Softwr

Databases · head to head

BigQuery vs Convex

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
Convex logo

Convex

Databases

Reactive backend combining a document database, TypeScript server functions and live queries, source-available under the Functional Source Licence.

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.; Convex the Functional Source Licence is not an OSI open source licence: competing use is prohibited until each release reaches its second anniversary and converts to Apache 2.0, so you may self-host but you may not build a service on it.
  • They diverge on capability: BigQuery covers Serverless compute, Convex covers Reactive queries.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Convex actually diverge.

Attributes where BigQuery and Convex differ
AttributeBigQueryConvex
Pricing modelusage-basedsubscription
PlatformsWeb, Cloud APIWeb
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 Convex

  • Reactive queries
  • TypeScript server functions
  • ACID transactions
  • Document database
  • Scheduling and workflows
  • File storage
  • Text and vector search
  • End-to-end types

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

Convex

  • Collaborative applications where several users see the same data and every client must reflect a change immediatelynot BigQuery
  • Agent backends that need durable state, scheduled work and transactional writes without assembling a queue, a database and a cachenot BigQuery
  • Small product teams who need a complete backend, including auth integration, file storage and subscriptions, without hiring infrastructure engineersnot BigQuery
  • Prototypes that must become production without a rewrite of the data layer, where end-to-end TypeScript types remove a class of integration bugsnot 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.

Convex

  • The Functional Source Licence is not an OSI open source licence: competing use is prohibited until each release reaches its second anniversary and converts to Apache 2.0, so you may self-host but you may not build a service on it.
  • Transactions are bounded at one second of user code, 16 MiB read and written, 32,000 documents scanned and 16,000 written, so every backfill, migration or bulk import has to be chunked into scheduled batches rather than written as a single operation.
  • There is no SQL and no query planner; you declare up to 32 indexes per table and traverse them, and joins are loops in TypeScript, so an unanticipated access pattern requires a schema and index change rather than a new query.
  • It is not an analytical database, so reporting means streaming data out to a warehouse and BI tools cannot be pointed at Convex directly, which adds a pipeline the architecture diagram did not originally include.
  • The application is written against Convex's function and client APIs rather than a standard protocol, so leaving means rewriting the data access layer and replacing the reactivity model, not repointing a connection string.

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

Convex

Free
  • Free & StarterFree
    • Supports 1-6 developers
    • Reactive database
    • File storage
  • Professional$25/month per developer
    • Supports up to 20 developers
    • All Starter features
    • Log streaming
  • Business & Enterprise$2500/month minimum
    • Supports 50+ developers
    • SAML/SSO
    • Service SLAs

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 Convex if

  • You need reactive queries.
  • You want to start without paying.
  • You also want typescript server functions.
Convex review

Questions people ask

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

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.

Convex: Is Convex open source?

It is source-available under FSL-1.1-Apache-2.0. You may read, modify and self-host it, but competing use is prohibited until each release converts to Apache 2.0 on its second anniversary.

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.

Convex: Can I self-host it?

Yes. The backend, dashboard and CLI can run on your own infrastructure, with most of the features of the cloud product. Self-hosted instances include a telemetry beacon that can be disabled.

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.

Convex: Does it support SQL?

No. Data is accessed through a TypeScript query builder over declared indexes. Relationships are traversed in code, which is explicit and type-safe but means no ad hoc querying.

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.

Convex: What happens if a mutation exceeds the limits?

It fails rather than running longer, so bulk work must be split into batches and scheduled. The limits are per transaction: one second of user code, 16 MiB read and written, 32,000 documents scanned and 16,000 written.

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.

Convex: How does reactivity actually work?

Queries are deterministic functions and Convex records the data each one read. When a mutation changes that data, affected queries are re-run and subscribed clients receive the new result, so cache invalidation is handled by the platform.

Share

Related pages

More on BigQuery

More on Convex

Keep looking

Other head to heads