// route_optimization_api
// why_developers_ship_with_us
Ten years of hard logistics cases, in one engine. Real constraints, real routes, not toy examples.
Typed SDKs, clear docs, forkable examples. From first call to production fast.
Pay per stop, no quote, no sales call. The full product on the free tier, test your case before you spend a cent.
MCP server, agentic docs, typed schemas. Your agent models the problem, not just the call.
// how_it_works
No credit card. The free tier is live the moment you sign up.
A sandbox key, ready to paste into the SDK or your agent.
Send vehicles, stops and constraints, get optimized routes back.
// the_old_way → deleted
Every other path to route optimization, built in-house or bought, costs you the same things: weeks, experts, and a quote. Here’s what switching looks like.
// proven_depth
Ten years of modeling depth, exposed as first-class API concepts. Toggle the constraints your operation actually has and watch the request build, this is exactly what you’d send. Every constraint, on every plan, including the free tier.
No subscription, no tiers, no sales call. Start free, then top up with prepaid packs, the price per stop drops as they grow.
Re-optimizations are free, and you only pay for the stops you actually optimize.
// free_tier
From first call to production, no payment, no sales call. 1,000 free stops a month, full sandbox, Python and JS/TS SDKs, complete docs.
The whole product, capped by volume, prove it works before you spend a cent.
// 2026_benchmark
An independent, reproducible study scored 11 route optimization engines on 149 documented business constraints across 8 sectors.
Kardinal recognized as a Representative Vendor in the 2026 Gartner® Market Guide for Vehicle Routing & Scheduling.
// trust
One engine, written in Rust, owned end to end. No wrapper around an open-source core, so we can answer for every result it returns.
A single proprietary optimization engine, built in Rust, controlled from the algorithm to the API.
Engine updates ship 1–2× a week, each validated against a non-regression benchmark. Backward-compatible for 5+ years.
Same input, same solution, every time. Fixed-seed local search, fully reproducible. No flaky outputs to debug.
Autoscaling SaaS. Fire thousands of routes in under an hour, then scale back to zero.
// quickstart
A consistent, predictable API. Errors that are clear and actionable. Typed SDKs for Python and JS/TS. Reference docs that don’t assume an operations researcher.
curl -X POST https://api.kardinal.ai/v1/optimize \ -H "Authorization: Bearer $KARDINAL_KEY" \ -H "Content-Type: application/json" \ -d @plan.json
from kardinal import Kardinal k = Kardinal(api_key=os.environ["KARDINAL_KEY"]) result = k.optimize( vehicles=vehicles, stops=stops, constraints=["time_windows", "capacity"], ) print(result.routes)
import { Kardinal } from "@kardinal/sdk"; const k = new Kardinal({ apiKey: process.env.KARDINAL_KEY }); const { routes } = await k.optimize({ vehicles, stops, constraints: ["time_windows", "capacity"], }); console.log(routes);
// agent_ready
Agents fail at route optimization because it’s a modeling problem, not a coding problem. We ship the modeling layer your agent is missing: MCP server, agentic docs, typed SDKs, skills.
$ claude mcp add kardinal -- npx @kardinal/mcp ✓ Added stdio MCP server kardinal $ claude > /mcp · kardinal · 6 tools optimize solve a routing problem validate_model check constraints before solving explain_plan why the solver chose this route docs_search modeling guides and patterns simulate what-if on a solved plan usage stops consumed this cycle
An auto-generated MCP can call an endpoint. It can’t model a pickup-and-delivery problem with legal driver breaks. The modeling layer is the product, that’s what your agent gets here and nowhere else.
// agent_ready
No sales call. No credit card. 500 free stops a month, enough to take your integration all the way to done.
Want to talk to our experts? →
// services_for_enterprises
For large-scale managed operations, Kardinal Lab models, deploys and operates optimization with your teams. A different universe, same company.
