Developer Tools · head to head
Fig vs Trino
Fig
Developer Tools
IDE-style autocomplete for your existing terminal
- From
- Free
- Free plan
- Yes
Trino
Technology
A distributed SQL engine that queries data where it already lives, across object storage, warehouses and operational databases.
- From
- Free
- Free plan
- Yes
The short version
- Each has a real cost: Fig shut down on 1 September 2024. Amazon acquired Fig in August 2023 and folded it into Amazon Q Developer, which now carries the terminal autocomplete; Trino trino stores nothing and computes no statistics of its own, so the plan it produces is only as good as the partitioning, file sizes and table statistics on the source; a Hive table of thousands of small files or a lake with no stats produces a slow query the engine cannot improve.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Fig and Trino actually diverge.
Identical on both: starting price (Free), free tier (Yes), platforms (Web), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Fig
Nothing recorded that Trino does not also cover.
Only in Trino
- Federated querying
- Connector architecture
- Predicate and aggregation pushdown
- Massively parallel execution
- Fault-tolerant execution
- Resource groups
- Iceberg and Delta table support
- Standard client protocols
What people use each for
The jobs each tool is most often brought in to do.
Fig
No use cases recorded yet. See the Fig review.
Trino
- Ad hoc analysis that spans a data lake and one or more operational databases, without building an ingestion pipeline firstnot Fig
- Serving a BI tool a single SQL endpoint over an estate that is actually several separate storage systemsnot Fig
- Querying Iceberg or Delta tables on object storage interactively, as the compute layer of a lakehousenot Fig
- Investigating whether a dataset is worth ingesting, by querying it in place before committing to a pipeline for itnot Fig
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Fig
- Shut down on 1 September 2024. Amazon acquired Fig in August 2023 and folded it into Amazon Q Developer, which now carries the terminal autocomplete
- The standalone desktop app is no longer available or supported
- The completion specs remain open source on GitHub, but the product that consumed them is gone
Trino
- Trino stores nothing and computes no statistics of its own, so the plan it produces is only as good as the partitioning, file sizes and table statistics on the source; a Hive table of thousands of small files or a lake with no stats produces a slow query the engine cannot improve.
- Federated queries pull data out of the systems they touch, so a join between a lake table and a production Postgres can put a full table scan onto an OLTP database that other applications depend on, and the person who wrote the query will not see the incident it causes.
- Releases come roughly every one to two weeks with no community long-term support line, and deprecations arrive quickly, so you either dedicate someone to keeping current or you buy Starburst Enterprise for a supported long-term version.
- It is memory-based and disk spilling was deprecated in favour of fault-tolerant execution, so a query exceeding cluster memory fails outright rather than degrading; enabling fault-tolerant execution requires an external exchange store on object storage and makes queries measurably slower.
- It is a query engine and not a warehouse: there is no built-in job scheduling, no incremental materialised view maintenance and no transformation framework, so producing curated tables still needs dbt or an equivalent layer that somebody has to own.
- The 2020 fork split the ecosystem, so documentation, connectors, Stack Overflow answers and vendor material written before then describe PrestoDB, which is now a different project with different behaviour, and following the wrong one wastes real time.
Pricing, plan by plan
Fig
FreeNo published plan breakdown. See the Fig review.
Trino
FreeNo published plan breakdown. See the Trino review.
Which should you pick?
Choose Trino if
- You need federated querying.
- You want to start without paying.
- You also want connector architecture.
Questions people ask
- Is Fig or Trino better?
- Neither clearly leads. Fig starts at Free and Trino at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Fig or Trino?
- Fig starts at Free and Trino at Free.
- Does Fig or Trino run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- Can I use Fig for free?
- Both have a free tier, so you can try either at no cost before committing.
- What can Fig do that Trino cannot?
- Trino covers Federated querying, Connector architecture, Predicate and aggregation pushdown, Massively parallel execution.
Answered from the vendors’ own pages
Trino: What is the difference between Trino and Presto?
They share an origin. The original creators left Meta and renamed their fork from PrestoSQL to Trino in December 2020; PrestoDB continues separately under the Linux Foundation. They have diverged in features, connectors and SQL behaviour, so material written for one may not apply to the other.
Trino: Does Trino replace my data warehouse?
Not on its own. It is compute without storage, scheduling or transformation. Paired with Iceberg or Delta on object storage and something like dbt for modelling it can serve as a lakehouse; used alone it is a query layer over what you already have.
Trino: Why is my federated query slow?
Usually because a connector could not push a filter or aggregation down, so Trino is pulling whole tables across the network to join them itself. The fix is usually better source-side partitioning or statistics, or ingesting that source rather than federating it.
Trino: What happens when a query runs out of memory?
It fails. Disk spilling was deprecated in favour of fault-tolerant execution, which checkpoints to an external exchange store such as S3 and lets long queries survive memory pressure and worker loss, at the cost of noticeably slower execution.
Trino: Is there commercial support?
Yes, from Starburst, which offers Starburst Enterprise with long-term supported releases and Starburst Galaxy as a managed service. The open source project itself has no long-term support line.
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