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Databases · head to head

BigQuery vs Harness

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
Harness logo

Harness

Developer Tools

Software delivery platform across CI, CD, security testing and cost management

From
Free
Free plan
Yes
Visit HarnessFull review

The short version

  • Harness is free; BigQuery starts at $6.25/mo.
  • Where Harness falls short: essentials and Enterprise pricing requires sales contact; no published rates.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which BigQuery and Harness actually diverge.

Attributes where BigQuery and Harness differ
AttributeBigQueryHarness
Pricing modelUsage-basedFreemium
PlatformsWeb, APIWeb
CategoryDatabasesDeveloper Tools

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

Not yet recorded: founded for Harness.

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 Harness

Nothing recorded that BigQuery does not also cover.

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

Harness

  • Deploying apps to AWS, Azure, and Google Cloud with rollbacksnot BigQuery
  • Integrating security scanners throughout build pipelines before productionnot BigQuery
  • Automating cloud cost reduction and tracking unit-level expensesnot BigQuery
  • Enabling developers to provision infrastructure without operations bottlenecksnot BigQuery
  • Assessing deployment frequency and lead time productivity metricsnot 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

Harness

  • Essentials and Enterprise pricing requires sales contact; no published rates
  • DevOps Essentials has no on-premises deployment option
  • Internal Developer Portal requires minimum 20 developer licenses
  • Pipeline execution history retention 6 months (Essentials) vs 25 months as add-on (Enterprise)

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

Harness

Free
  • FreeFree
    • Starter platform for software delivery lifecycle
    • Open source options available (Harness Open Source, Litmus Chaos)
  • EssentialsOn request
    • Growing organizations with small to mid-size teams
    • Packaged DevOps Essentials bundle
    • Continuous software delivery capabilities
  • EnterpriseOn request
    • Large-scale organizations
    • Pick any modules based on needs
    • Advanced features

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 Harness if

  • You want to start without paying.
Harness review

Questions people ask

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

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.

Harness: Is there a free Harness plan?

Yes, Harness offers a free plan for individual developers and small teams with open source options including Harness Open Source and Litmus Chaos.

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.

Harness: What are Harness's paid pricing tiers?

Harness offers Essentials for growing teams and Enterprise for large-scale organizations. Both require contacting sales for custom pricing. Pricing is based on modules and features selected.

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.

Harness: Can Harness be deployed on-premises?

Enterprise plans include self-managed platform deployment option. DevOps Essentials bundle does not offer on-premises deployment.

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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