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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Building a generative AI product today is as much an economic challenge as a technical one. With the rise of GPTs and large language models, engineers can no longer focus solely on model accuracy and latency. Every decision, whether related to architecture, cost structure, legal considerations, or business strategy, has broader implications. Engineers now need to think like economists and strategists, not just coders. This post explores why engineering teams must adopt a business-first mindset.
The Rising Cost Complexity of GenAI Development
One stark difference with generative AI features is that they introduce a significant variable cost to each user interaction. Traditional software features might scale with negligible cost per use, but each query to a large language model (LLM) incurs expense in computing power or API fees. As OpenView Venture Partners put it, “unlike other product advancements, adding generative AI has real costs for SaaS companies”.
For example, OpenAI’s popular ChatGPT API (based on GPT-3.5 Turbo) costs about $0.002 per 1,000 tokens (roughly 750 words). That may sound trivial, but it quickly adds up when you have thousands of users. In fact, developers have enjoyed a 10x cost reduction compared to earlier GPT-3 models, yet the assumption of “near $0” marginal cost no longer holds. Every AI-driven feature now has a cloud compute price tag attached.
At a large scale, these costs become impossible to ignore.
According to SemiAnalysis’ Chief Analyst Dylan Patel, ChatGPT costs about $694,444 per day to operate, with GPU costs at roughly $0.36 per conversation.
While startups may not yet match ChatGPT's volume, the lesson is clear: serving advanced AI models is expensive. Developers must now factor cost per query into their metrics alongside performance.
For example, if a model costs 10x more but only offers a slight quality improvement, is it worth it? These trade-offs reflect diminishing returns, requiring teams to rethink the assumption that new features won’t impact cost structure. In practice, generative AI often turns cloud usage into one of the largest line items on the P&L. In this new paradigm, the GenAI engineer’s role extends beyond creating functional systems, it’s also about building cost-efficient models that sustain business profitability.
Model Cost Disparities: Open-Source vs. Closed-Source
When choosing an AI model, engineers face a fork in the road: go with a third-party proprietary model (like OpenAI’s GPT series), or opt for an open-source model you can host and customize. The decision is about far more than just performance or access, it’s fundamentally about economics, risk, and control.
There’s a common assumption that open-source models are “free” and thus cheaper, while proprietary APIs are expensive. Reality is more nuanced.
Often, open-source isn’t always cheaper in the long run, especially when you factor in infrastructure and scaling costs.
A cost per million tokens comparison shows that while open-source Llama 2 carries no license fee, it can be pricier to run on your own GPUs than highly-optimized closed models like OpenAI’s GPT-3.5 Turbo. OpenAI’s premium GPT-4 (red) remains dramatically more expensive per token.
For example, in late 2023, a comparison of Meta’s Llama 2 vs. OpenAI’s GPT-3.5 Turbo revealed that some startups faced running costs that were 50% to 100% higher with Llama 2 than with GPT-3.5 Turbo. Specifically:
- In one case, the founders of chatbot startup, Cypher ran identical tests on both:
- Llama 2 on cloud: $1,200 for a specific workload.
- GPT-3.5 Turbo: $5 for the same workload.
The difference stems from OpenAI’s ability to amortize GPU costs across millions of requests, achieving better GPU utilization than a small startup can with dedicated GPUs. At a lower-to-medium scale, the pay-as-you-go model of GPT-3.5 Turbo was more cost-efficient for this startup than the "free" open-source Llama 2.
Does that mean closed-source is always cheaper to use?
Not necessarily.
The calculus changes with scale and model choice.
While OpenAI’s GPT-4 offers premium performance, it comes at a steep cost, around $0.06 per 1,000 tokens for output, which is 30x more expensive than GPT-3.5.
- GPT-4 cost: Approximately $60 per million tokens.
- For startups, this can skyrocket the cost of goods sold, forcing high prices or resulting in major losses.
By contrast, open-source models like DeepSeek R1, a "GPT-4-class" reasoning model, are much cheaper to run:
- DeepSeek R1 cost: Approximately $7 per million tokens.
- Compared to $60 per million for OpenAI’s latest model, this is a significant cost-saving.
This disparity directly impacts a startup’s pricing strategy and ability to offer competitive free tiers.
