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Databases · head to head

BigQuery vs Ona (formerly Gitpod)

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
Ona (formerly Gitpod) logo

Ona (formerly Gitpod)

Developer Tools

Cloud development environments repositioned as a control plane for software engineering agents

From
Free
Free plan
Yes
Visit Ona (formerly Gitpod)Full review

The short version

  • Free plans on both; paid from $6.25/mo and $20/mo.
  • Only BigQuery lists two pricing models; only Ona (formerly Gitpod) lists ephemeral environments.
  • Prices and features on this page were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and Ona (formerly Gitpod) actually diverge.

Attributes where BigQuery and Ona (formerly Gitpod) differ
AttributeBigQueryOna (formerly Gitpod)
Pricing modelUsage-basedPer month by compute unit
PlatformsWeb, APIWeb, Linux, macOS, Windows, AWS
CategoryDatabasesDeveloper Tools

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

Not yet recorded: founded for Ona (formerly Gitpod).

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 Ona (formerly Gitpod)

  • Ephemeral environments
  • Agent sessions
  • Bring your own cloud
  • Devcontainer standard
  • Compute unit budgeting
  • Enterprise identity

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

Ona (formerly Gitpod)

  • Giving a large engineering team identical, disposable environments so onboarding is a link rather than a two day setupnot BigQuery
  • Running several coding agents in parallel against real databases and services instead of on a laptopnot BigQuery
  • Keeping source code off contractor and offshore laptops by moving the whole environment into a controlled accountnot BigQuery
  • Reproducing a hard-to-configure toolchain for a short-lived project without polluting anyone machinenot 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

Ona (formerly Gitpod)

  • Acquisition risk has already materialised: the company closed a sale to OpenAI on 10 August 2026 and now sits inside the Codex team, so nothing public guarantees pricing, continuity or neutrality between model vendors beyond honouring existing commitments
  • Two disruptive changes of direction inside a year, from development environments to agent orchestration and then from independence to acquisition, mean the product a browser IDE buyer originally chose no longer exists in that form
  • The Gitpod Classic sunset on 15 October 2025 required manual conversion of configuration, hand-moved secrets, self-rescued workspace data and a brand new account, which is the clearest available precedent for how the vendor handles deprecation
  • Community self-hosting is finished: the AGPL-3.0 repository is deprecated by its own README, and self-hosted now means a vendor-operated install in your AWS or GCP account, with no Azure, no on-premises and no air-gapped path
  • Pricing is difficult to model because it runs on a compute unit currency where subscription credits expire at the end of the month, the mapping from units to actual compute is not published on the pricing page, and Enterprise is quotation only

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

Ona (formerly Gitpod)

Free
  • FreeFree
    • Up to 3 parallel environments
    • Machines up to 4 vCPU, 16 GB memory and 80 GB disk
    • Ona Cloud multi-tenant only
  • Core$20/mo
    • From 20 US dollars a month, up to 100 team members
    • Includes 80 to 2,200 recurring compute units a month depending on tier
    • Additional units from 10 US dollars per 40 units
  • EnterpriseOn request
    • Single-tenant deployment in your own AWS or GCP account
    • SAML and OIDC single sign-on
    • Dedicated account management and forward deployed engineers

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 Ona (formerly Gitpod) if

  • You need ephemeral environments.
  • You want to start without paying.
  • You work on Web, Linux, macOS, Windows, AWS.
  • You also want agent sessions.
Ona (formerly Gitpod) review

Questions people ask

Is BigQuery or Ona (formerly Gitpod) better?
Neither clearly leads. BigQuery starts at Free and Ona (formerly Gitpod) at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Ona (formerly Gitpod)?
BigQuery starts at Free and Ona (formerly Gitpod) at Free.
Does BigQuery or Ona (formerly Gitpod) run on more platforms?
BigQuery runs on Web, API. Ona (formerly Gitpod) runs on Web, Linux, macOS, Windows, AWS.
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 Ona (formerly Gitpod) is typically brought in for.
What can BigQuery do that Ona (formerly Gitpod) cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Ona (formerly Gitpod) covers Ephemeral environments, Agent sessions, Bring your own cloud, Devcontainer standard.

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.

Ona (formerly Gitpod): Is Gitpod still called Gitpod?

No. It rebranded to Ona in September 2025, and gitpod.io now redirects to ona.com. The company was acquired by OpenAI in a deal that closed on 10 August 2026.

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.

Ona (formerly Gitpod): Can I still self-host it for free?

No. The AGPL-3.0 self-hosted project is deprecated by its own README. What is offered now is a vendor-operated single-tenant install in your AWS or GCP account on the Enterprise plan.

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.

Ona (formerly Gitpod): What happened to Gitpod Classic?

Pay-as-you-go Classic was switched off on 15 October 2025. Migration required converting gitpod.yml to devcontainer.json, moving secrets by hand, downloading workspace data before the cutoff and creating a new account.

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.

Ona (formerly Gitpod): What does it cost?

Core starts at 20 US dollars a month and includes a monthly allowance of Ona Compute Units, with more from 10 dollars per 40 units. Subscription credits expire monthly. Enterprise is quoted.

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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