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Databases · head to head

BigQuery vs Cerebrium

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
Cerebrium logo

Cerebrium

Cloud

Serverless GPU infrastructure for real-time AI inference and applications

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.; Cerebrium free Hobby tier limited to 3 apps and 5 GPU concurrency
  • They diverge on capability: BigQuery covers Serverless compute, Cerebrium covers Ultra-fast cold starts.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Cerebrium actually diverge.

Attributes where BigQuery and Cerebrium differ
AttributeBigQueryCerebrium
Pricing modelusage-basedFreemium with monthly plans and per-second compute charges
PlatformsWeb, Cloud APICloud, Docker
CategoryDatabasesCloud
Founded2008Unknown

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 Cerebrium

  • Ultra-fast cold starts
  • Elastic scaling
  • Bring your own code
  • Multi-region failover
  • WebSocket and streaming
  • Asynchronous jobs
  • CI/CD with gradual rollouts
  • OpenTelemetry 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 Cerebrium
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Cerebrium
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Cerebrium
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Cerebrium

Cerebrium

  • Deploying voice agents and conversational AI applicationsnot BigQuery
  • Video and image model serving with low latencynot BigQuery
  • LLM inference and completion endpointsnot BigQuery
  • Real-time embeddings and vector database operationsnot BigQuery
  • Distributed model training with hyperparameter sweepsnot 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.

Cerebrium

  • Free Hobby tier limited to 3 apps and 5 GPU concurrency
  • Standard plan at $100/month required for production deployments
  • Per-second compute pricing requires continuous cost monitoring
  • Storage costs add up for large model files

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

Cerebrium

Free
  • HobbyFree
    • 3 user seats
    • Up to 3 deployed apps
    • 5 GPU concurrency
  • Standard$100/month
    • Unlimited seats and apps
    • 30 GPU concurrency
    • Custom domains
  • Enterprise$undefined/custom
    • Unlimited resources
    • Volume discounts
    • Dedicated support
  • GPU Compute$undefined/per-second
    • T4: $0.000164/s
    • H100: $0.00167/s

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 Cerebrium if

  • You need ultra-fast cold starts.
  • You want to start without paying.
  • You work on Cloud, Docker.
  • You also want elastic scaling.
Cerebrium review

Questions people ask

Is BigQuery or Cerebrium better?
Neither clearly leads. BigQuery starts at Free and Cerebrium at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Cerebrium?
BigQuery starts at Free and Cerebrium at Free.
Does BigQuery or Cerebrium run on more platforms?
BigQuery runs on Web, Cloud API. Cerebrium runs on Cloud, Docker.
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 Cerebrium is typically brought in for.
What can BigQuery do that Cerebrium cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Cerebrium covers Ultra-fast cold starts, Elastic scaling, Bring your own code, Multi-region failover.

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.

Cerebrium: Is Cerebrium only for inference or can it train models?

Cerebrium supports both inference serving and model training with hyperparameter sweeps. It enables deployment of voice agents, LLMs, video models, and other AI applications.

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.

Cerebrium: How do the cold starts compare to other platforms?

Cerebrium achieves 2-4 second cold starts through memory and GPU snapshotting, significantly faster than traditional 30+ second cold boots. This is competitive with platforms like Beam Cloud.

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.

Cerebrium: What compliance certifications does Cerebrium have?

Cerebrium maintains SOC 2 Type II compliance, HIPAA certification, GDPR compliance, and ISO certification. It provides gVisor container isolation and configurable data residency for regulated workloads.

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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