How to Choose a Route Optimization API: Matching Constraint Coverage to Fleet Complexity

A Route Optimization API is not just a routing API with a better sales page. That distinction matters before any pricing table does. A routing API calculates a path between waypoints that have already been chosen; a route optimization API decides stop order, vehicle assignment, and often whether the plan is even feasible under constraints such as capacity, time windows, skills, shifts, truck rules, or hazardous-material restrictions. Geoapify states the line cleanly: routing is about paths between predefined points, while route optimization solves Vehicle Routing Problems by choosing sequences and assignments across stops and vehicles.[1]

That is why “cheapest API call” is a bad first filter. It can work when the operation is small, mostly homogeneous, and tolerant of manual exceptions. It breaks down when dispatchers are juggling vehicle dimensions, driver hours, incompatible goods, depot rules, pickup-and-delivery precedence, or contractual time windows. In those fleets, the question is not whether an API can draw a shorter line on a map. It is whether the optimizer can represent the mess the operation already lives with.

The most useful public comparison available here is Kardinal’s 2025 benchmark of route optimization APIs. It is not a universal performance benchmark. It measures constraint coverage from public API documentation, not route quality in production, solve speed, driver acceptance, or how painful the integration becomes after the proof of concept. Kardinal is also a competing vendor, so the usual caution applies. Still, the methodology is explicit enough to make the data useful as a shortlisting tool, especially because it exposes how wide the functional spread is: from roughly 40% coverage for Routific to 70% for Google and 69% for NextBillion.ai in the benchmark set.[2]

The comparison that matters first

A useful shortlist has to put three things next to each other: what constraints the API can express, how the vendor charges, and where the system can run. Looking at only one of those columns is how teams end up with a neat demo and an ugly deployment.

The table is deliberately blunt. A 70% score does not mean Google will produce a better route than a 64% vendor in every operation. It means Google’s public documentation covered more of Kardinal’s tested constraint set. That is a different claim, and it is the one buyers should keep in view when they move from shortlist to proof of concept.

If the fleet is simple, do not overbuy the optimizer

At the lighter end of the market, a route optimization project often means daily delivery sequencing, basic capacity checks, driver visibility, and a bill that does not shock a small operations team. Routific and Geoapify belong in that conversation before the heavier enterprise platforms do.

Routific’s benchmark position is easy to misread. Its 40% constraint coverage is not an indictment if the buyer’s operation does not need the missing complexity. The product is aimed at simpler last-mile scenarios, and its pricing reflects that lane: a $150–$1,500/month Pro tier and $0.05/visit Enterprise pricing.[6] For a delivery operation with standard vans, ordinary parcel constraints, and a dispatcher who mainly needs better stop order and driver execution, that can be a sensible fit.

Geoapify is similar in the sense that it should not be dragged into an enterprise hazmat comparison just because it has a route planner API. Its draw is accessibility: 3,000 free credits/day, credit-based pricing, unmetered plans from €700/month, and support for multiple VRP variants.[7] That makes it plausible for experimentation, low-volume production, or teams whose optimization problem is real but not especially constraint-heavy.

The trap is using these entry-level economics to price an enterprise requirement. A low per-visit or free-credit starting point does not answer whether the API can model the operation’s binding constraints. If dispatchers must clean up the plan every morning because the model cannot understand the fleet, the savings moved from the invoice to the labor queue.

Google is broad and convenient, but check the constraints before the brand carries the decision

Google Route Optimization API deserves serious attention. In Kardinal’s benchmark it has the highest listed constraint coverage among the six providers here, at 70%, and is described as strong on sequencing.[2] For teams already using Google Maps Platform, that matters. Procurement is easier, developer familiarity is higher, and the surrounding mapping ecosystem can reduce integration friction.

But the same benchmark flags weaker support around truck routing and hazardous materials.[2] That is not a footnote for fleets where vehicle class, road restrictions, bridge limits, or regulated cargo determine feasibility. In those environments, a route that looks optimized but violates a truck constraint is not a route. It is an exception waiting for a dispatcher, driver, or compliance team to catch it.

Google’s pricing also needs to be read in operational units, not just API units. The available pricing data describes per-shipment billing and $100–$1,200/month subscription plans after the March 2025 Google Maps Platform pricing change.[3] Per-shipment pricing can be perfectly rational if shipments are the way the business measures work. It becomes less tidy when internal cost allocation is based on vehicles, routes, depots, contracts, or business units that do not map cleanly to shipment volume.

The deployment model is the other hard boundary. Google is cloud-only in the available comparison.[2] Many buyers can live with that. Some cannot. If data residency, customer contracts, latency architecture, or internal security policy requires on-premise deployment, Google’s convenience does not solve the procurement problem. It just postpones it.

