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Databases · head to head

BigQuery vs Zeabur

BigQuery logo

BigQuery

Databases

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

From
Free
Rated
-
Full reviewWebsite
Zeabur logo

Zeabur

Cloud

AI-powered cloud deployment platform for developers

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Zeabur free plan limited to single server with 48-hour log retention
  • They diverge on capability: BigQuery covers Serverless compute, Zeabur covers AI Agent.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Zeabur actually diverge.

Attributes where BigQuery and Zeabur differ
AttributeBigQueryZeabur
Pricing modelusage-basedFixed monthly plans
PlatformsWeb, Cloud APIWeb, CLI
CategoryDatabasesCloud
Founded20082025

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 Zeabur

  • AI Agent
  • Managed Servers
  • Domain and DNS Management
  • Email Service
  • AI Hub
  • Deploy Templates
  • Git Integration

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

Zeabur

  • Deploying full-stack web applications without DevOps expertisenot BigQuery
  • Building AI agent infrastructure with integrated API accessnot BigQuery
  • Managing multi-region deployments with automated scalingnot BigQuery
  • Running containerized services with built-in domain and emailnot 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.

Zeabur

  • Free plan limited to single server with 48-hour log retention
  • Small team size may limit support and feature velocity
  • Building spec upgrades limited on lower tiers
  • Enterprise support not clearly documented

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

Zeabur

Free
  • FreeFree
    • 1 manageable server
    • 2C4G build specs
    • 48-hour log retention
  • Dev$5/month
    • 3 manageable servers
    • 7-day log retention
    • 250 MB file uploads
  • Pro$19/month
    • 10 manageable servers
    • 4C8G build specs
    • 30-day log retention
  • Team$79/month
    • 3 included seats
    • Unlimited manageable servers
    • 90-day log retention

Which should you pick?

Choose BigQuery if

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

Choose Zeabur if

  • You need ai agent.
  • You want to start without paying.
  • You work on Web, CLI.
  • You also want managed servers.
Zeabur review

Questions people ask

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

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.

Zeabur: What is included in the free plan?

The free plan includes 1 manageable server, 2C4G build specifications, 48-hour log retention, 50 MB file upload limit, and community support.

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.

Zeabur: How do additional seats work on the Team plan?

The Team plan includes 3 seats with additional seats available at $24 per seat per month.

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.

Zeabur: Is there a free trial period?

The Dev and Pro plans include 14 days free before billing begins.

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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