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Databases · head to head

BigQuery vs Canary Labs

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
Canary Labs logo

Canary Labs

Databases

Industrial process historian with published per-tag pricing and no client licence fees

From
$3000/year
Rated
-
Full reviewWebsite

The short version

  • Only BigQuery has a free tier, so it costs nothing to try first.
  • 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.; Canary Labs pricing steps by tag band rather than actual tags, so a site sitting just above a threshold pays the next band in full; growth from 3,400 to 3,600 tags is a licence purchase, not a rounding error.
  • They diverge on capability: BigQuery covers Serverless compute, Canary Labs covers Loss-less compression.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which BigQuery and Canary Labs actually diverge.

Attributes where BigQuery and Canary Labs differ
AttributeBigQueryCanary Labs
Starting priceFree$3000/year
Pricing modelusage-basedPer tag band per year
Free tierYesNo
PlatformsWeb, Cloud APIWindows, Web
Founded2008Unknown

Identical on both: 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 Canary Labs

  • Loss-less compression
  • Unlimited clients
  • Store and forward
  • Views service
  • Sparkplug B
  • Calculation engine
  • Axiom
  • ODBC and REST

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

Canary Labs

  • A manufacturer that needs a compliant historian but cannot justify a PI System enterprise agreementnot BigQuery
  • A water utility replacing SCADA-embedded logging with a historian that survives the next SCADA replacementnot BigQuery
  • An Ignition site that wants long-term storage and trending without paying per-client for dashboardsnot BigQuery
  • A plant with regulatory data-retention obligations where swinging-door compression would not survive an auditnot 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.

Canary Labs

  • Pricing steps by tag band rather than actual tags, so a site sitting just above a threshold pays the next band in full; growth from 3,400 to 3,600 tags is a licence purchase, not a rounding error.
  • CustomerCare at 18 percent of licence cost is effectively mandatory on perpetual licences because lapsing it removes upgrades, so the perpetual option is not really a one-time cost.
  • The storage format is proprietary; getting data out at volume means the ODBC connector, an additional 2,500 USD one-time or 1,000 USD a year, or writing against the REST API.
  • Canary is a small vendor with a limited partner network outside North America, so support coverage and integrator availability in Europe and Asia are thinner than for AVEVA or Ignition.
  • It is a Windows-only server product with no Linux or container deployment, which complicates placing collectors in modern edge estates.

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

Canary Labs

$3000/year
  • Subscription, 3,500 tags$3000/year
    • Unlimited Axiom clients
    • Unlimited Excel clients
    • Support and upgrades included
  • Perpetual, 3,500 tags$8730/one-time
    • 7,400 USD licence plus 1,330 USD first-year CustomerCare
    • CustomerCare renews at 18 percent of licence cost per year
    • Unlimited clients
  • Private cloud, 3,500 tags$12320/year
    • Hosted by Canary
    • 1,500 USD one-time setup fee per system
    • Scales to around 58,684 USD per year at 60,000 tags
  • Enterprise unlimited$undefined/year
    • Unlimited tags
    • Negotiated licensing terms
    • Quoted pricing

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 Canary Labs if

  • You need loss-less compression.
  • You work on Windows, Web.
  • You also want unlimited clients.
Canary Labs review

Questions people ask

Is BigQuery or Canary Labs better?
Neither clearly leads. BigQuery starts at Free and Canary Labs at $3000/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Canary Labs?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and $3000/year for Canary Labs.
Does BigQuery or Canary Labs run on more platforms?
BigQuery runs on Web, Cloud API. Canary Labs runs on Windows, Web.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Canary Labs starts at $3000/year.
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 Canary Labs is typically brought in for.
What can BigQuery do that Canary Labs cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Canary Labs covers Loss-less compression, Unlimited clients, Store and forward, Views service.

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.

Canary Labs: What does Canary cost?

8,730 USD for a 3,500 tag perpetual system including first-year CustomerCare, or 3,000 USD a year on subscription.

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.

Canary Labs: Are client licences extra?

No. Unlimited Axiom browser clients and unlimited Excel clients are included on every tier.

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.

Canary Labs: Is perpetual cheaper than subscription?

Canary states perpetual pays off after roughly three years, and after that you still pay 18 percent CustomerCare annually.

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.

Canary Labs: Does it use lossy compression?

No. Canary stores every collected value rather than approximating the trend, which matters for regulated data retention.

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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