Route optimization APIs compared

Search for “route optimization API” today and you find two fundamentally different things mixed together. Routing engines that calculate how to get from A to B. And optimization APIs that decide which vehicle visits which stops, in which order. These are different problems, and the tools that solve them are not interchangeable.

Every routing API solves the textbook TSP. The question is what happens when your problem isn’t a textbook problem. Constraint depth separates APIs that demo well from APIs that survive production.

This guide is for the developer or technical lead evaluating which route optimization API to integrate. We compare seven options across the spectrum, from open-source constraint-solver libraries to full-stack commercial platforms, and evaluate each on the dimensions that matter once you’re past the hello-world stage. For the operational buyer’s perspective on the same market, see our route optimization platforms comparison.

Prices drift; verify before shortlisting.

Routing Engine vs Optimization API

This distinction matters more than any feature comparison. Get it wrong and you’ll either build a solver you didn’t need to build or pay for one you could have avoided.

Routing engines solve point-to-point navigation. Given an origin and a destination, return a road-network path with distance, duration, and turn-by-turn instructions. Open-source routing engines are excellent at this. They handle travel-time matrices, isochrone calculations, and multi-modal directions. What they do not do is decide which vehicle should visit which stops, in what order, subject to time windows, capacity limits, driver schedules, and forty other constraints.

Optimization APIs solve vehicle routing problems (VRP). Given a set of orders with locations, time windows, and constraints, plus a set of vehicles with capacities, depots, and working hours, return an assignment of orders to vehicles with optimized stop sequences. This is a different computational problem entirely. It consumes routing-engine output (travel-time matrices) as an input, but the optimization logic sits on top.

If you only need travel-time calculations between points, use a routing engine. If you need to decide who goes where, you need an optimization layer. Some platforms bundle both. Others require you to bring your own routing engine and plug it into their optimization API. Know which piece you’re buying before you evaluate.

For a deeper look at how VRPTW optimization works under the hood, see our complete guide to AI-powered route optimization.

API-by-API Comparison

Mycelium

Best for. Teams that need production-grade VRPTW optimization across multiple transport verticals with dispatch automation, dynamic pricing, and white-label capability, delivered as a REST API.

Mycelium’s API exposes the full platform as REST endpoints. Route optimization, order management, automated dispatch, dynamic pricing, fleet management, geocoding, and distance matrices. The VRPTW solver handles dozens of simultaneous constraints including multiple time windows per stop, multi-depot operations, mixed pickup and delivery, territory optimization, driver shift rules, vehicle dimension restrictions, balanced route distribution, and rules-driven soft constraints. Hundreds of orders return optimized routes in seconds.

Beyond the solver, the API covers the complete dispatch lifecycle. Orders flow in, the engine optimizes, routes auto-dispatch to drivers or carriers based on rule-driven dispatch logic, all parties receive notifications, and exceptions trigger dynamic re-optimization. This is the key architectural difference from solver-only APIs. You don’t just get a route plan back. You get a running operation.

The API handles synchronous optimization for small batches and queue-backed asynchronous processing for large-scale jobs. Authentication is OAuth2 with JWT, per-tenant service accounts, and per-user scopes. The platform is multi-tenant, ISO/IEC 27001:2022 certified, and white-label ready. In production since 2015 across airline crew transport, grocery delivery, corporate commute, field service, and five additional verticals.

Limitations. Enterprise-focused with no self-service sandbox signup. API access requires a conversation with the team. Not the right fit for a hobby project or a hackathon prototype.

Nextmv

Best for. Engineering teams that want to build and version custom optimization models with managed infrastructure.

Nextmv is a decision-science platform, not a turnkey routing API. It provides SDKs (Go, Python) and a cloud runtime for creating, testing, deploying, and A/B-testing custom vehicle routing models. You define your own objective function, constraints, and solver configuration. Nextmv manages the infrastructure, versioning, and experimentation layer.

The platform supports standard VRP variants (CVRP, CVRPTW, PDPTW) and lets you compose custom models by combining routing with knapsack, scheduling, and other optimization primitives. Shadow mode lets you run a new model against production traffic before switching over.

Limitations. Nextmv is a developer tool, not an operational platform. There is no built-in dispatcher, no consumer-facing apps, no driver management UI, and no out-of-the-box notifications. You get a managed solver runtime and you build the rest. The constraint library is whatever your team models from scratch. Organizations without a dedicated optimization engineering team should look at turnkey solutions.

Routific API

Best for. Small delivery operations that need a simple, well-documented routing API for planned next-day routes.

