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Databases · head to head

BigQuery vs Sweep

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
Sweep logo

Sweep

Developer Tools

AI coding assistant for JetBrains IDEs

From
Free
Free plan
Yes
Visit SweepFull review

The short version

  • Sweep is an AI-powered coding assistant plugin.
  • Free plans on both; paid from $6.25/mo and $20/mo.
  • Only BigQuery lists serverless compute; only Sweep lists AI Agent.
  • Where Sweep falls short: limited to JetBrains IDEs only, not available for VS Code or other editors.
  • Prices and features on this page were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Sweep actually diverge.

Attributes where BigQuery and Sweep differ
AttributeBigQuerySweep
Pricing modelUsage-basedFreemium with paid tiers
PlatformsWeb, APIJetBrains IDEs
CategoryDatabasesDeveloper Tools
Founded20082023

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 Sweep

  • Autocomplete
  • AI Agent
  • Full codebase indexing
  • Code review functionality
  • Multi-IDE support
  • GitHub 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 Sweep
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Sweep
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Sweep
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Sweep

Sweep

  • Accelerating code completion for JetBrains developersnot BigQuery
  • Automating refactoring tasks across codebasesnot BigQuery
  • Handling bug fixes with full repository contextnot BigQuery
  • Code review and quality assurancenot 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

Sweep

  • Limited to JetBrains IDEs only, not available for VS Code or other editors
  • Free tier limited to 1,000 autocompletes per month
  • API credit consumption varies by feature usage
  • Performance dependent on codebase size and indexing quality

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

Sweep

Free
  • FreeFree
    • 1,000 autocompletes
    • 5 USD API credits included
    • All JetBrains IDEs supported
  • Pro$20/mo
    • Unlimited autocomplete
    • Higher API credit allocation
    • Priority support
  • Ultra$60/mo
    • Unlimited autocomplete
    • Maximum API credit allocation
    • Extended context window

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 Sweep if

  • You need autocomplete.
  • You want to start without paying.
  • You work on JetBrains IDEs.
  • You also want ai agent.
Sweep review

Questions people ask

Is BigQuery or Sweep better?
Neither clearly leads. BigQuery starts at Free and Sweep at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Sweep?
BigQuery starts at Free and Sweep at Free.
Does BigQuery or Sweep run on more platforms?
BigQuery runs on Web, API. Sweep runs on JetBrains IDEs.
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 Sweep is typically brought in for.
What can BigQuery do that Sweep cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Sweep covers Autocomplete, AI Agent, Full codebase indexing, Code review functionality.

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.

Sweep: What programming languages does Sweep support?

Sweep works with all programming languages supported by JetBrains IDEs, including Python, Java, JavaScript, Go, Kotlin, C++, and many others.

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.

Sweep: Does Sweep retain my code data?

No. Sweep is SOC 2 Type II certified with zero data retention. Code is processed for suggestions but not stored.

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.

Sweep: Can I cancel my Sweep subscription anytime?

Yes. You can cancel your subscription at any time from your account settings with no long-term commitments or cancellation fees.

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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