NextBillion.ai is where the shortlist gets serious for messy fleets

NextBillion.ai sits just behind Google in Kardinal’s benchmark at 69% constraint coverage, but the shape of its offer is different. Available data notes 50+ real-world constraints, up to 10,000 orders per request, 5000x5000 distance matrices, and both cloud and on-premise availability.[2] That combination is the reason it should be evaluated by fleets whose real issue is not merely stop sequencing but operational representation.

This is the tier where proof-of-concept scripts often lie by omission. A sample run with 200 clean stops can make several APIs look similar. The differences show up when the route has mixed vehicle types, truck restrictions, pickups tied to deliveries, zones, driver rules, or customer-specific service windows. If the API cannot express those constraints natively, the engineering team starts building workaround logic around the optimizer. That workaround layer is where integrations become brittle.

NextBillion.ai’s flexible pricing model also matters. The available data lists per-vehicle, per-order, or per-call options.[2] That does not automatically make it cheaper. It makes it easier to align the commercial model with how the fleet actually runs. A carrier, a private fleet, and a field-service network may all call the same endpoint, but they may not think about cost in the same denominator.

The on-premise option is not a decoration. For some enterprise buyers, it is the difference between a vendor that can enter the final round and one that cannot. If the operation has strict data controls, sensitive customer locations, or architectural requirements that rule out a purely cloud-hosted optimization layer, deployment flexibility belongs in the first screening pass, not the last security review.

Solvice and GraphHopper pull the decision in different directions

Solvice and GraphHopper are easy to flatten into the middle of a comparison table, but they answer different questions.

Solvice shows 64% constraint coverage in Kardinal’s benchmark and supports up to 20,000 orders per request, with a noted strength in field service.[2] Its pricing is resource-based at €16/resource/month with a minimum of 10 resources.[4] That can be attractive where the operational unit is a technician, vehicle, crew, or other resource that planners already manage as capacity. The large request scale also deserves attention for buyers who need to optimize many jobs together rather than split the problem into smaller batches too early.

GraphHopper, by contrast, is interesting because of architecture and engineering control. It has 62% constraint coverage in Kardinal’s benchmark, uses an OpenStreetMap-based foundation, and has an open-source core that can be self-hosted.[2] Its listed pricing is €160/month for 15,000 credits/day.[5] For engineering teams that want more control over infrastructure, map data assumptions, or hosting, that open-source foundation may matter more than a few percentage points in a documentation-based constraint score.

The boundary is scale per request. The available comparison lists GraphHopper at a maximum of 10 vehicles per request.[2] That does not make it unsuitable for every serious use case, but it is a real constraint for buyers expecting to optimize larger fleets in a single solve. If the operation has to break a fleet into artificial chunks to fit the API, the team needs to test whether that segmentation damages route quality or simply shifts complexity into preprocessing.

Three checks before believing the cheapest quote

Once the shortlist is down to two or three vendors, the evaluation should stop treating price as a single row. The pricing model has to be tested against actual planning behavior.

• Match billing units to operating units. Per-shipment, per-vehicle, per-order, per-call, per-resource, per-visit, and per-credit pricing can produce very different outcomes for the same fleet. A high-stop-density parcel operation and a low-stop specialized transport fleet should not expect the same “cheapest” vendor.
• Test the constraints that create exceptions, not the ones every optimizer handles. Time windows and capacity are table stakes in many evaluations. Truck routing, hazmat, skills, pickup-and-delivery dependencies, depot rules, and route duration limits are where feasibility usually becomes expensive.
• Decide deployment eligibility before route-quality testing. If cloud-only deployment is disallowed, do not waste a month proving that a cloud-only API can solve routes nicely. It cannot solve the governance problem.

The Kardinal benchmark is useful here because it gives buyers a constraint map before they enter vendor demos. But it should not be treated as the final scorecard. It does not measure solve speed, production reliability, driver adherence, integration support, or actual route quality on the buyer’s data.[2] Those have to be tested with representative orders, representative vehicles, and the ugly constraints that dispatchers currently handle by hand.

A practical shortlist by fleet complexity

For many buyers, the shortlist should get smaller rather than more impressive. If the operation only needs lightweight last-mile sequencing, Routific or Geoapify may be enough. If the fleet has truck restrictions, hazmat concerns, large optimization requests, or on-premise requirements, start with NextBillion.ai and Solvice, then decide whether GraphHopper’s self-hosting profile or Google’s cloud ecosystem belongs in the final test.

The wrong Route Optimization API usually fails quietly at first. It produces a route, the demo looks plausible, and the spreadsheet says the unit cost is attractive. Then operations starts finding the constraints the model did not understand. That is why the first buying question should be simple and uncomfortable: can this API represent the routes your dispatchers are actually allowed to run?

References

1. What Is the Difference Between a Routing API and a Route Optimization API? — Geoapify
2. Benchmark of Route Optimization APIs — Kardinal, 2025
3. Google Maps Platform Pricing — Google Maps Platform
4. OnRoute — Solvice
5. Route Optimization — GraphHopper
6. Routific Developer Pricing — Routific
7. Route Planner API — Geoapify