Databases · head to head
BigQuery vs Fig
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
Fig
Developer Tools
IDE-style autocomplete for your existing terminal
- From
- Free
- Free plan
- Yes
The short version
- Fig is free; BigQuery starts at $6.25/mo.
- Where Fig falls short: the standalone desktop app is no longer available or supported.
- Prices and features on this page were last checked on 13 November 2026.
Where they differ
Only the attributes on which BigQuery and Fig actually diverge.
Identical on both: starting price (Free), free tier (Yes).
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 Fig
Nothing recorded that BigQuery does not also cover.
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 Fig
- Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Fig
- Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Fig
- Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Fig
Fig
No use cases recorded yet. See the Fig review.
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
Fig
- Shut down on 1 September 2024. Amazon acquired Fig in August 2023 and folded it into Amazon Q Developer, which now carries the terminal autocomplete
- The standalone desktop app is no longer available or supported
- The completion specs remain open source on GitHub, but the product that consumed them is gone
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
Fig
FreeNo published plan breakdown. See the Fig review.
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.
Questions people ask
- Is BigQuery or Fig better?
- Neither clearly leads. BigQuery starts at Free and Fig at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery or Fig?
- BigQuery starts at Free and Fig at Free.
- Does BigQuery or Fig run on more platforms?
- BigQuery runs on Web, API. Fig 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 Fig is typically brought in for.
- What can BigQuery do that Fig cannot?
- BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.
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.
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.
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.
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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- Fig vs Helix
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