Understanding AI Costs for Developers

When you query AI, it gathers relevant information to answer you. But, how much information does the model need? Conversations with practitioners revealed the their intuition : the input was ~20x larger than the output. But my experiments with Gemini tool command line interface, which outputs detailed token statistics, revealed its much higher. 300x on average & up to 4000x. Here’s why this high input-to-output ratio matters for anyone building with AI: Cost Management is All About the Input. With API calls priced per token, a 300:1 ratio means costs are dictated by the context, not the answer. This pricing dynamic holds true across all major models. On OpenAI’s pricing page, output tokens for GPT-4.1 are 4x as expensive as input tokens. But when the input is 300x more voluminous, the input costs are still 98% of the total bill. Latency is a Function of Context Size. An important factor determining how long a user waits for an answer is the time it takes the model to process the input. It Redefines the Engineering Challenge. This observation proves that the core challenge of building with LLMs isn’t just prompting. It’s context engineering. The critical task is building efficient data retrieval & context - crafting pipelines that can find the best information and distilling it into the smallest possible token footprint. Caching Becomes Mission-Critical. If 99% of tokens are in the input, building a robust caching layer for frequently retrieved documents or common query contexts moves from a “nice-to-have” to a core architectural requirement for building a cost-effective & scalable product. For developers, this means focusing on input optimization is a critical lever for controlling costs, reducing latency, and ultimately, building a successful AI-powered product.

One learning from AWS days: Product Managers who don’t own pricing are project managers with PM ego's. Without digging into the pricing, you’re just managing projects—not driving strategy. GenAI applications, however, aren’t your typical SaaS products. Pricing them requires a AI specific knowledge, centered on three critical questions: 1. What are your costs? 2. How do you reward users through pricing? 3. How do you balance commodity versus specialty pricing? Here are some hard-earned lessons on pricing GenAI products, with insights. 💲 Know Your Costs Pricing strategy starts with understanding your costs—not just infrastructure , but the often-overlooked evaluation, training, and assurance expenses. Key Insights: 1️⃣ Evaluation Costs: It costs 10x more to evaluate a GenAI app than to build it. Human evaluation is crucial for quality, but it’s slow and expensive. 2️⃣ Training Costs: Fine-tuning models is tempting but risky. The ROI is usually poor, and it’s rarely worth the effort. 3️⃣ Assurance Costs: To handle high demand, you’ll need resources to avoid latency issues tied to Token Per Minute (TPM) and Request Per Minute (RPM) limits. Costs to get dedicated TPM/RPM are 100X more. 🏆 Reward Usage Early and Wisely Freemium models are common in GenAI apps, but conversion rates are often disappointing. For instance, ChatGPT’s freemium-to-premium conversion rate is just 0.5%—far below the SaaS average of 2-5%. 1️⃣ Move from Freemium to Paid Quickly: Offer low-cost subscriptions early. For example, after 5 free uses, charge $9.99 for 50. Use this to calculate ROI early. 2️⃣ Introduce Tiered Pricing: Create sliding-scale plans to encourage adoption. The next 50-500 uses could cost $19.99 at a reduced per-use rate. 3️⃣ Focus on Performance Tiers: Offer improved service instead of just more usage. For example, premium tiers could guarantee response times under 1 second for $19.99 per 50 queries. 🫅 Commodity vs. Specialty Pricing With tools like ChatGPT and Gemini offering more capability in each release, differentiation is key. But pricing that differentiation effectively can be tricky. 1️⃣ Plan for On-Premise Offerings: Some customers will demand localized models or data control. Be ready with a premium-priced on-prem solution. 2️⃣ Discount Future Pricing: AI API costs are dropping rapidly (100x every three months). Offer locked-in discounts for long-term commitments to create pricing moats. 3️⃣ Outcome-Based Pricing: Move from subscriptions to charging for results. For example, instead of selling a tool to write emails, charge $10 for every qualified lead it generates. The biggest lesson? Own your pricing strategy. Let’s connect! 👇 Join our community where we meet every Saturday and learn together for FREE :  https://lnkd.in/gSk8gs3k Check out the #1 only 5 star rated course that goes in details of pricing and much more : https://lnkd.in/gWzDSMJp 

