Maritime · head to head
Bearing AI vs Kaleris
Bearing AI
Maritime
Machine learning vessel performance models for voyage and commercial decisions, embedded in partner platforms
- From
- On request
- Rated
- -
Kaleris
Maritime
Terminal operating, yard, rail and vessel software assembled from acquired supply chain brands
- From
- On request
- Rated
- -
The short version
- Each has a real cost: Bearing AI bearing increasingly reaches customers embedded in partner platforms such as StormGeo, so the contract, support path and commercial relationship may be with the partner rather than with Bearing.; Kaleris the portfolio was assembled by acquisition, so the single platform description is commercial rather than technical, and connecting two Kaleris products often requires professional services and a custom interface.
- They diverge on capability: Bearing AI covers Vessel-specific consumption models, Kaleris covers Terminal operating system.
- Prices and features above were last checked on 1 September 2026.
Where they differ
Only the attributes on which Bearing AI and Kaleris actually diverge.
| Attribute | Bearing AI | Kaleris |
|---|---|---|
| Platforms | Web, Cloud, API | Web, On-premise, Private cloud |
Identical on both: starting price (On request), pricing model (quote), free tier (No), user rating (Not yet rated), category (Maritime).
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 Bearing AI
- Vessel-specific consumption models
- Speed optimisation
- Emissions forecasting
- Voyage evaluation
- AIS-derived data
- Weather-aware routing inputs
- Fleet benchmarking
- Partner platform embedding
Only in Kaleris
- Terminal operating system
- Yard management
- Transportation management
- Carrier and vessel solutions
- Rail visibility
- Maintenance and repair operations
- Execution and visibility platform
- Equipment control integration
What people use each for
The jobs each tool is most often brought in to do.
Bearing AI
- A chartering desk evaluating fixtures against learned consumption rather than a delivery-era speed and consumption tablenot Kaleris
- Projecting a vessel CII rating for a planned trading pattern before committing to itnot Kaleris
- Identifying which sister ship in a series is underperforming because of hull condition rather than routingnot Kaleris
- Feeding realistic consumption predictions into an existing voyage optimisation platform through an APInot Kaleris
Kaleris
- A container terminal replacing a legacy or in-house terminal operating system before an automation programmenot Bearing AI
- A port authority standardising several terminals on one operating system and reporting layernot Bearing AI
- An ocean carrier that needs stowage planning and vessel performance tooling alongside terminal systemsnot Bearing AI
- A rail or intermodal operator trying to reduce demurrage exposure with asset level visibilitynot Bearing AI
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Bearing AI
- Bearing increasingly reaches customers embedded in partner platforms such as StormGeo, so the contract, support path and commercial relationship may be with the partner rather than with Bearing.
- Model accuracy depends on the volume and honesty of historical voyage data, and fleets with sparse or inaccurate noon reporting get materially weaker vessel-specific models.
- It is a modelling layer rather than a voyage management system of record, so it must be bought alongside a platform that handles the operational workflow.
- As a venture-funded specialist with a small headcount, its long-term independence is uncertain, and a modelling layer inside someone else product is a fragile position if a partnership lapses.
- Nothing is published on pricing or fleet size thresholds, so small owners cannot tell whether they are a viable customer without entering a sales process.
Kaleris
- The portfolio was assembled by acquisition, so the single platform description is commercial rather than technical, and connecting two Kaleris products often requires professional services and a custom interface.
- A terminal operating system implementation runs 12 to 24 months with parallel operation, and the software licence is a minor line next to integration, equipment interfaces and retraining crane and gate staff.
- Private equity ownership means the roadmap is shaped by acquisition strategy and an eventual exit, and a terminal signing a decade-long dependency has no visibility of who the next owner will be.
- Configuration expertise is concentrated in the vendor and a small consultancy pool, so terminals end up buying vendor services for changes an internal team should be able to make.
- The strength is container handling, and bulk, breakbulk and ro-ro terminals get a thinner functional fit that has to be closed with configuration and workarounds.
Pricing, plan by plan
Bearing AI
On request- Bearing AI$undefined/year
- Priced per vessel per year on annual agreements
- Often purchased indirectly as a component of a partner voyage optimisation service
- Model onboarding requires historical voyage data supplied by the customer
Kaleris
On request- Kaleris platform$undefined/year
- Quoted per site, by throughput and modules taken
- Implementation and integration services are the larger share of first-year cost
- Equipment and gate interfaces scoped and priced individually
Which should you pick?
Choose Bearing AI if
- You need vessel-specific consumption models.
- You work on Web, Cloud, API.
- You also want speed optimisation.
Choose Kaleris if
- You need terminal operating system.
- You work on Web, On-premise, Private cloud.
- You also want yard management.
Questions people ask
- Is Bearing AI or Kaleris better?
- Neither clearly leads. Bearing AI starts at On request and Kaleris at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Bearing AI or Kaleris?
- Bearing AI starts at On request and Kaleris at On request.
- Does Bearing AI or Kaleris run on more platforms?
- Bearing AI runs on Web, Cloud, API. Kaleris runs on Web, On-premise, Private cloud.
- What is Bearing AI best used for?
- Bearing AI is most often used for a chartering desk evaluating fixtures against learned consumption rather than a delivery-era speed and consumption table, projecting a vessel cii rating for a planned trading pattern before committing to it, identifying which sister ship in a series is underperforming because of hull condition rather than routing, feeding realistic consumption predictions into an existing voyage optimisation platform through an api. Of those, a chartering desk evaluating fixtures against learned consumption rather than a delivery-era speed and consumption table and projecting a vessel cii rating for a planned trading pattern before committing to it are not what Kaleris is typically brought in for.
- What can Bearing AI do that Kaleris cannot?
- Bearing AI covers Vessel-specific consumption models, Speed optimisation, Emissions forecasting, Voyage evaluation. Kaleris covers Terminal operating system, Yard management, Transportation management, Carrier and vessel solutions.
Answered from the vendors’ own pages
Bearing AI: Can I buy Bearing AI directly?
Yes, but a growing share of its distribution is through partners such as StormGeo, where the models are embedded in that vendor voyage optimisation service.
Kaleris: Is Kaleris the same thing as Navis?
No. Navis is the terminal operating system product line, now owned by Kaleris after Cargotec sold it to Accel-KKR in 2021. Kaleris is the parent group and sells several other product lines alongside it.
Bearing AI: Does it replace a voyage management system?
No. It is a performance modelling layer that feeds commercial and operational decisions made in another system.
Kaleris: Who owns Kaleris?
It is backed by the private equity firm Accel-KKR and assembled from acquired software businesses.
Bearing AI: What data does it need?
Historical voyage data, noon reports and AIS positions, plus weather history. Data quality drives model quality directly.
Kaleris: How long does a terminal operating system deployment take?
Plan for 12 to 24 months at a working container terminal, including parallel running. Anything shorter usually means a limited scope or a small facility.
Bearing AI: Does it cover CII?
Yes, it projects CII ratings from the learned vessel model rather than from nominal consumption figures.
Kaleris: Does it suit bulk or breakbulk terminals?
Less well. The functional depth is in container handling, and other cargo types require significant configuration.
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