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The optimization-only pattern is for partners who already have everything except the algorithm itself: route geometry, a curated station network, current prices, and computed distances. You call a single endpoint, /v1/optimize, with all the data prepared from your systems.

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Who This Is For

This pattern suits partners with mature infrastructure:
  • Fleet management platforms with built-in routing
  • Logistics companies with proprietary route optimization layers
  • Telematics providers who already track vehicle positions and station detours
  • Anyone integrating fuel-cost optimization into an existing routing product
If you have routing, station data, prices, and the ability to compute station detours, this is the simplest possible integration: one API call per route.

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Architecture

1

Your system computes the route

Using your existing routing infrastructure, compute the driving route between origin and destination.
2

Your system selects candidate stations

From your station database (fleet card network, partner network, or proprietary list), filter stations near the route.
3

Your system attaches prices and distances

Add current price per gallon to each station from your pricing data source. Compute miles_from_route_start and detour_minutes for each station using your routing system.
4

POST /v1/optimize

Send the prepared data to the optimizer. Receive the cost-optimal fueling plan.

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Data You Provide

For each /v1/optimize call, you provide:
  • Route: total distance, total duration, start/end coordinates, and (optionally) polyline geometry
  • Stations: a list of candidates with id, name, location, price_per_gallon, fuel_type, miles_from_route_start, and detour_minutes
  • Vehicle: either direct specs (mpg, tank_capacity_gallons, current_fuel_gallons) or a year/make/model lookup

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Example Request

import requests

result = requests.post('https://api.nozlerouting.com/v1/optimize',
    headers={'X-API-Key': 'YOUR_API_KEY'},
    json={
        'route': {
            'total_distance_miles': 1287.4,
            'total_duration_minutes': 1124.8,
            'start': {'lat': 32.7767, 'lng': -96.7970},
            'end':   {'lat': 33.7490, 'lng': -84.3880},
        },
        'stations': [
            {
                'id': 'WEX_45821',
                'name': 'Pilot Travel Center #234',
                'location': {'lat': 32.9, 'lng': -94.1},
                'price_per_gallon': 4.12,
                'fuel_type': 'diesel',
                'miles_from_route_start': 187.3,
                'detour_minutes': 2.1,
            },
            # ... more stations
        ],
        'vehicle': {
            'mpg': 6.5,
            'tank_capacity_gallons': 240.0,
            'current_fuel_gallons': 40.0,
        },
    },
).json()

print(f"Optimal stops: {len(result['stops'])}")
print(f"Savings: ${result['summary']['baseline_comparison']['savings_dollars']:.2f}")

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Performance

A single /v1/optimize call typically completes in 500ms-2s depending on the number of stations and route length. Because you’re skipping the routing and enrichment endpoints, end-to-end latency is the lowest of any integration pattern.

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Advantages

Minimal Latency

One API call per route. No round trips for data your systems already have.

Full Data Control

Your station network, your prices, your routing — all stay in your stack.

Easy to Adopt

No need to migrate routing or station data. Slot the optimizer into your existing pipeline.

Predictable Costs

Lower per-request rate limits aren’t an issue when you only call one endpoint.

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When to Choose a Different Pattern

This pattern assumes you have all the input data ready. Consider another pattern if:
  • You don’t have routing infrastructure → use Full Integration, which includes /v1/routes
  • You have station data but not detour distances → use Station Network Integration, which includes /v1/stations/enrich-distances

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Next Steps

API Reference: /v1/optimize

Full endpoint reference including all request and response fields

Try the Demo

See the optimization endpoint in action with synthetic data