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Databases · head to head

BigQuery vs Tinybird

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
Tinybird logo

Tinybird

Databases

Managed ClickHouse with a workflow that turns SQL queries into hosted HTTP APIs

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.; Tinybird it is ClickHouse underneath, so it inherits ClickHouse limits: multi-table joins degrade badly at scale, updates and deletes are expensive mutations rather than cheap operations, and a poorly chosen sorting key at table creation cannot be fixed without rebuilding the data.
  • They diverge on capability: BigQuery covers Serverless compute, Tinybird covers Managed ClickHouse.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and Tinybird actually diverge.

Attributes where BigQuery and Tinybird differ
AttributeBigQueryTinybird
Pricing modelusage-basedPer month by compute and storage
PlatformsWeb, Cloud APIWeb, Cloud, Linux, macOS
Founded2008Unknown

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

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 Tinybird

  • Managed ClickHouse
  • Pipes as APIs
  • Events HTTP endpoint
  • Streaming connectors
  • Materialized views
  • Git-based workflow
  • Token-scoped auth
  • Observability

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

Tinybird

  • A SaaS product adding a per-customer usage dashboard that must render in under a second across billions of eventsnot BigQuery
  • A team building rate limiting or fraud checks that need an aggregate over the last few minutes returned inside a request cyclenot BigQuery
  • A data team offloading interactive operational dashboards from Snowflake, where per-query warehouse cost makes constant refresh untenablenot BigQuery
  • A game or ad-tech company ingesting a high-volume event stream and exposing live counters back to customers through an APInot 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.

Tinybird

  • It is ClickHouse underneath, so it inherits ClickHouse limits: multi-table joins degrade badly at scale, updates and deletes are expensive mutations rather than cheap operations, and a poorly chosen sorting key at table creation cannot be fixed without rebuilding the data.
  • Compute is metered per vCPU-second with overage at 0.0002 USD per second, so an inefficient query shipped to production shows up directly on the invoice rather than merely running slowly.
  • Only the Enterprise tier gets horizontal scaling and dedicated infrastructure; Free, Developer and SaaS all run on shared infrastructure with vertical scaling only, which caps both isolation and headroom for anyone not on a custom contract.
  • Storage is billed at 0.058 USD per gigabyte on top of compute, and egress is charged separately at 0.01 USD per gigabyte intra-cloud and 0.10 USD inter-cloud, so a high-fanout API serving many small responses accrues costs in three places at once.
  • You are building on a proprietary workflow around an open database: the pipes, tokens and API layer are Tinybird specific, so leaving means keeping your data but rewriting the entire serving layer you adopted Tinybird to avoid writing.

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

Tinybird

Free
  • FreeFree
    • 0.25 vCPU on shared infrastructure
    • 10 GB storage included
    • 1,000 requests per day
  • Developer$25/month
    • 0.5 vCPU scaling to 8 vCPU
    • 25 GB storage included
    • Two replicas
  • SaaS$undefined/month
    • Up to 32 vCPU
    • 500 GB storage included
    • Four to sixteen threads per request
  • Enterprise$undefined/year
    • Unlimited vCPU and bottomless storage
    • Dedicated infrastructure and private regions
    • Vertical and horizontal scaling

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 Tinybird if

  • You need managed clickhouse.
  • You want to start without paying.
  • You work on Web, Cloud, Linux, macOS.
  • You also want pipes as apis.
Tinybird review

Questions people ask

Is BigQuery or Tinybird better?
Neither clearly leads. BigQuery starts at Free and Tinybird at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Tinybird?
BigQuery starts at Free and Tinybird at Free.
Does BigQuery or Tinybird run on more platforms?
BigQuery runs on Web, Cloud API. Tinybird runs on Web, Cloud, Linux, macOS.
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 Tinybird is typically brought in for.
What can BigQuery do that Tinybird cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Tinybird covers Managed ClickHouse, Pipes as APIs, Events HTTP endpoint, Streaming connectors.

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.

Tinybird: Is Tinybird just hosted ClickHouse?

No. The database is ClickHouse, but the product is the layer above it: publishing parameterised SQL as authenticated, rate-limited REST endpoints without writing an API server.

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.

Tinybird: What does it actually cost?

Free tier at 1,000 requests a day, Developer from 25 USD a month, then compute at 0.0002 USD per vCPU-second and storage at 0.058 USD per gigabyte. Higher tiers are quoted.

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.

Tinybird: Can I run it on my own infrastructure?

Only on Enterprise, which offers dedicated infrastructure and private regions. Lower tiers are shared multi-tenant cloud.

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.

Tinybird: Does it handle updates and deletes?

Poorly, as ClickHouse does. Design for append-only event data; frequent mutation is the wrong workload for this engine.

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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