Routific offers a developer-friendly Route Optimization API alongside its fleet management product. The API accepts a set of visits (with time windows and service durations) and a set of vehicles (with shift times and capacities), and returns optimized routes with ETAs. The API is clean, well-documented, and produces realistic ETAs that dispatchers can trust without heavy manual cleanup.

Routific provides 100 free API calls per month with no time limit, making it easy to prototype against. Paid plans start at $33 per vehicle per month (billed yearly). The API handles standard delivery constraints well and is popular among meal delivery, florists, and subscription-box businesses.

Limitations. The constraint set is limited to standard delivery scenarios. No multi-depot optimization, no mixed pickup-and-delivery routing, no territory constraints, no driver-vehicle pairing rules. No dispatch automation, no dynamic pricing, no white-label capability. The API is designed for planned routes, not dynamic or real-time dispatch. Not suited for passenger transport, crew scheduling, or complex enterprise scenarios where constraint density is high.

OptimoRoute API

Best for. Mid-size delivery teams that want route optimization with driver schedule planning up to five weeks out.

OptimoRoute provides a REST API that exposes its core route optimization and scheduling capabilities. The standout feature is multi-week planning that factors in driver work hours, overtime, break times, and availability patterns. The API handles time windows, capacity constraints, and balanced workload distribution across drivers.

Pricing starts at approximately $39 per driver per month. A 30-day trial (limited to 250 stops) lets you test integration before committing. Customer support scores are high (9.7/10 on review platforms), and the API documentation is solid for the capabilities it covers.

Limitations. The API is primarily a route planning interface, not a full optimization-and-dispatch platform. It lacks autonomous dispatching, dynamic pricing, and white-label capabilities. Per-driver pricing gets expensive for larger or seasonal fleets. The constraint library is focused on delivery and does not cover passenger transport, crew scheduling, or multi-vertical scenarios. API access is available but the platform is UI-first in its design philosophy.

Route4Me API

Best for. High-volume last-mile operations that want modular, add-on-driven route optimization via API.

Route4Me offers a comprehensive set of REST APIs that expose its route optimization, geocoding, territory management, telematics, and order management capabilities. The platform has processed over 30 million optimized routes. Its modular architecture means you can call individual API endpoints for specific functions (optimization, address book, activity feed, notes) and combine them to build a custom workflow.

The API documentation covers a wide surface area and supports multiple programming languages. Route4Me also provides address-level geocoding, territory polygon management, and route analytics via API.

Limitations. ETA accuracy has been reported as unreliable, with journey times routinely underestimated, which is a significant problem if your integration depends on accurate arrival predictions. The basic plan optimizes one route at a time, not across the full fleet simultaneously. Modular pricing means total cost escalates as you add capabilities that other APIs include by default. Pricing is not publicly available.

OptaPlanner

Best for. Java teams that want full control over their constraint solver with no vendor dependency.

OptaPlanner (now part of the Timefold Solver ecosystem) is an open-source constraint-satisfaction solver written in Java. It solves vehicle routing alongside other optimization problems like employee scheduling, bin packing, and task assignment. You define your domain model as Java classes, express constraints in Java or Drools, and OptaPlanner searches for optimal solutions using metaheuristic algorithms (construction heuristics, local search, tabu search, simulated annealing).

The solver handles CVRPTW out of the box with example projects. Because you write the constraint logic yourself, you can model arbitrarily complex business rules. There is no constraint ceiling, only development time. The project has strong documentation, an active community, and a commercial support option through Timefold.

Limitations. OptaPlanner is a solver library, not an API. There is no REST endpoint, no hosted service, no dispatch layer, no fleet UI, no notifications, and no operational tooling. You build and host everything yourself. Performance tuning requires understanding metaheuristic configuration. You also need to bring your own routing engine for travel-time calculations. The time from “add dependency” to “production-quality VRP solver” is measured in months, not days, because you are building the entire constraint-modeling layer from scratch.

Locus.sh

Best for. Enterprise logistics teams that need AI-driven route optimization with real-time control-tower capabilities, accessed via API.

Locus.sh provides a comprehensive logistics optimization platform with API access to its Fireworks routing engine. The engine plans against 250+ real-world constraints and applies a decision-intelligence loop that evaluates route plans against live operational inputs. The platform includes auto-dispatch, a real-time control tower for active shipment monitoring, and integrations with ERP and WMS systems.

Locus reports significant cost reductions for enterprise customers and handles dynamic route re-optimization when disruptions occur during execution. The API supports both planning and execution-time operations.

Limitations. Focused on delivery and logistics. The API is not designed for passenger transport, corporate commute, crew scheduling, or multi-vertical operations. Enterprise-only pricing with no published rates and no self-service tier. The platform’s breadth may be more than mid-market integrations require.