The era of "move fast and bill the VC" for AI development is officially over. And the bills are coming due. Oscar Le recently shared this screenshot (see image) after their team burned through 500 premium requests on a $7,000 yearly Cursor AI team subscription... in a single day. This isn't just about one Cursor's pricing change. It's a flashing red light for engineering leadership and developers building with AI today. For a long time, the true cost of AI has been masked by VC-subsidized API calls and "unlimited" plans. Those days are disappearing very quickly. P.S. Optimal AI has an unlimited plan because we use smart model routing techniques, our agents are multi “model”. This is how we can continue to provide quality outputs at a drastically lower cost. This is what the new reality is: The real cost of a request: What does an API call to the latest model actually cost? It's no longer a simple flat rate. The shift to token-based pricing means complex queries on powerful models can now cost orders of magnitude more than simple ones. Predictability is out the window. The end of subsidized experimentation: Many AI-powered tools that we've come to love were essentially burning venture capital to acquire users. As the market matures, these companies are being forced to pass the real costs onto their customers. The "rug pull" is the new business model. The talent tax is real: The most significant cost in any AI project isn't the API bill, it's the highly specialized (and expensive) talent required to build, fine-tune, and manage these systems effectively. As users, we need to shift our mindset from purely focusing on the magic of AI to also managing its economics. We must instill a culture of cost-awareness in our teams. This means: Deeper cost monitoring and attribution: Who on your team is running up the bill? What features are the most expensive to run? You can't manage what you can't measure. Smarter model routing: Is the latest and greatest model always necessary? Or can a smaller, more efficient model get the job done for a fraction of the cost? The "vibe" of building with AI was fun while it lasted. Now it's time to get serious about the P&L. The companies that thrive in this next chapter of AI will be the ones that master the art of building both innovative and economically viable products. What did your last AI bill look like and what are you doing to manage the rising costs of AI in your teams?

The AI Revolution is propelled by Large Language Models (LLMs) and cost per million tokens is the metric that drive AI's unit economics. Prices vary wildly, from $0.015 to $60, why is this the case? SaaS applications often consume LLMs as Model-as-a-Service (MaaS) which is priced per token. A token is a word or part of a word. As an example, the first Harry Potter book is about 100,000 tokens. Input tokens (i.e. the prompt and context) are much cheaper to process than output tokens (i.e. what the LLM generates) and sometimes this is reflected in the LLMs pricing. For example, OpenAI has a 4x price difference between GPT-4o input and output tokens. The main driver for cost is model size. Right now, a good rule of thumb is that one million tokens cost about $0.01 per billion model parameters for a regular model. The cheapest model I am aware of right now is Llama 3.2 1b on DeepInfra at $0.015 per million tokens (https://lnkd.in/gf2d7nT9). Llama 405b costs about $3.50 on Together AI. The most expensive one is likely OpenAI's o1 due to it's internal reasoning tokens. Cost per token also depends on the latency and token rate. Most AI accelerators run most efficiently with high batch sizes. Running many requests in parallel increases the overall output of the AI accelerator, but each user now has to wait until everyone is finished. So faster tokens end up costing more. The fastest LLM inference currently is offered by companies like Cerebras Systems, Groq and SambaNova Systems that use different AI accelerators architectures. You essentially trade cost for speed. An example for Llama 405b: - Cerebras Systems ~1,000 TPS at $12/million tokens - Together AI ~80 TPS at $3.5/million tokens It's not clear to me how big the market for these high-speed tokens will be. 10 TPS is already human speed reading territory, so it's not really needed for humans. Agents (once they actually work) would benefit, but most may be cost sensitive. And last but not least, as we wrote last week the cost of tokens is currently decreasing by 10x year-over-year as we wrote last week. Links: - Prices decrease 10x year-over-year: https://lnkd.in/gyGuGCDD - DeepInfra Pricing: https://lnkd.in/gAe4yian - Together Pricing: https://lnkd.in/gfdfYQyf - OpenAI Pricing: https://lnkd.in/g3Kud9gR - Cerebras with 1k Tokens/s: https://lnkd.in/gu_eDdTb