The Startup Dilemma: Open-Source vs. Closed-Source
When choosing a proprietary model like GPT-4, startups must ensure their revenue stream can cover the steep costs. Otherwise, they risk unsustainable business models. For example:
- Jasper.ai built its early product by reselling OpenAI’s GPT-3 with a UI, but the margins were thin:
- Jasper and Copy.ai both operated at a 60% gross margin, with around 40% of revenue going to OpenAI for API costs.
- This left only 20% of value for operational profits, compared to typical SaaS margins of 80-90%.
In contrast, companies like MosaicML are focusing on reducing model training and hosting costs for others:
- MosaicML: Successfully reduced the cost of training large models to $160k, which is 2.5x cheaper than traditional self-hosting. They later reduced this cost further to $125k.
Lower infrastructure costs allow startups to offer:
- Lower prices
- Higher usage limits
- More flexible go-to-market strategies, like generous free tiers.
Key Takeaways for Engineers
When selecting an AI model, it's crucial to consider the unit economics:
- Quality, cost, and scalability: A technically brilliant solution that costs more to run than it generates in revenue is unsustainable.
- The optimal solution may involve a hybrid approach:
- Use open-source models for less complex tasks.
- Utilize premium APIs only for the most difficult queries.
- Negotiate volume discounts as your startup scales.
By focusing on ROI and optimizing costs, engineers can serve more users at a lower cost, thereby expanding access and boosting long-term growth.
Bottom Line
The right choice of AI model hinges on balancing cost-efficiency with model performance. For many startups, choosing the right model might mean running a cheaper open-source model for general tasks while relying on a premium API like OpenAI’s for specialized, high-quality outputs.
Marrying Model Choice to Monetization Strategy
Picking a foundation model is now a strategic business decision as much as a technical one. The model you select impacts your pricing, margins, and scalability. Therefore, model choice and monetization strategy must be considered together, as the two are tightly linked. If the engineering team chooses a model in isolation, the product's economics could be doomed from the start. Similarly, if the business side promises “unlimited AI usage for a flat $10 fee” without consulting engineering, it could lead to unsustainable losses.
Smart teams plan both the model and monetization strategy in tandem. A prime example of this is Notion’s AI writing and editing features:
- Notion’s strategy: They introduced generative AI as an add-on with usage caps in free plans and an upsell for heavy users.
- Pricing: Users are charged $10 per month for additional AI access beyond the complimentary quota.
- Economic balance: The $10 fee likely covers the average user’s queries plus margin, aligning user behavior with economics. Only those who find value from the AI, and thus use it heavily, will pay, creating a feedback loop that maintains cost and revenue balance.
Many SaaS companies follow similar pricing models for generative AI, often incorporating a hybrid approach.i.e.,:
- Base subscription fee with a usage-based component beyond a certain limit.
OpenView's Kyle Poyar notes that B2B companies often use “usage-based paywalls”, charging per thousand queries or characters generated beyond a free tier. This helps cover the variable costs of licensing AI like ChatGPT.
The lesson for engineers: when designing AI-powered features using a paid API, consider scaling costs and ensure they’re recouped via the business model. If the product team hasn’t figured this out, it’s a red flag.
Now flip the scenario:
If you choose an open-source model and self-host, there’s no per-call fee to an external provider. However, the costs shift to infrastructure, paying for GPUs, cloud servers, and MLOps. How you monetize this model should still be carefully planned:
- Flat-rate pricing: You’ll be on the hook for cost overruns unless you optimize performance per dollar.
- Efficiency over time: Owning the model end-to-end gives you the potential to improve efficiency and lower costs, thereby increasing margins. AI startup Mosaic ML focused on continuously optimizing model training and inference to pass on savings to clients. Engineers should plan for ongoing efficiency gains, which can translate to better margins or more competitive pricing.
Owning the model allows you to control your own "AI cloud." The business inside your business focuses on optimizing model training, inference, and infrastructure.
Industry observers have noted that OpenAI as an API provider could become an “AWS-like tax on the entire ecosystem”, profiting from a slice of every AI-powered app’s revenue.
- Some companies are comfortable paying that tax for the convenience and quality it affords (just as many gladly pay AWS rather than run their own servers).
- Other companies will see an opportunity to differentiate by avoiding that tax, either by switching to open models or by passing the cost to users through pricing.
There’s no one-size-fits-all answer, but the important thing is recognizing the trade-off explicitly.
