Distance matrix API costs scale with the number of origin-destination pairs calculated. A naive optimization request for 100 locations computes 100×100 = 10,000 pairs, costing $50 at standard Google Distance Matrix pricing. Run this every hour across 10 service regions, and you're spending $5,000 monthly just on distance calculations—before optimization. Smart strategies cut costs by 60-90% without sacrificing solution quality.
Cache aggressively. Travel time from location A to location B doesn't change significantly day-to-day unless traffic patterns differ dramatically. Calculate distance matrices once for your regular service area, store them in a database, and reuse them. For a service company with 500 recurring customer locations, compute the 500×500 matrix once monthly instead of daily. Update only when customers are added or road networks change. This reduces API calls from 7.5 million monthly (500×500×30 days) to 250,000 (once monthly), cutting costs by 97%.
Cluster locations before optimizing. Don't calculate distances for locations that will never be visited on the same route. If you have technicians in Seattle and Miami, don't compute Seattle-to-Miami distances—cluster locations geographically first. Split your optimization problem into regional sub-problems (Pacific Northwest, Southeast, Northeast), then optimize each region independently. This reduces the distance matrix size from N² to multiple smaller matrices summing to far less than N².
Use distance approximations for initial filtering. Haversine distance (straight-line distance between coordinates) costs nothing to calculate. Before calling distance matrix APIs, filter out obviously unsuitable pairs. If a customer is 50 miles away but your technician's route only covers 20-mile radius, skip that distance calculation. This pre-filtering can eliminate 40-60% of API calls for sparse problems.
Leverage warm-start optimization. When a new job arrives mid-day, you don't need to recalculate the entire distance matrix. A warm-start optimization API only needs distances between the new location and vehicles' current positions—not all possible pairs. Instead of recalculating 10,000 pairs, you calculate 50 pairs (one per vehicle). This reduces distance API costs by 99% for dynamic re-optimization scenarios.
Batch requests efficiently. Some distance matrix APIs charge per element but allow batch requests. Instead of making 100 separate API calls for 100 origin-destination pairs, make one batched request. This reduces HTTP overhead and often qualifies for bulk pricing tiers. Structure your optimization workflow to batch distance calculations before optimization runs.
Choose asymmetric matrices only when necessary. Travel time from A to B often differs from B to A due to one-way streets or traffic patterns. But for many suburban or rural routes, the difference is negligible. Using symmetric matrices (assuming A→B equals B→A) cuts API calls in half—calculate only the upper triangle of the matrix. For 100 locations, this reduces from 10,000 to 5,050 calculations.
Monitor and audit distance matrix usage. Many optimization platforms over-fetch distances due to inefficient algorithms. Audit which distances are actually used in final routes versus which were calculated unnecessarily. Implement request logging to identify wasteful patterns.
Practical impact: A delivery company optimizing 200 routes daily reduced distance matrix costs from $12,000 monthly to $1,800 by implementing caching, clustering, and warm-start optimization—same solution quality, 85% cost reduction.
The practical decision: Use Solvice Maps as your default distance matrix provider. It eliminates the cost disadvantage of traditional premium APIs while avoiding the operational complexity of self-hosting. Reserve self-hosting only for edge cases: extremely high volume with predictable patterns, fully offline requirements, or specialized routing rules unsupported by standard APIs. For 90% of routing applications, Solvice Maps delivers the best combination of cost, speed, accuracy, and operational simplicity.