AI companies have huge operating costs. A TechCrunch article stated that the AI gross margins are lower due to heavy cloud infrastructure usage and ongoing human support. AI startups have lesser gross margins compared to traditional SaaS companies.
The computational demands of continually training AI models make it expensive for providers to operate. Hence, we're seeing leading AI services establish high price points for their services.
From Microsoft's $20 per month Copilot Pro to Google's new $20 consumer offering with the latest AI features.
OpenAI's surging revenue, amid ChatGPT's popularity, implies interest in AI is converting into paid usage. Rather than hampering tech providers, AI cost structures seem to unlock new, lucrative growth avenues. So while AI may burn through cash operationally, its value propositions could drive additive expansion.
But how can you stop burning cash operationally, well the answer lies in your pricing! AI companies need to price for the value they provide. Boxing their pricing in a traditional tier based model doesn’t work!
Unlike traditional SaaS, the AI industry needs to bill its customers differently! Not just that, with traditional SaaS companies bringing in AI features, pricing also needs to evolve. That’s why we are writing the features you need in your billing software because, in the end, your billing software is the backbone where you implement all your pricing strategies.
Top Features that Your AI Billing Software Needs
1. Billable Items
Billable items form the building blocks of a pricing model tailored for AI companies. They allow configuring flexible plans that align with specific business needs. For AI providers, key billables fall into three main categories:
Usage Metrics:
These track direct consumption of AI capabilities - for example, the number of text prompts processed by a language model, or images generated by an image creation tool. Monitoring real-time usage provides insights into customer demand patterns.
For an AI startup like Anthropic dealing with computational limits, usage metrics could involve API calls to Claude models, text character limits, or computer vision inference counts. Tracking these volumes is crucial.
Add-Ons:
These represent fixed charges for supplementary services like custom model training, data labeling, ML ops, or specialized deployments. Add-ons enable monetizing ancillary tasks around core AI offerings.
Add-ons allow AI companies to charge for supplemental services beyond core model access. These could include custom model training, data labeling, ML engineering support, or specialized deployments. One-time setup fees, recurring managed services charges, and consumption-based add-ons provide flexibility.
For example, an AI startup could bill a one-time fee for initially integrating their API, then ongoing support fees monthly. Add-ons become crucial revenue streams alongside basic model usage charges.
Service Tiers:
Tiered plans with differing access levels to AI models and features. For instance, Claude could have a "Basic" tier with limited prompts/month, an "Essential" tier with more quotas, and a "Pro" tier for unrestricted access. Tiers facilitate upselling customers to expanded capabilities.
Tiered plans with different capabilities cater to diverse customers. For instance, GPT could have a "Basic" tier with limited monthly prompts, a "Pro" tier with higher quotas, and an "Enterprise" with full, unmetered access.
If startups hit prompt limits, they could pay-as-they-go for added batches beyond their tier rather than upgrading plans. Usage alerts trigger upsell opportunities. Over time, demand trends would reveal which advanced features to bundle into lower tiers.
Metered plans lock in baseline revenue, while granular overage charges or feature add-ons maximize monetization. This balanced approach suits AI companies drawing high computing costs to serve expanding customer needs. Configuring an array of billables via flexible billing software allows realizing this Hybrid Transactional/Subscription model.
2. Prepaid and Postpaid Invoicing:
Traditional billing systems require upfront subscriptions, yet enterprises often prefer postpaid models. Prepayment also restricts AI companies to seat-based licenses, lacking support for usage or hybrid pricing crucial to monetizing AI. Without postpaid capabilities, providers face manual invoices every period across multiple metrics.
This rapidly becomes chaotic operationally. With dozens of customers, combining prepaid and postpaid charges on separate statements compounds confusion in tracking receivables and revenue.
AI businesses need consolidated billing visibility, flexibly generating invoices for any period. Whether prepaid, postpaid, subscription, consumption, or hybrid models, a single solution should handle any billing scenario. Togai, the most reliable Usage based billing software provides one ledger per customer encompassing all transaction types - usage, subscriptions, and adjustments in one place.
This allows seamless shifts between prepay, postpay, and blended models without revenue leakage or reconciliation headaches.
One robust platform streamlining prepaid and postpaid invoicing, usage pricing, subscriptions, and hybrid models solves major billing pain points for AI companies.
Consolidated views of complex commercial relationships reduce friction while enabling specialized monetization frameworks attuned to AI economics. Togai facilitates this by uniting flexible billing configurations with integrated financial reporting for AI businesses.
3. In-house Usage Monitoring Capabilities
AI businesses need billing systems that natively integrate usage monitoring, not third-party bolt-ons. Tracking consumption in external tools and then manually compiling invoice details is operationally taxing. Rather, purpose-built AI billing platforms directly ingest metrics like model prompts, compute jobs, and API calls to trigger automated usage-based charges.
