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Energy · head to head

Bidgely UtilityAI vs DuckDB

Bidgely UtilityAI logo

Bidgely UtilityAI

Energy

AI-powered energy disaggregation and customer engagement

From
On request
Rated
-
Full reviewWebsite
DuckDB logo

DuckDB

Databases

MIT-licensed analytical SQL database that runs inside your process, with no server, no dependencies and one writer at a time.

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only DuckDB has a free tier, so it costs nothing to try first.
  • Each has a real cost: Bidgely UtilityAI sold to utilities rather than to energy consumers, so an individual cannot buy it; DuckDB a database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
  • They diverge on capability: Bidgely UtilityAI covers Energy disaggregation, DuckDB covers In-process execution.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Bidgely UtilityAI and DuckDB actually diverge.

Attributes where Bidgely UtilityAI and DuckDB differ
AttributeBidgely UtilityAIDuckDB
Starting priceOn requestFree
Pricing modelquoteopen-source
Free tierNoYes
PlatformsWeb, Mobile, ApiLinux, macOS, Windows, WebAssembly
CategoryEnergyDatabases
Founded20112019

Identical on both: user rating (Not yet rated).

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 Bidgely UtilityAI

  • Energy disaggregation
  • AI-powered analytics
  • Personalized recommendations
  • Customer segmentation
  • Home energy reports
  • Program enrollment
  • Mobile app platform
  • Behavioral insights

Only in DuckDB

  • In-process execution
  • Vectorised columnar engine
  • Direct file querying
  • Zero dependencies
  • Larger-than-memory queries
  • MIT licence
  • Postgres-flavoured SQL
  • Extension ecosystem

What people use each for

The jobs each tool is most often brought in to do.

Bidgely UtilityAI

  • Disaggregating household energy use to appliance level from meter datanot DuckDB
  • Targeting energy efficiency programmes at the right customersnot DuckDB
  • Detecting EV ownership for utility programmesnot DuckDB
  • Demand response and load flexibility planningnot DuckDB
  • Identifying customers for affordability programmesnot DuckDB

DuckDB

  • Transformation steps in a data pipeline that would otherwise need Spark, replaced by SQL over Parquet in a single processnot Bidgely UtilityAI
  • Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot Bidgely UtilityAI
  • Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot Bidgely UtilityAI
  • Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot Bidgely UtilityAI

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Bidgely UtilityAI

  • Sold to utilities rather than to energy consumers, so an individual cannot buy it
  • Pricing is not published
  • Value depends on access to meter data, so it needs the utility's own data pipeline

DuckDB

  • A database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
  • There is no network protocol, authentication or user management, so exposing it to remote clients means writing and securing your own service around it.
  • It is built for scans and aggregations, not for many small transactions, so a workload of high-frequency single-row inserts and updates performs badly compared with SQLite or Postgres.
  • Storage files are backwards compatible but not forwards compatible, so a file written by a newer version cannot be read by an older one and every consumer of a shared file must be upgraded together.
  • Query memory settings matter: some operations still need to hold significant state, so an under-configured memory limit turns a large join or a high-cardinality aggregation into a spill-heavy query or an out-of-memory failure rather than a slow success.

Pricing, plan by plan

Bidgely UtilityAI

On request

No published plan breakdown. See the Bidgely UtilityAI review.

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Which should you pick?

Choose Bidgely UtilityAI if

  • You need energy disaggregation.
  • You work on Web, Mobile, Api.
  • You also want ai-powered analytics.
Bidgely UtilityAI review

Choose DuckDB if

  • You need in-process execution.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, WebAssembly.
  • You also want vectorised columnar engine.
DuckDB review

Questions people ask

Is Bidgely UtilityAI or DuckDB better?
Neither clearly leads. Bidgely UtilityAI starts at On request and DuckDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Bidgely UtilityAI or DuckDB?
DuckDB has a free tier; the other does not. Paid plans start at On request for Bidgely UtilityAI and Free for DuckDB.
Does Bidgely UtilityAI or DuckDB run on more platforms?
Bidgely UtilityAI runs on Web, Mobile, Api. DuckDB runs on Linux, macOS, Windows, WebAssembly.
Can I use DuckDB for free?
Yes. DuckDB has a free tier, so you can try it without paying. Bidgely UtilityAI starts at On request.
What is Bidgely UtilityAI best used for?
Bidgely UtilityAI is most often used for disaggregating household energy use to appliance level from meter data, targeting energy efficiency programmes at the right customers, detecting ev ownership for utility programmes, demand response and load flexibility planning. Of those, disaggregating household energy use to appliance level from meter data and targeting energy efficiency programmes at the right customers are not what DuckDB is typically brought in for.
What can Bidgely UtilityAI do that DuckDB cannot?
Bidgely UtilityAI covers Energy disaggregation, AI-powered analytics, Personalized recommendations, Customer segmentation. DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies.

Answered from the vendors’ own pages

Bidgely UtilityAI: Why is pricing not published for Bidgely?

Bidgely does not list specific pricing on its website. Interested parties are directed to contact the company through 'Contact Us' or 'Speak with an Expert' options for pricing inquiries.

Source
DuckDB: Can multiple applications share one DuckDB database?

Not for writing. One process holds the database read-write; others may attach read-only and will not see later writes. Shared multi-writer access needs a different database or a table format with a catalogue.

Bidgely UtilityAI: How do I get a pricing quote from Bidgely?

Bidgely directs potential customers to use the 'Speak with an Expert' call-to-action on their website to discuss pricing and capabilities.

Source
DuckDB: Is it a replacement for a data warehouse?

For single-node analytical workloads up to a few hundred gigabytes it very often is. It is not a replacement when many concurrent users need a shared, governed, always-on service.

Bidgely UtilityAI: Is there a free trial available without a sales contact?

The website does not mention a free trial option. All pricing inquiries appear to require direct engagement with Bidgely's sales team.

Source
DuckDB: Do I have to load data into it?

No. It queries Parquet, CSV, JSON and Arrow in place, including on object storage. Its own storage format is optional and mainly useful when you want indexes, constraints and faster repeated access.

DuckDB: What is MotherDuck's relationship to it?

MotherDuck is a separate company offering a managed and hybrid service built on the DuckDB engine. DuckDB itself remains MIT-licensed and independent of it, with the IP held by the DuckDB Foundation.

DuckDB: Is it suitable for OLTP?

No. It is designed for analytical scans. For transactional workloads with frequent small writes, SQLite or Postgres is the right tool.

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