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Databases · head to head

BigQuery vs Depot

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
Depot logo

Depot

Developer Tools

Cloud-based builds and CI runs 55x faster with less cost

From
Free
Free plan
Yes
Visit DepotFull review

The short version

  • Depot is a cloud-based CI/CD platform.
  • Free plans on both; paid from $6.25/mo and $20/mo.
  • Only BigQuery lists serverless compute; only Depot lists Depot CI.
  • Where Depot falls short: developer plan minute limits may be insufficient for active development.
  • Prices and features on this page were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Depot actually diverge.

Attributes where BigQuery and Depot differ
AttributeBigQueryDepot
PlatformsWeb, APIWeb, GitHub, API
CategoryDatabasesDeveloper Tools
Founded20082022

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

Not yet recorded: pricing model for Depot.

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 Depot

  • Depot CI
  • Container builds
  • GitHub Actions runners
  • Distributed caching
  • Build API
  • Depot Registry
  • Cross-platform builds
  • SSH debugging

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 Depot
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Depot
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Depot
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Depot

Depot

  • Accelerating Docker builds for container-based developmentnot BigQuery
  • Replacing GitHub Actions runners with faster infrastructurenot BigQuery
  • Distributing cache across Monorepo buildsnot BigQuery
  • Debugging failed CI builds interactively with SSHnot 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

Depot

  • Developer plan minute limits may be insufficient for active development
  • Startup plan at $200/mo is expensive for smaller teams
  • Business plan requires custom pricing consultation
  • macOS M2 runners not available on Developer plan

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

Depot

Free
  • Developer$20/mo
    • 1 user
    • 500 Docker build minutes per month
    • 2,000 Depot CI minutes per month
  • Startup$200/mo
    • Unlimited users
    • 5,000 Docker build minutes per month
    • 20,000 Depot CI minutes per month
  • BusinessOn request
    • Custom pricing
    • Unlimited users
    • Custom monthly compute

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 Depot if

  • You need depot ci.
  • You want to start without paying.
  • You work on Web, GitHub, API.
  • You also want container builds.
Depot review

Questions people ask

Is BigQuery or Depot better?
Neither clearly leads. BigQuery starts at Free and Depot at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Depot?
BigQuery starts at Free and Depot at Free.
Does BigQuery or Depot run on more platforms?
BigQuery runs on Web, API. Depot runs on Web, GitHub, 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 Depot is typically brought in for.
What can BigQuery do that Depot cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Depot covers Depot CI, Container builds, GitHub Actions runners, Distributed caching.

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.

Depot: How much faster are Depot runners compared to GitHub Actions?

Depot runners offer 30% faster CPUs and 10x faster networking than standard GitHub-hosted runners, enabling builds to complete significantly faster.

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.

Depot: What is included in the Developer plan?

Developer plan ($20/month) includes 500 Docker build minutes, 2,000 Depot CI minutes, 2,000 GitHub Actions minutes, 25 GB storage, unlimited concurrency, and community support.

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.

Depot: Can I use Depot with my own AWS account?

Yes, Depot Managed allows deployment within customer AWS accounts. This is available to interested customers; contact sales for details.

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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