With real-time visibility into customer consumption patterns, AI providers also gain a strategic analytics asset beyond invoicing automation.
Consumption dashboards spotlight opportunities to optimize service tiers, fine-tune pricing, and react to demand swings. Usage statistics inform product feature development and sales initiatives targeting the most lucrative customer personas.
Robust data integrations are equally vital for reliable metrics. AI billing solutions should flexibly ingest usage values from diverse sources - whether company instrumentation, cloud monitors, or data pipelines. Ongoing usage feed reconciliation confirms accuracy before invoice generation.
Without integrated monitoring and flexible data ingestion, AI business models falter. Conversely, purpose-built billing systems with out-of-the-box analytics empower AI startups to generate recurring revenue aligned tightly to actual usage. This creates sustainable businesses that scale pricing to AI resource utilization and deliver exceptional customer value.
Another important thing to remember is that your tool should allow you to fetch this data easily - be it integrations, bulk upload CSV values, or anything else that you might have.
Also Read: AI Billing Platform: How Togai Powered Flexible Billing for an AI Copywriting Company
4. Overrides and Overages
AI businesses need billing agility to provide real-time discounts, overage allowances, and custom deals without added paperwork. Manual price overrides and usage limit increases become unscalable. Rather, AI providers need automated preferential pricing configurations per customer.
For example, an AI writing tool could incentivize startups to upgrade to more robust API tiers by including first-month discounts or free overage prompts. Enterprises might negotiate one-off bundles blending usage allotments, special professional services, and discounted rates.
The ideal AI billing platform enables precise preferential pricing policies across any billing dimension - usage volumes, subscriptions, and add-ons - without deviating from standardized plans. Custom price contracts then integrate seamlessly without manual exception processing.
This flexibility allows AI companies to incentivize specific customer behaviors, whether rewarding loyalty, spurring the adoption of new capabilities, or enticing high-value segments with preferential access. Automated preferential pricing also reduces friction in closing customized enterprise deals.
5. Entitlements
Entitlement management enables AI companies to create tiered self-serve plans, crucial for sector growth. Gatekeeping certain model capabilities or prompt allowances behind payment unlocks new monetization avenues. Yet manually restricting access handicaps scaling.
Robust billing platforms therefore automate entitlements tied to customized payment rules. For example, GPT could offer a discounted "Startup" tier with basic NLP features, reserving advanced capabilities for higher-priced plans.
New signups self-provision their chosen tier including prompt allotments, and models, and pay predetermined rates automatically.
Entitlements facilitate frictionless packaging of AI modular services into good/better/best bundles at varying price points.
This streamlines selling to diverse customer maturity levels. Companies also future-proof revenue growth with entitlement support as new model versions or supplementary services are commercialized.
Rather than divert engineering resources into complex in-house entitlement infrastructure, AI startups should leverage flexible billing tools with out-of-the-box capabilities. Entitlement management, prepaid credits, and self-serve commerce are handled natively so teams stay focused on core AI competencies.
6. Revenue Simulator
Monetizing AI models involves complex pricing tradeoffs between profitability, customer acquisition, and retention. Without historical consumption data, determining optimal per-use rates is guesswork. Togai's revenue simulator addresses this uncertainty.
Togai’s revenue simulator ingests actual customer usage logs - text prompts processed, compute jobs run, API calls - then runs simulations comparing business outcomes of alternate pricing models. Rates and tiers can be adjusted to balance revenue expansion versus customer saturation and churn risk.
For an AI startup launching new usage-based services, the simulator quantifies market receptiveness to different metering and tiered plans based on comparable data. This yields science-based, outcomes-optimized price configuration even without established customer pools.
Meanwhile, scaling AI companies can feed live usage statistics into simulations for continuous optimization. As new capabilities launch, the tool calibrates pricing to customer subtype consumption patterns for maximized recurring value.
Conclusion
The AI sector shows enormous promise, but profitability remains a major challenge given the computational demands of developing and running AI models. Monetization innovation is imperative if AI companies hope to sustain growth.
Leveraging flexible, purpose-built billing technology provides a pathway to recurring revenue aligned tightly to customer usage and value. With integrated monitoring, tailored pricing controls, and financial modeling support, AI providers can overcome margin pressures from swelling infrastructure expenses.
Automation around metering, billing workflows, and financial analytics are no longer “nice-to-haves” but fundamental pillars empowering the next generation of AI businesses.
By taking control of monetization infrastructure, AI startups can focus engineering and commercial resources on differentiating capabilities. Specialized billing technology tailor-made for AI economics unlocks this strategic advantage.