Engineers need to collaborate with product managers and even finance teams early:
- How much are we willing to rely on external AI services?
- What does that do to our gross margin at scale?
- Do we have a path to reduce that dependency if needed (e.g., fine-tune our own model later)?
These questions tie into the company’s strategy. For a high-end enterprise product with high margins, maybe using the best API is fine. For a mass-market consumer app with thin margins, you might need a more cost-efficient model or a very clever monetization scheme (ad-supported AI, perhaps) to make ends meet.
Engineers can lead these conversations by providing data that informs the pricing strategy. For example:
- Simulating costs: “If we have 100k users averaging 20 AI queries a day, at $X per 1k tokens, that’s $Y cost per month, can our revenue per user support this?”
- Collaboration: Engineers can model costs at various adoption levels, helping the business team set realistic pricing structures.
This approach combines engineering and financial modeling, ensuring that the AI features aren’t costly to give away for free. It’s a skill set that helps avoid surprise losses later.
Finally, model choice can influence go-to-market flexibility:
- Low-cost models: If you fine-tune a smaller, more efficient model, you can confidently offer free trials, freemium tiers, or usage-based bundles without fear of unsustainable costs.
- Expensive models: Using costly models might force you to charge sooner or limit usage, slowing user adoption.
There’s a competitive dynamic here: if you don’t get the cost/price equation right but a rival does (perhaps by using a cheaper model or more efficient approach), they can undercut you or offer a more attractive deal to users. We’re already seeing this in the market, some AI writing assistants tout using cheaper proprietary models or open-source ones to offer more generous plans compared to those solely using, say, GPT-4.
In summary, model choices and monetization decisions are two sides of the same coin. Your tech stack determines your cost structure, and your business model must support that. The best Generative AI teams iterate on both:
- Business goals (e.g., margin optimization) may influence engineering decisions on which model to choose.
- Engineering breakthroughs in model efficiency can open new pricing opportunities, such as usage-based billing or competitive pricing.
This feedback loop between engineering and business is key to building sustainable AI-powered products.
The Overlooked Legal and Compliance Costs
While focusing on dollars and cents, it’s easy to overlook another critical dimension where engineers must broaden their thinking: legal and compliance considerations. Generative AI is so cutting-edge that laws and regulations are scrambling to catch up, and this creates a minefield of potential risks and costs.
Today’s AI engineer has to be aware of intellectual property (IP) issues, data privacy regulations, and ethical guidelines, areas that historically might have been left to lawyers or compliance officers long after the product was built. In GenAI, these concerns need to be front and center during development, because they can heavily influence architecture and model choices.
Key Legal Considerations:
- IP Risks: GenAI models are trained on vast data sets, not all of which are properly licensed. This has led to lawsuits, such as OpenAI’s GPT-4 being targeted by publishers claiming unauthorized use of copyrighted text. Even if you didn’t train the model, potential restrictions or penalties could trickle down to your startup.
- Engineers should:
- Review the licensing terms of models.
- Be aware of non-commercial clauses or attribution requirements.
- Consider models like Llama 2, which require permission from Meta for certain use cases.
- Engineers should:
- Content Ownership and Liability: If your AI generates content or code, who owns that output, and who is liable for any legal issues? AI models sometimes produce content that mirrors copyrighted materials, which could lead to DMCA takedowns or legal disputes. Mitigating this risk may require:
- Implementing filters or watermarking to track AI outputs.
- Additional verification steps in your development process.
- Data Privacy and Security: If your product processes user data, consider where it is stored and how it is protected. For companies in regulated industries, ensuring compliance with data residency laws and data protection regulations (like GDPR) is essential.
- When choosing a model provider, ensure features like:
- Data encryption and opt-outs for data reuse.
- On-premise deployment options for sensitive data handling.
- Example: European companies might prefer models hosted within the EU to avoid potential GDPR conflicts, even though this may complicate architecture.
- When choosing a model provider, ensure features like:
Compliance Costs and Engineering Impact
- Global Regulations and Local Compliance: Governments are intensifying scrutiny on AI. For instance:
- The EU’s AI Act will soon impose strict transparency and risk management requirements.
- The U.S. AI Executive Order will regulate aspects of AI development.
Meeting local standards across different regions will require maintaining multiple versions of AI models, adding significant engineering complexity. This includes:
- Separate datasets, content filtering for different regions, and disabling high-risk features where necessary. Example: A startup operating across the U.S., EU, and Asia may need different models for each region, which increases the engineering workload.
