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Databases · head to head

BigQuery vs Earthly

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
Earthly logo

Earthly

Developer Tools

Engineering guardrails for the AI era

From
Free
Free plan
Yes
Visit EarthlyFull review

The short version

  • Earthly is free; BigQuery starts at $6.25/mo.
  • Only BigQuery lists serverless compute; only Earthly lists policy as code.
  • Where Earthly falls short: pricing model not published; requires sales contact.
  • Prices and features on this page were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Earthly actually diverge.

Attributes where BigQuery and Earthly differ
AttributeBigQueryEarthly
Pricing modelUsage-basedNo pricing published on website
CategoryDatabasesDeveloper Tools
Founded20082020

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

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 Earthly

  • Policy as code
  • Multi-stage enforcement
  • AI agent governance
  • Repository-wide policies
  • Incident response automation
  • Compliance management
  • Earthfiles build tool
  • Satellites

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

Earthly

  • Enforcing engineering standards across AI and human developersnot BigQuery
  • Automating incident response procedures across all repositoriesnot BigQuery
  • Embedding compliance requirements into development workflowsnot BigQuery
  • Standardizing build processes with Earthfilesnot 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

Earthly

  • Pricing model not published; requires sales contact
  • Earthly Lunar is a recent launch (2025) with limited track record
  • Primarily GitHub-focused; limited integration with other platforms
  • No public documentation on pricing or feature tiers

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

Earthly

Free

No published plan breakdown. See the Earthly review.

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 Earthly if

  • You need policy as code.
  • You want to start without paying.
  • You work on Web, API.
  • You also want multi-stage enforcement.
Earthly review

Questions people ask

Is BigQuery or Earthly better?
Neither clearly leads. BigQuery starts at Free and Earthly at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Earthly?
BigQuery starts at Free and Earthly at Free.
Does BigQuery or Earthly run on more platforms?
Both run on Web, API, so platform support will not decide this one for you.
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 Earthly is typically brought in for.
What can BigQuery do that Earthly cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Earthly covers Policy as code, Multi-stage enforcement, AI agent governance, Repository-wide policies.

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.

Earthly: What is the difference between Earthfiles and Earthly Lunar?

Earthfiles is an open-source build tool that reached 12,000 GitHub stars and is used by companies like NVIDIA, Intel, and Databricks. Earthly Lunar is a separate governance platform launched in 2025 for enforcing standards across development workflows.

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.

Earthly: How does Earthly Lunar enforce standards?

Lunar converts engineering standards, compliance requirements, and processes documented in AGENTS.md files, wikis, and incident postmortems into enforceable guardrails that apply consistently across repositories and development stages.

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.

Earthly: What is the pricing for Earthly Lunar?

Pricing is not published on the website. Interested customers should contact Earthly's sales team for current pricing and feature tiers.

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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