The landscape of artificial intelligence is experiencing a significant shift. While cloud-based AI has dominated for years, the need for lower latency, enhanced privacy, and offline functionality is pushing AI inference—the process of using a trained model to make predictions—out to the edge and onto on-premise servers. This move away from centralized cloud infrastructure introduces new challenges and opportunities for pricing, requiring a departure from the familiar pay-per-token models of the cloud.

This guide explores the unique pricing strategies for edge and on-prem AI, offering a framework for developers, product managers, and founders to build sustainable revenue models in a hybrid AI world.

Edge and on-prem AI inference refer to the execution of AI models on local hardware, close to the source of data, rather than in a centralized cloud environment. This can include a wide range of devices and locations:

• On-device: Running directly on user devices like smartphones, laptops, or smart home assistants.
• Edge gateways: Deployed on dedicated hardware at the edge of a network, such as in a factory, retail store, or autonomous vehicle.
• On-premise servers: Hosted within an organization’s own data center, giving them full control over their infrastructure.

This approach minimizes latency by eliminating the round-trip to the cloud, enhances data privacy by keeping sensitive information local, and ensures continuous operation even without an internet connection.

Pricing for edge and on-prem AI must account for a different value proposition than cloud-based services. Instead of selling direct access to a cloud API, you are often selling a license to use software on hardware you don’t own or manage. This requires a shift in thinking from usage-based metrics like API calls to models that reflect the value delivered on the user’s own infrastructure.

Here are some common pricing models for edge and on-prem AI:

• Per-device or per-seat licensing: A straightforward approach where customers pay a recurring fee for each device or user running the AI software. This is simple to understand and manage, making it a good fit for applications with a clearly defined number of endpoints.
• Feature-based tiers: Similar to traditional SaaS pricing, this model offers different subscription plans with varying levels of functionality. A basic tier might offer core inference capabilities, while premium tiers could unlock advanced models, higher performance, or dedicated support.
• Hybrid models: Many applications will operate in a hybrid mode, with some processing happening on the edge and some in the cloud. Pricing models can reflect this, with a base fee for the on-prem software and a usage-based component for cloud services.
• Perpetual license with maintenance: In some enterprise scenarios, a one-time perpetual license for the software combined with an annual maintenance and support fee is still a preferred model.

The need for edge and on-prem AI pricing strategies is driven by a growing number of real-world applications:

• Industrial IoT: AI models running on factory floors for predictive maintenance and quality control, where low latency and reliability are critical.
• Autonomous vehicles: Real-time decision-making for self-driving cars, which cannot rely on a constant cloud connection.
• Healthcare: On-device analysis of medical imaging or patient data to ensure privacy and speed.
• Retail analytics: In-store cameras with on-site processing to analyze customer behavior without sending sensitive video data to the cloud.
• Smart home devices: Voice assistants and security cameras that can function without an internet connection.

Moving to an edge or on-prem pricing model is not without its challenges. Here are some common hurdles and misconceptions:

• Difficulty in metering usage: When your software is running on a customer’s device, it can be more challenging to accurately track usage for billing purposes, especially if the device is often offline.
• Value perception: Customers accustomed to paying for cloud services based on usage may be resistant to upfront licensing fees. It’s crucial to clearly communicate the value of on-prem software, including benefits like reduced latency, enhanced security, and offline capabilities.
• Complexity of hybrid models: Combining on-prem licensing with cloud usage can create complex billing scenarios that are difficult for customers to understand and for you to manage.
• Channel and distribution: Selling downloadable or installable software often involves different sales and distribution channels than a cloud-native SaaS product.

Successfully pricing your edge and on-prem AI solution requires careful planning and a deep understanding of your customers’ needs. Here are some best practices to follow:

• Align pricing with value: Ensure your pricing model reflects the unique value your solution provides. If you’re offering significant cost savings by reducing cloud spend, your pricing should reflect that.
• Offer flexible plans: Provide a range of pricing options to cater to different customer segments, from individual developers to large enterprises.
• Be transparent: Clearly explain how your pricing works, especially for hybrid models. Provide calculators or tools to help customers estimate their costs.
• Use feature flags to control access: Feature flags are an excellent way to manage different tiers of service in an on-prem environment. They allow you to enable or disable specific features based on a customer’s subscription level, without requiring a separate software build for each plan.

While Kinde is primarily a cloud-based authentication and user management platform, its powerful feature flagging and billing capabilities can be instrumental in managing complex, hybrid AI pricing models.

You can use Kinde to:

• Manage subscription plans: Kinde’s billing engine allows you to create and manage a variety of subscription plans, including flat-rate, usage-based, and tiered models. This flexibility is ideal for creating the hybrid plans often needed for edge AI solutions.
• Control features with flags: Kinde allows you to gate access to specific features based on a user’s subscription plan. This is perfect for implementing feature-based tiers in your on-prem software. Your application can fetch the user’s feature flags from Kinde upon authentication and dynamically adjust the available functionality, even in an offline or on-prem environment.

This combination of robust billing and granular feature control makes Kinde a valuable tool for any business looking to build and monetize a modern, hybrid AI application.

• Pricing models
• About feature flags