Comparison Summary

Feature checkboxes are useless for API comparisons because every vendor checks the same boxes on their own docs page. The table below shows the dimensions that hold up once you’re past the getting-started guide and into production integration.

What Developers Don’t Realize

The hard part of building a route optimization API is not the solver. Open-source solver libraries handle textbook VRPTW reasonably well. A well-configured OptaPlanner model will solve a 200-stop capacitated vehicle routing problem with time windows. So will a dozen academic solvers you can find on GitHub. The solver is not the bottleneck.

The hard part is the constraint-modeling layer that sits between your business logic and the solver. The part that translates “this driver can’t work past 8pm on Thursdays but can on Fridays if given a comp day” or “these two deliveries must share a vehicle but the fragile one loads last” or “airline crew rest regulations changed this quarter and now minimum layover is 11 hours instead of 10” into solver-compatible constraints. That layer accumulates over years, one operational edge case at a time, across every vertical and every customer that runs in production.

Developers who choose a build-your-own path expecting to ship in a sprint discover this around month three. The solver works. The demo looks great. Then the first real customer brings a constraint the solver doesn’t model natively, and the team spends two weeks wiring it in. Then the next customer brings three more. This is not a bug in the plan. It is the plan. The question is whether you want to spend your engineering time building and maintaining a constraint library or whether you want to call an API that already has one.

Commercial APIs like Mycelium ship with a decade of accumulated constraints across nine verticals. Self-build paths ship with whatever your team models from scratch. Both are legitimate choices for different teams. But the build-vs-buy math should include the constraint-modeling layer, not just the solver. The solver is the easy part.

Build vs Buy

There is no universally correct answer here. The right choice depends on your team, your problem, and your timeline.

Build with a solver library (OptaPlanner, Nextmv) when you have a dedicated optimization engineering team, your problem is novel enough that no commercial API models it well, and you want full control over solver configuration and the objective function. Accept that time-to-production is months, not days, and that you are taking ownership of the constraint library for the life of the product.

Buy a delivery-focused API (Routific, OptimoRoute, Route4Me) when you have a standard delivery routing problem with straightforward constraints, you want fast integration with good documentation, and your operation fits within a single vertical. These APIs are well-suited for planned routes and small-to-mid-size fleets where the constraint set doesn’t extend far beyond time windows and capacity.

Buy a full-stack platform API (Mycelium, Locus.sh) when you need constraint depth across multiple verticals, dispatch automation beyond just route planning, dynamic pricing, white-label deployment, or the complete order-to-delivery lifecycle managed via API. The integration is heavier than a lightweight routing API, but you get an operational platform, not just a solver endpoint.

A common pattern is to start with a lightweight API for an MVP, prove the business case, and migrate to a full-stack platform when constraint complexity outgrows the original choice. Plan for that migration from day one by keeping your optimization layer behind an internal abstraction rather than coupling your application directly to a specific vendor’s API shape.

Integration Patterns

Developers typically integrate route optimization APIs in one of three ways, and the pattern you choose determines how much of the operational stack you own versus delegate.

Optimization as a service. Send orders and vehicles, get optimized routes back. Your application handles the UI, workflow, and execution. The API handles the math. This is the simplest integration and works well with any of the APIs compared here. Most teams start here.

Full-lifecycle automation. Use the API to manage orders, optimize routes, dispatch to drivers or carriers, track execution, and handle exceptions. This pattern replaces manual dispatch operations entirely but requires a platform API that covers the full lifecycle, not just the optimization step. Mycelium and Locus.sh support this pattern. Solver-only tools do not.

Headless white-label. Embed the optimization engine and operational tooling under your own brand. Your customers interact with your interface. The API runs behind the scenes. This pattern is common for logistics platforms, fleet-management vendors, and ride-hailing companies that want to offer optimization without building it. It requires a multi-tenant, white-label-ready API. See the Mycelium developers page for how this works in practice.

How to Evaluate

Feature comparisons on vendor websites are not useful for API evaluation. Every vendor checks the same boxes. Instead, run this test.

1. Take a real dataset from your operation. Not a demo dataset. Real orders, real vehicles, real constraints.
2. Submit it to each API on your shortlist.
3. Compare the output against what your dispatchers actually did with the same orders.
4. Note which constraints each API handled natively, which required workarounds, and which it could not model at all.

The API that handles the most constraints natively, without workarounds, is the one that will cause the least pain in production. The one that demos best on a synthetic 20-stop problem is not necessarily the one that survives contact with your actual operation.