Softwr

Machine Learning · head to head

H2O.ai vs Syft

H2O.ai logo

H2O.ai

Machine Learning

AI Cloud for building and deploying AI applications

From
Free
Rated
-
Full reviewWebsite
Syft logo

Syft

Cybersecurity

Generates a software bill of materials from images, filesystems and archives

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: H2O.ai java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported; Syft lockfile parsing can drop packages silently. An open issue filed in August 2026 reports the yarn v1 cataloguer returning 118 of 745 packages with no error raised, which means a complete bill of materials and an 84 percent incomplete one look identical to the caller.
  • They diverge on capability: H2O.ai covers AutoML, Syft covers Multi-format output.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which H2O.ai and Syft actually diverge.

Attributes where H2O.ai and Syft differ
AttributeH2O.aiSyft
Pricing modelfreemiumOpen source, no licence fee
PlatformsWeb, CloudmacOS, Linux, Windows, Docker
CategoryMachine LearningCybersecurity
Founded2011Unknown

Identical on both: starting price (Free), free tier (Yes), 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 H2O.ai

  • AutoML
  • Distributed computing
  • Feature engineering
  • Model explainability
  • Time series forecasting
  • Spark
  • Hadoop
  • Python

Only in Syft

  • Multi-format output
  • Broad ecosystem coverage
  • Binary classifiers
  • In-toto attestations
  • Library and CLI
  • Pairs with Grype

What people use each for

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

H2O.ai

  • Distributed in-memory machine learning over large datasetsnot Syft
  • Training and productionising models from R or Python against a shared H2O clusternot Syft

Syft

  • Producing a bill of materials for a customer or regulator that requires onenot H2O.ai
  • Feeding an inventory into a vulnerability scanner rather than scanning images directlynot H2O.ai
  • Recording what shipped in a build so a future disclosure can be answered quicklynot H2O.ai
  • Public sector work where an SBOM is a contractual deliverablenot H2O.ai

Where each one falls short

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

H2O.ai

  • Java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported
  • Supported Java versions stop at Java SE 17; newer versions only run by forcing an unsupported version flag and are guaranteed for experiments rather than production
  • H2O-3 only supports numpy below version 2, so a numpy 2 environment must be downgraded
  • Supported Python versions are limited to 3.7 through 3.11
  • The Flow web UI requires an internet browser and is the only graphical interface

Syft

  • Lockfile parsing can drop packages silently. An open issue filed in August 2026 reports the yarn v1 cataloguer returning 118 of 745 packages with no error raised, which means a complete bill of materials and an 84 percent incomplete one look identical to the caller.
  • Fidelity varies sharply by ecosystem. Conan for C and C++, Haskell and Terraform get cataloguer support with no licence data, no dependency relationships and no file ownership, so a C and C++ shop gets the least from it.
  • Binary classification yields no licence or dependency metadata, and vendored or statically linked code is exactly where supply chain risk hides, so the blind spot and the risk overlap.
  • Incorrect CPE values and CPE collisions are recorded as open issues, and since Grype matches on CPE and PURL, an inventory error becomes a false negative in the security report downstream.
  • An inventory is not a risk assessment. Even a perfect bill of materials says a vulnerable version is present, never that the vulnerable function is called, and the triage burden lands entirely on the reader.

Pricing, plan by plan

H2O.ai

Free
  • H2O-3 Open SourceFree
    • Core algorithms
    • AutoML
    • Community support
  • Driverless AIFree
    • Automatic feature engineering
    • Model explainability
    • Enterprise support

Syft

Free
  • SyftFree
    • Apache-2.0
    • No usage limits
    • Community support
  • Anchore Enterprise$undefined/year
    • Policy enforcement and reporting
    • Federal and commercial tiers
    • Pricing not published, quoted on request

Which should you pick?

Choose H2O.ai if

  • You need automl.
  • You want to start without paying.
  • You work on Web, Cloud.
  • You also want distributed computing.
H2O.ai review

Choose Syft if

  • You need multi-format output.
  • You want to start without paying.
  • You work on macOS, Linux, Windows, Docker.
  • You also want broad ecosystem coverage.
Syft review

Questions people ask

Is H2O.ai or Syft better?
Neither clearly leads. H2O.ai starts at Free and Syft at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, H2O.ai or Syft?
H2O.ai starts at Free and Syft at Free.
Does H2O.ai or Syft run on more platforms?
H2O.ai runs on Web, Cloud. Syft runs on macOS, Linux, Windows, Docker.
Can I use H2O.ai for free?
Both have a free tier, so you can try either at no cost before committing.
What is H2O.ai best used for?
H2O.ai is most often used for distributed in-memory machine learning over large datasets, training and productionising models from r or python against a shared h2o cluster. Of those, distributed in-memory machine learning over large datasets and training and productionising models from r or python against a shared h2o cluster are not what Syft is typically brought in for.
What can H2O.ai do that Syft cannot?
H2O.ai covers AutoML, Distributed computing, Feature engineering, Model explainability. Syft covers Multi-format output, Broad ecosystem coverage, Binary classifiers, In-toto attestations.

Answered from the vendors’ own pages

H2O.ai: Is H2O open source and free?

Yes. H2O-3 OSS is free and Apache-licensed, designed for self-managed and experimental workflows. H2O.ai also offers enterprise cloud solutions with additional features.

Source
Syft: Does Syft find vulnerabilities?

No. It produces an inventory. Grype, from the same company, matches that inventory against vulnerability feeds. They are separate tools and the distinction is frequently lost.

H2O.ai: How many companies use H2O's open source platform?

Over 18,000 companies across Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing use H2O's open-source machine learning platform.

Source
Syft: Does anything in the Anchore stack do reachability analysis?

No. Neither Syft, Grype nor the commercial Anchore platform performs call graph or reachability analysis, so none of them tells you whether a vulnerable code path is actually invoked.

Syft: Is it a CNCF or OpenSSF project?

No. It is single-vendor open source owned by Anchore, with no foundation governance. That is a different licence risk profile from Sigstore.

Syft: What does Anchore Enterprise cost?

Not published. The pricing page is contact-sales only, with named but unpriced commercial and federal tiers.

Syft: How do I know my SBOM is complete?

You largely cannot, which is the honest answer. Silent partial parsing is a known open defect, so a bill of materials used for compliance should be spot-checked against a known dependency list.

Share

Related pages

More on H2O.ai

More on Syft

Keep looking

Other head to heads