differentiated.
Frequently asked questions
Is it cheaper to build a blockchain data pipeline in-house?
Rarely, once the second year is counted. Archive nodes, per-program decoders, reconciliation, gap repair, and backfills are ongoing obligations rather than a one-time build, and every protocol upgrade reopens them. Building makes sense when coverage does not exist commercially, when data cannot leave your perimeter, or when the pipeline is your competitive edge.
How long does it take to build Solana instruction decoding?
A first working version for a single program is typically weeks. Coverage across many programs, with inner instructions, account state, reorg handling, and reconciliation against chain state, is a standing engineering commitment rather than a project with an end date, because program upgrades continually change what must be decoded.
What do we give up by buying instead of building?
Field-level control. A purchased dataset uses documented, versioned schemas rather than a shape you designed, so unusual requirements may need a transformation step on your side. In exchange you skip node operations, decoder maintenance, and backfills, and every table is inspectable before purchase.
Start with the data
Skip the indexing project. Run the query.
Browse documented datasets or send the exact protocol, tables, and historical coverage your team needs.