Softwr

Developer Tools · head to head

Dagger vs Trino

Dagger logo

Dagger

Developer Tools

Programmable CI/CD engine that runs your pipeline as containers on any runner

From
Free
Free plan
Yes
Full reviewWebsite
Trino logo

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
Full reviewWebsite

The short version

  • Each has a real cost: Dagger dagger is not a CI provider, so you keep paying for GitHub Actions or GitLab runners underneath it; the Dagger Cloud subscription is an addition to your CI bill, not a replacement for it.; 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.
  • They diverge on capability: Dagger covers Pipelines as code, Trino covers Federated querying.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Dagger and Trino actually diverge.

Attributes where Dagger and Trino differ
AttributeDaggerTrino
Pricing modelPer month per team for Dagger Cloudopen-source
PlatformsLinux, macOS, Windows, Self-hostedWeb
CategoryDeveloper ToolsTechnology

Identical on both: starting price (Free), free tier (Yes), 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 Dagger

  • Pipelines as code
  • Local and CI parity
  • Content-addressed caching
  • Dagger Functions and modules
  • CI-provider agnostic
  • Dagger Cloud traces
  • GitHub Checks integration
  • Container-native execution

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.

Dagger

  • A platform team maintaining near-identical build YAML across forty repositories that wants one versioned module every repository calls insteadnot Trino
  • An engineering organisation migrating off a CI provider that does not want to rewrite every pipeline as part of the migrationnot Trino
  • A team whose engineers waste hours pushing commits to debug CI-only failures and needs to reproduce the exact run locallynot Trino
  • A monorepo where most commits touch a small subset of services and cache-accurate step skipping cuts build minutes materiallynot Trino

Trino

  • Ad hoc analysis that spans a data lake and one or more operational databases, without building an ingestion pipeline firstnot Dagger
  • Serving a BI tool a single SQL endpoint over an estate that is actually several separate storage systemsnot Dagger
  • Querying Iceberg or Delta tables on object storage interactively, as the compute layer of a lakehousenot Dagger
  • Investigating whether a dataset is worth ingesting, by querying it in place before committing to a pipeline for itnot Dagger

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Dagger

  • Dagger is not a CI provider, so you keep paying for GitHub Actions or GitLab runners underneath it; the Dagger Cloud subscription is an addition to your CI bill, not a replacement for it.
  • Writing pipelines in Go or TypeScript raises the barrier to entry: a release engineer who could edit a YAML step now needs to read code, and the people who can fix a broken build shrink to those comfortable with the SDK.
  • The engine runs as a container on the runner and needs a persistent cache volume to deliver its main benefit, so ephemeral CI runners give you correctness without the speed, and provisioning durable cache storage is extra platform work.
  • The Team plan caps at ten users and ten million events per month; an organisation of any size crosses that quickly and moves to Enterprise pricing that is not published, so cost at scale is unknowable up front.
  • The module ecosystem is young and thinly maintained relative to the marketplace of a mature CI provider, so integrations that exist as a one-line Action often have to be written yourself as a Dagger function.

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

Dagger

Free
  • IndividualFree
    • One user
    • One million events per month
    • Workflow logs and function call traces
  • Team$50/month
    • Up to ten users
    • Ten million events per month
    • Module insights and module catalogue
  • Enterprise$undefined/year
    • Custom user and event limits
    • SSO
    • Managed single-tenant deployment

Trino

Free

No published plan breakdown. See the Trino review.

Which should you pick?

Choose Dagger if

  • You need pipelines as code.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Self-hosted.
  • You also want local and ci parity.
Dagger review

Choose Trino if

  • You need federated querying.
  • You want to start without paying.
  • You also want connector architecture.
Trino review

Questions people ask

Is Dagger or Trino better?
Neither clearly leads. Dagger 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, Dagger or Trino?
Dagger starts at Free and Trino at Free.
Does Dagger or Trino run on more platforms?
Dagger runs on Linux, macOS, Windows, Self-hosted. Trino runs on Web.
Can I use Dagger for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dagger best used for?
Dagger is most often used for a platform team maintaining near-identical build yaml across forty repositories that wants one versioned module every repository calls instead, an engineering organisation migrating off a ci provider that does not want to rewrite every pipeline as part of the migration, a team whose engineers waste hours pushing commits to debug ci-only failures and needs to reproduce the exact run locally, a monorepo where most commits touch a small subset of services and cache-accurate step skipping cuts build minutes materially. Of those, a platform team maintaining near-identical build yaml across forty repositories that wants one versioned module every repository calls instead and an engineering organisation migrating off a ci provider that does not want to rewrite every pipeline as part of the migration are not what Trino is typically brought in for.
What can Dagger do that Trino cannot?
Dagger covers Pipelines as code, Local and CI parity, Content-addressed caching, Dagger Functions and modules. Trino covers Federated querying, Connector architecture, Predicate and aggregation pushdown, Massively parallel execution.

Answered from the vendors’ own pages

Dagger: Does Dagger replace GitHub Actions?

No. Dagger runs inside GitHub Actions or any other CI. It replaces the YAML that describes your pipeline steps, not the trigger and runner layer.

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.

Dagger: Is the engine open source?

Yes, the Dagger engine and SDKs are open source and free. Dagger Cloud, the observability product, is what costs money.

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.

Dagger: What is an event in the pricing?

Dagger Cloud meters pipeline telemetry events. One million per month is free; the Team plan at 50 USD per month allows ten million.

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.

Dagger: Can I run the same pipeline on my laptop?

Yes, that is the main reason teams adopt it. The dagger CLI executes the identical graph locally, so CI-only failures become reproducible.

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.

Share

Related pages

More on Dagger

More on Trino

Keep looking

Other head to heads