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Databases · head to head

BigQuery vs ConfigCat

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
ConfigCat logo

ConfigCat

Developer Tools

Feature flag service priced per flag and environment with unlimited seats and unlimited monthly active users

From
Free
Free plan
Yes
Visit ConfigCatFull review

The short version

  • Free plans on both; paid from $6.25/mo and $110/mo.
  • Only BigQuery lists serverless compute; only ConfigCat lists feature flags and configuration.
  • Prices and features on this page were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and ConfigCat actually diverge.

Attributes where BigQuery and ConfigCat differ
AttributeBigQueryConfigCat
Pricing modelUsage-basedPer month by feature flag and environment count
PlatformsWeb, APIWeb, API, iOS, Android, Windows
CategoryDatabasesDeveloper Tools

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

Not yet recorded: founded for ConfigCat.

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 ConfigCat

  • Feature flags and configuration
  • Percentage rollouts
  • Targeting rules and segments
  • Unlimited seats
  • Global CDN delivery
  • Audit log
  • Public API and webhooks
  • SDKs across languages

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

ConfigCat

  • A consumer or mobile app with millions of users where per-MAU flag pricing would cost more than the flags are worthnot BigQuery
  • A team that wants every engineer, product manager and support agent to have a login without paying per seatnot BigQuery
  • Decoupling deployment from release with percentage rollouts and a kill switch, without buying an experimentation platformnot BigQuery
  • A small team that needs ten flags permanently and can run indefinitely on the free tiernot 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

ConfigCat

  • There is no experimentation or statistical analysis engine, so teams wanting real A/B tests with significance testing must buy and integrate a separate analytics or experimentation product
  • Pricing by flag count punishes exactly the hygiene you want: the Pro tier caps at 100 flags, so teams that do not aggressively retire stale flags are pushed to the 325 dollar Smart tier for housekeeping reasons rather than growth
  • The free tier allows only 2 environments, which does not cover the common development, staging and production split, so almost any real deployment starts on a paid plan
  • It is a smaller vendor than the category incumbents, so enterprise procurement teams will find less depth in compliance attestations, contractual terms and reference architecture than LaunchDarkly offers
  • Flag evaluation relies on SDK-side caching of a CDN-delivered config, which is fast but means propagation of a change is eventually consistent rather than instant, something to understand before relying on it as an emergency kill switch

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

ConfigCat

Free
  • Forever FreeFree
    • 10 feature flags
    • 2 environments and 2 products
    • 2 segments
  • Pro$110/mo
    • 100 feature flags
    • 3 environments and 3 products
    • Unlimited seats and MAUs
  • Smart$325/mo
    • Unlimited feature flags
    • Unlimited environments, products and segments
    • Unlimited seats and MAUs
  • Enterprise$900/mo
    • Everything in Smart
    • Enterprise support and controls
    • Unlimited everything on flag counts

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 ConfigCat if

  • You need feature flags and configuration.
  • You want to start without paying.
  • You work on Web, API, iOS, Android, Windows.
  • You also want percentage rollouts.
ConfigCat review

Questions people ask

Is BigQuery or ConfigCat better?
Neither clearly leads. BigQuery starts at Free and ConfigCat at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or ConfigCat?
BigQuery starts at Free and ConfigCat at Free.
Does BigQuery or ConfigCat run on more platforms?
BigQuery runs on Web, API. ConfigCat runs on Web, API, iOS, Android, Windows.
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 ConfigCat is typically brought in for.
What can BigQuery do that ConfigCat cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. ConfigCat covers Feature flags and configuration, Percentage rollouts, Targeting rules and segments, Unlimited seats.

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.

ConfigCat: How is ConfigCat priced?

By feature flag, environment and product count. Seats, monthly active users and flag reads are unlimited on every plan.

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.

ConfigCat: Is the free tier usable?

For small projects yes, but at 10 flags and 2 environments it will not cover a dev, staging and production setup.

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.

ConfigCat: Can it run A/B tests?

It can split traffic, but there is no statistics engine. You need a separate analytics tool to decide a winner.

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.

ConfigCat: How fast does a flag change take effect?

SDKs poll and cache, so changes propagate on the polling interval rather than instantly.

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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