AI field note: progress in inference cost is expanding the scope of where AI can be applied effectively and efficiently. Is AI finally breaking free from cost constraints? Let's dive in. AI models require computation, and any amount of computation incurs a cost (even if you've already paid for the computer). For any model, the cost of that computation is part of its design, and it's why you see families of model variants (GPT 4o and 4o-mini, Claude Haiku and Sonnet, Nova Lite and Pro, etc). Historically, the cost of this computation has forced constraints at both the low and the high end, resulting in a pretty narrow price range overall. Not low enough at the low end for very high scale deployments, without enough additional capability and quality at the high end to warrant higher prices. The knock-on impact to this narrow range was a relatively narrow set of use cases which could be operationalized and put into production, cost effectively. But that range - of price and use case - is rapidly expanding. Advances in model architectures, capabilities, chips, and inference optimization have really released some of these constraints in the past few months, materially widening the range of prices, and with it, the places where gen AI apps can be put into production effectively and efficiently. A few examples... 👛 Cheaper cost at the low-end. Gen AI is getting cheaper for repetitive, low-complexity tasks which are routine and common for many organizations (such as contact centers). 🌟 Amazon Nova Lite is a good example of this, which is remarkably cheap (0.00014 per 1000 output tokens), and retaining a good amount of capability. 💴 Unbounded capability at the high end. The opportunity to pay more for a better answer is a good one for many use cases where reasoning and math play an important part (like advanced coding tasks). 🧰 OpenAI's o1 models let the model compute for longer, resulting in more sophisticated responses, and with the new Pro mode ($200 a month), the length of time a model can think is extended (resulting in commensurately better responses where thinking really helps). It's not unbounded yet, but I suspect it's not far off. Models - and the systems build around them - continue to differentiate themselves from one another in price, latency, capability, and behaviors. This breadth of capability can be channeled to solve a broad set of tasks, making it more vital than ever for organizations to keep multiple model families on deck for their solutions. This isn't the 'race to the bottom' that some predicted, and I think we can expand this range (reducing at the low end and becoming unbounded at the high end) will continue for some time. Commoditization is a long way off.

Understanding The AI Cost vs. Performance Tradeoff As AI models scale, so do infrastructure requirements—and costs. For example, the difference between a 1B parameter model and a 100B parameter model is substantial: - 100B models require 100x more storage and 100x more compute compared to 1B models. - Models up to 10-20B parameters can fit within the memory of a GPU. - Deploying 100B+ parameter models demands a custom platform, which significantly increases costs. For most practical applications, models in the 10B or smaller range often provide the best balance between performance and cost. In the world of AI, bigger isn't always better. When it comes to scaling, it's about finding the sweet spot that maximizes value while controlling costs.

As companies race to integrate generative AI into their products, understanding the cost of these features becomes just as important as building them. But GenAI costs aren’t like those of traditional software—API calls, model tokens, and latency all add up in invisible ways. In a recent blog post, Workday’s engineering team shared how they built a framework to measure the unit cost of GenAI features, giving product and engineering teams the visibility needed to make informed trade-offs. One key insight from the blog is that the cost of a GenAI feature can’t be separated from its usage patterns. For example, costs vary depending on how frequently a feature is used, how many tokens are generated, and even the prompts passed to the model. This becomes especially tricky in shared infrastructures where model APIs are called asynchronously or in the background. To address this, the team built a system that combines product analytics, runtime instrumentation, and LLM billing data to calculate cost per feature. This granular view allows teams to track GenAI unit economics much like cloud infrastructure costs, enabling them to generate insights and make cost analysis an integral part of financial, product management, and engineering governance. It’s a recommended read for anyone interested in building GenAI features with cost transparency in mind. #DataScience #Analytics #Insights #MachineLearning #LLM #GenAI #ROI #cost #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gvPfiXgg