Hidden Compliance Costs
An autonomous vehicle startup (PerceptIn) found that its compliance costs were 2.3x the development costs.
- PerceptIn's compliance cost: $344,000
- R&D cost: $150,000
This “compliance trap” applies to AI startups as well, where regulatory compliance can quickly exceed core development expenses.
- Legal and Compliance as Hidden Costs: Engineers must factor in:
- Regulatory compliance: Includes documentation, certifications, and legal consultations.
- Response verification: Creating systems to validate AI outputs and mitigate errors.
- User training: Ensuring proper use of AI tools and avoiding misuse.
Ethical and Liability Concerns
- Bias and False Results: GenAI models can produce biased or incorrect outputs, which can have serious consequences in applications like finance or healthcare.
- Engineers now need to implement “response verification” layers to validate AI outputs, reducing errors and mitigating liability risks. This may involve:
- Hiring human moderators.
- Building AI evaluation models for checks and balances.
- Engineers now need to implement “response verification” layers to validate AI outputs, reducing errors and mitigating liability risks. This may involve:
- Example: An AI assistant giving faulty financial advice could lead to serious legal repercussions.
Categories of Costs
Experts categorize AI enterprise costs into four key areas:
- Technology Costs: Data, GPUs, servers, and cloud infrastructure.
- Regulatory Compliance Costs: Documentation, audits, and developing region-specific models.
- Response Verification Costs: Monitoring systems, content filters, and insurance for errors.
- User Training Costs: Change management, including training teams to handle AI outputs responsibly.
All told, the legal/compliance dimension means an engineer’s decision to “use Model X or approach Y” can’t be made purely on technical merit. You might have a model that performs great, but if it was trained on a sensitive dataset or can’t be audited for bias, it could be a regulatory headache. Or a model that saves cost but has a murky license could expose the company to IP litigation. Modern engineering teams therefore involve legal and compliance teams early in the design phase of GenAI products. It’s not the most exciting part of building AI, but neglecting it can sink a product just as quickly as a bad algorithm can.
What Business-First Thinking Looks Like for Modern Engineering Teams
In today’s landscape, engineering teams operate with a business-first mindset that prioritizes both technical innovation and cost-effectiveness. Modern AI engineering teams are multi-disciplinary, blending engineering, product, finance, and legal considerations. Here’s how this mindset manifests:
1. Every Feature Has a Cost-Benefit Analysis
This is perhaps the clearest sign of the “engineer-economist.” Before building or deploying a new model or feature, the team evaluates its expected cost (in compute, development time, compliance overhead) against its expected benefit (in user value, willingness to pay, strategic advantage).
For example, if fine-tuning a custom model will cost $500k and two months of work, is the performance gain and IP ownership worth it compared to using an existing API? A cost-benefit analysis must be one of the key metrics in such decisions. In meetings, it’s not unusual to see engineers presenting not just technical benchmarks, but also ROI calculations and scenario projections. This shift requires engineers to become conversant in the language of unit economics and business KPIs, essentially wearing the economist hat.
It’s no coincidence that many AI startups are hiring or consulting with experts in pricing and FinOps (financial operations). Tracking things like cost per thousand predictions, gross margin impact of model choices, or forecasted ROI of an optimization has become part of the development process.
2. Cost-Awareness and “FinOps” Culture
Teams now instrument their systems to track usage and costs in real-time.
- If using external APIs, they monitor the API spending like a hawk.
- If self-hosting, they monitor GPU utilization and throughput.
The goal is to identify inefficiencies and optimize, much like an operations team managing costs of goods in manufacturing. This might involve optimizing prompts to reduce token usage (since shorter prompts/responses cost less), caching results for repeated queries, or dynamically routing requests (e.g., use a cheaper model for low-tier customers and an expensive model for premium customers). Such optimizations can save significant money at scale and improve the overall profit curve of the product. So, engineers start to think in terms of marginal cost:
- “What is the cost of serving one more user or one more request, and how can we reduce that?”
That’s a very economic way of thinking. In many SaaS businesses historically the marginal cost of an extra user was near zero; in AI it might be measurable, so it must be managed actively.
