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Kardinal announced a self-serve route optimization API on September 29, 2026. It gives engineering teams and AI agents a way to submit vehicles, stops, and operational constraints to an optimization service and receive route plans. The main work is not simply making a valid request: teams must translate the rules drivers face in the field into a model the API can use.
What Kardinal launched
Kardinal says it has developed route optimization technology since 2015 and is now exposing it through an API for technical teams and AI agents. Its launch materials list a developer console, API keys, usage tracking, a Python SDK, machine-readable documentation, and public modeling guides. The documented workflow includes POST /v1/optimize, with Python and TypeScript SDK examples. These are vendor-described capabilities; no independent integration or performance testing was conducted for this article. Kardinal’s launch materials
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This is software, not a dispatch device or a vehicle product. A customer application sends the operational data; Kardinal’s API returns route plans. Teams still need to decide how those plans are reviewed, assigned, and used in their own dispatch process.
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Why route optimization is an operational modeling problem
A request can be syntactically correct yet produce a plan that does not reflect how a delivery or service operation actually works. The model has to represent relevant limits and priorities, and the input data must describe them consistently. Kardinal’s launch materials emphasize modeling guidance alongside API access for that reason.
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Kardinal lists possible constraints and operating contexts including:
- Time windows, driver breaks, and pickup-and-delivery pairing.
- Vehicle capacity, route balancing, and preferred zones.
- Traffic prediction and mixed vehicle profiles such as bikes, walkers, and vans.
- Parcel delivery, field services, bulky or heavy goods, fresh delivery, waste collection, and retail or e-commerce.
These examples describe the vendor’s stated scope, not proof that every operation or combination of constraints is supported in the same way. Before building around the API, check whether the exact rules that affect your routes can be represented and how the response handles them.
What the launch means for developers and AI agents
Kardinal is positioning the API for both people integrating it and agents that may help build or operate software. Its launch announcement includes agent-oriented documentation, modeling guidance, command-line tooling, and an SDK intended to catch mistakes early. The practical value depends on more than agent access: an agent still needs clear data definitions, valid constraints, and a way for the team to assess whether a generated model matches field operations.
Kardinal co-founder Cédric Hervet described the company’s expectation this way: “With this launch, we are making a bet: thanks to AI, route optimization will not just be faster, it will be more reliable.” That is a stated ambition, not a measured result. Explanatory API responses and the homepage’s MCP and CLI guardrails are marked as upcoming or “SOON” in the launch materials; they should not be treated as available launch features.
What Kardinal’s developer survey says—and does not say
Kardinal reports surveying 145 professionals from July 1 through September 8, 2026, with detailed results for 99 technical respondents. The survey is company-published and self-reported, so it describes the respondents rather than establishing a representative picture of all route-optimization buyers or validating Kardinal’s product quality. Kardinal’s survey and launch materials
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- Among the 99 technical respondents, 56% cited the gap between a model and real field conditions as a route-optimization difficulty; 47% cited performance or compute time, and 43% cited changing business needs.
- 45% preferred a self-hosted or open-source approach for a capability like route optimization.
- 35% said AI agents were part of their daily workflow, while 17% said they did not use agents at all.
- For a separate question answered by 92 technical respondents, 39% said missing public pricing bothered them a lot and 26% called it a dealbreaker.
The results help explain why Kardinal foregrounds modeling guidance, developer tooling, and public pricing. They are audience context, not evidence that the API performs well or fits a particular operation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How pricing is presented
Kardinal says it charges per optimized stop, not per vehicle or user, and that re-optimizing the same stop within 24 hours does not incur another charge. Its public pricing page displays 1,000 free stops and prepaid packs, but explicitly labels the currency and amounts as placeholders to confirm. Treat the figures below as page displays—not settled commercial terms—and confirm the live price before budgeting. The page also says credits remain valid while the account is active and that optional auto top-up can be switched off. Kardinal pricing page
| Displayed allowance or pack | Displayed amount | Qualification |
|---|---|---|
| 1,000 stops | Free | Listed on the pricing page; confirm current terms with Kardinal. |
| 2,000 stops | €99 | Currency and amount are explicitly marked as placeholders to confirm. |
| 10,000 stops | €299 | Currency and amount are explicitly marked as placeholders to confirm. |
| 50,000 stops | €999 | Currency and amount are explicitly marked as placeholders to confirm. |
| 250,000 stops | €2,999 | Currency and amount are explicitly marked as placeholders to confirm. |
For very large volumes or needs involving sequencing and appointment scheduling, the pricing page directs prospective customers to contact the team. A useful cost assessment should also account for constraint fit, integration work, support needs, and how stop usage and balances are managed.
When self-serve may not be enough
Kardinal presents its Factory offering as the supported route for work that needs custom modeling or more implementation help. The company describes three options: specify and benchmark a nonstandard problem using the customer’s data; deploy the API alongside the customer team with data mapping, integration, and constraint tuning; or build a complete application on Kardinal. Its page suggests Factory for teams without sufficient engineering capacity or optimization expertise, or where the problem does not fit a standard model. Kardinal Factory
| Approach | Best fit | What the team should expect |
|---|---|---|
| Self-serve API | The operation can be represented with the API’s standard constraints, and the team can implement and operate the integration. | The team owns data mapping, application integration, and deciding how route plans enter dispatch. |
| Factory support | The problem needs custom modeling, deployment help, or a full application; or the team lacks the expertise or capacity to deliver it alone. | Kardinal describes support ranging from custom specifications and benchmarking to integration work or application development. |
Kardinal says route optimization is self-serve today, while other products in its catalog are delivered through Factory support. The decision is therefore not just API versus service: it is whether your constraints fit the standard model, how much implementation your team can own, and what support is necessary to reach production.
Quick Recap
Questions to settle before integrating
- Can the API express the constraints that materially affect your routes, including their combinations and priorities?
- Can your source systems provide the vehicle, stop, and operational data in a consistent form?
- Do the available documentation and SDKs suit your team’s languages and development workflow?
- Who will validate generated routes against field conditions and handle exceptions?
- What are the current commercial terms for your usage, and how will you monitor balances and prevent unwanted top-ups?
- Would custom modeling or guided deployment be needed to get from a working request to a production-ready operation?
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
