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Databases · head to head

BigQuery vs Cody

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
Cody logo

Cody

Developer Tools

AI coding assistant powered by code search

From
Free
Free plan
Yes
Visit CodyFull review

The short version

  • Cody is free; BigQuery starts at $6.25/mo.
  • Where Cody falls short: sourcegraph now publishes a single Enterprise plan starting at $16K, with no free or individual Pro tier listed on the pricing page.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which BigQuery and Cody actually diverge.

Attributes where BigQuery and Cody differ
AttributeBigQueryCody
Pricing modelUsage-basedFreemium
PlatformsWeb, APIWeb
CategoryDatabasesDeveloper Tools
Founded20082013

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 Cody

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

Cody

  • Code completion and intelligent autocomplete in IDEsnot BigQuery
  • Developer chat with codebase context awarenessnot BigQuery
  • Automated debugging and error identificationnot BigQuery
  • Customizable prompts for workflow automationnot 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

Cody

  • Sourcegraph now publishes a single Enterprise plan starting at $16K, with no free or individual Pro tier listed on the pricing page
  • sourcegraph.com/cody redirects to the documentation rather than a Cody product or pricing page
  • AI features are metered as credits included per user, and exceeding the allocation requires buying a higher volume bucket
  • Volume credit buckets are sold as an add-on rather than included
  • A customer success manager and premium support are optional paid extras on top of the Enterprise plan
  • Standard support is 24x5 rather than 24x7, with round-the-clock coverage sold as an upgrade
  • The published $16K figure is a starting price that scales with team size, so the actual cost is quoted rather than listed

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

Cody

Free

No published plan breakdown. See the Cody 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.
BigQuery review

Choose Cody if

  • You want to start without paying.
Cody review

Questions people ask

Is BigQuery or Cody better?
Neither clearly leads. BigQuery starts at Free and Cody at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Cody?
BigQuery starts at Free and Cody at Free.
Does BigQuery or Cody run on more platforms?
BigQuery runs on Web, API. Cody 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 Cody is typically brought in for.
What can BigQuery do that Cody 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.

Cody: Is Cody free?

Cody offers a free tier for individual developers via VS Code, JetBrains, Visual Studio, and the web app. Unlimited free usage is available for solo developers.

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.

Cody: What does Cody Enterprise cost?

Cody Enterprise pricing is custom and requires contacting Sourcegraph sales. It is available for Sourcegraph Enterprise customers and must be quoted based on your deployment and usage needs.

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.

Cody: What LLMs does Cody use?

Cody uses the latest large language models for code completions, chat, and debugging. It leverages Sourcegraph's advanced Search API to pull codebase context from local and remote repositories.

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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