3. Collaboration With Product on Pricing and Packaging
Pricing strategy directly influences technical decisions. If the company decides to offer, say, 100 AI-generated summaries per month on the free plan and then charge beyond that, engineering needs to build the metering system to count those summaries and perhaps gracefully throttle or notify the user. Engineers also might suggest pricing changes based on technical reality: “Model X is costly; perhaps we reserve it for a premium tier and use a simpler model for the standard tier.”
Essentially, product-market fit for GenAI features includes finding the right price/cost balance, and engineers are key to achieving that fit. A SaaStr talk on AI product pricing noted that “pricing and packaging is an all-company strategy”, not just a product team task.
Everyone, including engineers, owns a piece of it. Engineers bring knowledge of the “inputs” (costs, performance limits) that inform how the product is sold.
4. Understanding of Customer Value and Willingness to Pay
Modern AI engineers are increasingly plugged into user feedback and value perception. It’s important to understand why a customer values an AI feature, is it saving them time (which might be monetizable), or is it just “cool” but not mission-critical? This matters because if the value is high, users might accept a usage-based pricing model or an upsell, whereas if the value is marginal, the feature should be cheap or included.
See putting yourself in the customer’s shoes: “What are they optimizing for? Fixed cost or fixed ROI?”. Engineers can contribute here by quantifying the performance in terms that matter to users (e.g., how much time does the AI feature actually save the user on average?). This crosses into product management territory, but it’s part of that blending of roles.
Some startups even have engineers join sales calls or customer interviews to hear pain points and understand how improvements or changes in the AI could unlock more value (and thus justify a higher price or wider adoption).
5. Emphasis on Scalability and Long-Term Sustainability
Business-first thinking means designing systems not just for the immediate demo, but for cost-effective scaling over time. For instance, an engineer might decide to incorporate an option to swap out the model later or use a model-agnostic architecture, knowing that today’s “best” model might become too expensive or deprecated, and the company might fine-tune its own model in a year. This kind of forward planning protects the business from being stuck with an untenable cost if circumstances change. It’s akin to managing risk, another economist trait.
Technically, it could mean using abstraction layers so you’re not tightly coupled to one vendor, or choosing an open-source framework that gives flexibility. The point is to keep options open that could improve economics later. We saw an example earlier: those who built products entirely reliant on OpenAI had to scramble when they introduced cheaper/faster models or consumer offerings that undercut them. Diversification and flexibility in your AI stack is a hedge against business risk.
5. Focus on Delivering Real Value (Outcomes) Over Hype
In the height of AI hype, it’s easy to justify using the fanciest model because it’s the latest thing. But a business-driven engineer will ask: does this actually improve the outcome for the user or the business? If a simpler solution gives 95% of the result at 50% of the cost, maybe that’s the wiser choice. If not, you’re underwater.
Keeping an outcome-focused mindset helps prioritize the right development efforts, e.g., maybe invest in fine-tuning the model to reduce hallucinations (improving reliability, which has huge business value in user trust) rather than, say, marginally increasing the model size for slightly better benchmark scores. It’s about delivering what actually moves the needle for users and the business, not tech for tech’s sake.
This often means measuring and talking about things like user retention, conversion rates on an AI-driven feature, support ticket reductions, etc., alongside precision or F1 scores. Again, it’s the fusion of engineering and business metrics.
A Dual Mindset in Practice: In this context, “engineers as economists” means cultivating an awareness that every technical choice has economic consequences. Engineers thrive when they can seamlessly pivot from debugging code to optimizing costs and strategizing pricing. The most successful GenAI products will be those that balance technical innovation with economic sustainability and commercial insight.
Conclusion: Building with an Investor’s Mindset
In the world of generative AI, engineering excellence alone is not enough. The difference between a demo-able AI and a profitable AI product lies in the economics and strategy behind it. Today’s engineers must think beyond code, balancing cost trade-offs, navigating regulations, and aligning technical decisions with monetization goals. Success in the GenAI space will belong to those who pair advanced AI capabilities with sound business execution, and that often starts with engineers who are equally at ease with both.
To engineers working with GPTs, transformers, and diffusion models: expand your role. Understand your company’s pricing strategy, stay informed on AI regulations, and be conscious of the costs behind what you build. The engineer who can navigate cloud GPU budgeting and gross margins as easily as model architecture will not only drive technical innovation but steer the product toward market success. In generative AI, engineers have become as much economists as coders, and the results speak for themselves.