Review of NextBillion.ai, Supply Chain Planning Software Vendor

By Léon Levinas-Ménard

Last updated: April, 2025

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NextBillion.ai, founded in 2020 by a team including Gaurav Bubna, Ajay Bulusu, and Shaolin Zheng, positions itself as an API‑first platform in location technology, specializing in route planning, mapping, and logistics optimization for complex supply chain challenges. The company offers a suite of APIs and SDKs that allow enterprises to generate multi‑stop delivery routes under diverse real‑world constraints (such as time windows, vehicle capacity, and even custom distance/duration matrices12), compute large distance matrices (up to 5000×5000 elements) for accurate ETA estimates and driving distances3, and deploy custom, editable maps with live tracking, geofencing, and dispatch capabilities. Designed with a modular architecture, NextBillion.ai’s solution accepts detailed input on vehicles, jobs, and locations, processes constraints reminiscent of classical operations research techniques, and supports flexible deployment options – from multi‑tenant clouds to private or even on‑premise installations—ensuring seamless integration with existing ERP and fleet management systems4. Although the company frequently touts machine learning and AI enhancements in its blog posts56, a closer examination of its documentation suggests that its intelligent routing decisions lean on well‑established optimization methods augmented with selective ML improvements, urging prospective users to rigorously test the system under their specific conditions.

Product Overview

What It Delivers

NextBillion.ai offers a comprehensive suite of tools including:

  • Route Optimization: Generates multi‑stop delivery routes while considering numerous real‑world constraints (e.g., time windows, vehicle capacity, driver skills) as demonstrated in their API tutorials1.
  • Distance Matrix Calculations: Computes extensive matrices—up to 5000×5000 elements—for precise estimations of ETAs and inter‑location distances3.
  • Mapping & Navigation: Provides custom, editable maps integrated with features like live tracking and geofencing, supporting advanced logistics visualization.
  • Dispatch and Field Management: Supplements the core routing API with solutions designed specifically for fleet management and dispatch operations.

How It Works

The platform is built around a modular architecture where various APIs interlock to solve classical Vehicle Routing Problem (VRP) variants. It accepts detailed input objects—covering vehicles, jobs (or shipments), and locations—and processes complex constraints (including time windows, capacity limits, and even custom cost matrices) to deliver optimized routes. The solution’s emphasis on configurability allows users to provide bespoke data (for example, custom distance/duration matrices) and to choose from diverse deployment models, whether on the cloud, private cloud, or on‑premise4. This flexibility ensures that enterprises can integrate NextBillion.ai seamlessly with existing ERP, telematics, or fleet management systems.

Technological Underpinnings

Algorithmic and Optimization Techniques

NextBillion.ai leverages a range of classical optimization heuristics to solve VRP challenges. Its APIs are designed to manage detailed constraints and enable custom objectives, ensuring robust performance in complex settings. While the company promotes the use of machine learning to enable real‑time data adaptation and predictive adjustments56, a scrutiny of the technical documentation reveals that the “intelligence” behind the routing decisions primarily relies on established operations research methods—augmented, rather than replaced, by incremental ML enhancements.

Deployment Flexibility and Scalability

A key strength of the platform is its deployment versatility. NextBillion.ai supports multi‑tenant cloud, private cloud, and on‑premise deployment options, catering to sectors with stringent data security and compliance requirements4. Its API‑first approach, combined with modular integration capabilities, also ensures scalability and smooth interoperability with legacy systems, although the promise of highly customizable solutions does demand significant configuration and continuous technical expertise.

Pricing Model and Business Claims

NextBillion.ai employs a flexible, value‑based pricing strategy that can be tailored according to per‑order, per‑asset, or per‑API call usage7. While the pricing model appears transparent and adjustable, the company’s heavy use of buzzwords such as “AI” and “advanced optimization” should be approached with healthy skepticism. The technical documentation indicates that its core routing engine is underpinned by classical optimization techniques, with machine learning playing a supplementary role. This reliance on established methodologies, while ensuring robustness, may also introduce complexities in implementation and integration that prospective customers must carefully validate against their operational needs.

NextBillion.ai vs Lokad

NextBillion.ai and Lokad both address challenges in the supply chain space, yet their focuses diverge significantly. NextBillion.ai is primarily an API‑first, location‑based platform dedicated to route planning, mapping, and logistics optimization. It excels at solving the Vehicle Routing Problem with flexible deployment options—including on‑premise models that appeal to organizations with strict data governance requirements. In contrast, Lokad—founded in 2008 and headquartered in Paris—emphasizes a comprehensive, quantitative supply chain optimization approach. Lokad’s proprietary platform leverages a custom domain‑specific language (Envision) and sophisticated techniques such as probabilistic forecasting, deep learning, and differentiable programming to drive decisions across inventory, production, and pricing8910. Whereas NextBillion.ai targets the optimization of physical routes and mapping data using classical OR methodologies enhanced with selective ML components, Lokad offers an end‑to‑end solution for predictive supply chain management that automates complex, multi‑step decisions within a cloud‑only, tightly integrated system. Ultimately, while both platforms deliver data‑driven insights, NextBillion.ai provides a specialized solution for routing and location intelligence, whereas Lokad delivers a broader, more holistic optimization engine for supply chain decision‑making.

Conclusion

NextBillion.ai delivers a robust and customizable solution for route planning, mapping, and logistics optimization that effectively addresses real‑world constraints in complex supply chain operations. Its API‑first, modular architecture and flexible deployment options make it an attractive option for enterprises needing seamless integration with existing systems. However, despite the company’s frequent appeals to cutting‑edge AI, a detailed reading of its technical documentation suggests that its core engine is founded on classical optimization techniques enhanced by incremental machine learning improvements. Enterprises considering NextBillion.ai should be prepared to invest in thorough integration and continuous configuration to fully leverage its capabilities—a commitment that contrasts with more comprehensive, cloud‑only offerings like Lokad’s platform for end‑to‑end supply chain optimization.

Sources

  1. Basic Route Optimization Tutorial ↩︎ ↩︎

  2. Custom Duration & Distance Matrix Tutorial ↩︎

  3. Solutions – Logistics ↩︎ ↩︎

  4. On-Premise Deployment ↩︎ ↩︎ ↩︎

  5. Machine Learning in Route Optimization Algorithms ↩︎ ↩︎

  6. Using AI for Dynamic Route Optimization ↩︎ ↩︎

  7. Pricing Page ↩︎

  8. Naked Forecasts Considered Harmful ↩︎

  9. The Lokad Platform ↩︎

  10. Forecasting via Deep Learning (2018) ↩︎