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Databases · head to head

BigQuery vs Daytona

BigQuery logo

BigQuery

Databases

Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.

From
Free
Free plan
Yes
Visit BigQueryFull review
Daytona logo

Daytona

Developer Tools

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

From
Free
Free plan
Yes
Visit DaytonaFull review

The short version

  • Free plans on both; paid from $6.25/mo and $0.0504/mo.
  • Only BigQuery lists serverless compute; only Daytona lists sub-second sandbox creation.
  • Where Daytona falls short: pricing is purely usage-based with no flat monthly plan, which can make costs harder to predict for steady workloads.
  • Prices and features on this page were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Daytona actually diverge.

Attributes where BigQuery and Daytona differ
AttributeBigQueryDaytona
PlatformsWeb, APIWeb, Linux, Windows, API
CategoryDatabasesDeveloper Tools

Identical on both: starting price (Free), pricing model (Usage-based), free tier (Yes).

Not yet recorded: founded for Daytona.

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 BigQuery

  • Serverless compute
  • Separation of storage and compute
  • Two pricing models
  • Partitioning and clustering
  • Materialised views
  • BigQuery ML
  • Storage Write API
  • BI Engine

Only in Daytona

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

What people use each for

The jobs each tool is most often brought in to do.

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Daytona
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Daytona
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Daytona
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Daytona

Daytona

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

Where each one falls short

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

BigQuery

  • On-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time
  • There is no way to join tables that live in different regions, so a data estate split across regions for residency reasons has to be reconciled with copies and the storage and transfer that implies
  • It is not built for point lookups; retrieving a single row has latency measured in hundreds of milliseconds or more, so BigQuery cannot serve an application's read path and always needs a second store in front of it
  • Frequent small mutations run into DML concurrency limits and the cost of rewriting storage blocks, so a workload that updates individual rows continuously behaves badly compared with an append-only design
  • The compute exists only inside Google Cloud, so while tables can be exported, the accumulated GoogleSQL, scheduled queries, authorised views, ML models and IAM structure do not move, and switching warehouses is a rewrite of the analytical layer

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

Pricing, plan by plan

BigQuery

Free
  • Free TierFree
    • 1TB queries/month
    • 10GB storage/month
    • Standard support
  • On-demand$6.25/tb
    • Pay per query
    • Pay per storage
    • All features

Daytona

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

Which should you pick?

Choose BigQuery if

  • You need serverless compute.
  • You want to start without paying.
  • You work on Web, API.
  • You also want separation of storage and compute.
BigQuery review

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

Questions people ask

Is BigQuery or Daytona better?
Neither clearly leads. BigQuery starts at Free and Daytona at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Daytona?
BigQuery starts at Free and Daytona at Free.
Does BigQuery or Daytona run on more platforms?
BigQuery runs on Web, API. Daytona runs on Web, Linux, Windows, API.
Can I use BigQuery for free?
Both have a free tier, so you can try either at no cost before committing.
What is BigQuery best used for?
BigQuery is most often used for a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place, bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running, event and clickstream analytics ingested continuously through the storage write api and queried without a load window, analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portability. Of those, a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place and bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running are not what Daytona is typically brought in for.
What can BigQuery do that Daytona cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Daytona covers Sub-second sandbox creation, Per-second billing, GPU instances, Windows OS support.

Answered from the vendors’ own pages

BigQuery: How is BigQuery actually billed?

Storage is billed separately from compute. Compute is either on-demand, priced by the bytes a query reads from the referenced columns, or capacity-based, where you reserve autoscaling slots. Most cost surprises come from on-demand queries that scan more than expected.

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
BigQuery: How do I control query cost?

Partition and cluster tables so queries prune data, select only the columns needed, use materialised views for repeated aggregations, and set maximum bytes billed on queries so a runaway scan fails instead of billing.

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
BigQuery: Can I use it without being on Google Cloud?

The service only runs on Google Cloud. BigQuery Omni can query data held in S3 or Azure storage, but the compute is still Google's and the account relationship is still with Google.

Daytona: Are discounts available for startups?

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

Source
BigQuery: Is it suitable for serving application queries?

No. Latency for single-row reads is far too high. BigQuery is an analytical warehouse and application read paths need a transactional database or a cache in front of it.

BigQuery: When should I move from on-demand to capacity pricing?

When on-demand spend becomes both large and predictable, or when unpredictable spend is a bigger problem than query queueing. The switch trades a variable bill for a fixed one plus contention between workloads.

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