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Developer Tools · head to head

Daytona vs Trino

Daytona logo

Daytona

Developer Tools

Infrastructure for spinning up secure, sub-second sandboxes to run AI-generated code

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: Daytona pricing is purely usage-based with no flat monthly plan, which can make costs harder to predict for steady workloads.; 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: Daytona covers Sub-second sandbox creation, Trino covers Federated querying.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Daytona and Trino actually diverge.

Attributes where Daytona and Trino differ
AttributeDaytonaTrino
Pricing modelusage-basedopen-source
Platformsweb, linux, windows, apiWeb
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 Daytona

  • Sub-second sandbox creation
  • Per-second billing
  • GPU instances
  • Windows OS support
  • Enterprise BYOC

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.

Daytona

  • Running AI agent-generated code in a disposable, isolated sandboxnot Trino
  • Executing untrusted user code for a coding platformnot Trino
  • Running GPU workloads on demand without provisioning dedicated serversnot Trino
  • Testing Windows-specific code paths in a sandboxnot Trino

Trino

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

Where each one falls short

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

Daytona

  • Pricing is purely usage-based with no flat monthly plan, which can make costs harder to predict for steady workloads.
  • GPU instances are priced separately and can be significantly more expensive than standard compute for AI-heavy use cases.
  • Enterprise features like SSO, audit logs, and BYOC are only available by contacting sales rather than self-serve.

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

Daytona

Free
  • Pay-as-you-go$undefined/mo
    • $0.0504 per vCPU/hour
    • $0.0162 per GiB memory/hour
    • $0.000108 per GiB storage/hour after 5 free GiB
  • Startup Program$undefined/mo
    • Up to $50,000 in free credits for qualifying startups
  • Enterprise$undefined/mo
    • Custom usage limits
    • SSO
    • Audit logs

Trino

Free

No published plan breakdown. See the Trino review.

Which should you pick?

Choose Daytona if

  • You need sub-second sandbox creation.
  • You want to start without paying.
  • You work on web, linux, windows, api.
  • You also want per-second billing.
Daytona 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 Daytona or Trino better?
Neither clearly leads. Daytona 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, Daytona or Trino?
Daytona starts at Free and Trino at Free.
Does Daytona or Trino run on more platforms?
Daytona runs on web, linux, windows, api. Trino runs on Web.
Can I use Daytona for free?
Both have a free tier, so you can try either at no cost before committing.
What is Daytona best used for?
Daytona is most often used for running ai agent-generated code in a disposable, isolated sandbox, executing untrusted user code for a coding platform, running gpu workloads on demand without provisioning dedicated servers, testing windows-specific code paths in a sandbox. Of those, running ai agent-generated code in a disposable, isolated sandbox and executing untrusted user code for a coding platform are not what Trino is typically brought in for.
What can Daytona do that Trino cannot?
Daytona covers Sub-second sandbox creation, Per-second billing, GPU instances, Windows OS support. Trino covers Federated querying, Connector architecture, Predicate and aggregation pushdown, Massively parallel execution.

Answered from the vendors’ own pages

Daytona: How is Daytona usage metered?

Usage is billed per second across vCPU, memory, and storage consumed, with separate per-hour rates for GPU and Windows instances.

Source
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.

Daytona: Is there a free plan, and what are its limits?

There is no flat free plan, but new signups receive $200 in free compute credit with no credit card required to start.

Source
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.

Daytona: Are discounts available for startups?

Yes, the Startup Program offers up to $50,000 in free credits for qualifying companies.

Source
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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