Understanding AI Costs for Developers

𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐨𝐬𝐭 𝐂𝐮𝐫𝐯𝐞 𝐨𝐟 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Most teams think AI cost equals Model Inference. That is the smallest part of the curve. The real cost of AI systems unfolds layer by layer. Here is the Full Stack most Organizations Underestimate: 1. Business Entry Point (Value Trigger)   Cost drivers   - Revenue, risk, or cost-driven use case   - User-facing or internal workflow   - Business outcome expectations  Reality   Cost exists only when value is expected. 2. AI Gateway (Where Cost Begins)   Cost drivers   - Authentication and rate limiting   - Policy enforcement  Reality   This is where cheap inference meets real-world controls. 3. Model Access Layer (Visible Cost)   Cost drivers   - Model selection and fallback   - Token usage   - Budgeting and throttling   - Prompt templates  Reality   This is the only cost most teams consider early. 4. Decision and Orchestration Layer (Complexity Cost)   Cost drivers   - Task decomposition   - Multi-agent decisions   - Tool versus retrieval trade-offs  Reality   Cost grows with complexity, not accuracy. 5. Memory and Cache (Persistence Cost)   Cost drivers   - Conversation memory   - Long-term embeddings  Reality   Memory reduces compute but increases storage cost. 6. Retrieval and Knowledge Systems (Data Cost)   Cost drivers   - Data ingestion and cleaning   - Chunking and indexing   - Vector databases   - Reranking and context packaging  Reality   Data costs scale with usage and time, not model size. 7. Tool Access and Integration (Integration Cost)   Cost drivers   - Secure tool execution   - External system dependencies  Reality   Integration is where AI meets legacy complexity. 8. Workflow and Agent Coordination (Organizational Cost)   Cost drivers   - Coordination overhead   - Responsibility diffusion  Reality   Organizational cost compounds faster than compute cost. 9. Execution Runtime (Operational Cost)   Cost drivers   - Parallel execution   - Retries and fallbacks  Reality   Reliability always costs more than correctness. 10. Guardrails and Controls (Governance Cost)   Cost drivers   - Content and safety filters   - Hallucination checks   - Confidence and uncertainty scoring  Reality   Governance cost grows with impact, not usage. 11. Observability and Governance (Permanent Cost Layer)   Cost drivers   - Token and infrastructure monitoring   - Evaluations and audits   - Human-in-the-loop reviews  Reality   These costs never disappear. They only stabilize. Model cost is visible.   System cost is structural.   Governance cost is permanent. My recent post on Substack highlights the real costs of multi-agent solutions:  https://lnkd.in/eXYMthAC PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #AIAgents #EnterpriseAI

After optimizing costs for many AI systems, I've developed a systematic approach that consistently delivers cost reductions of 60-80%. Here's my playbook, in order of least to most effort: Step 1: Optimizing Inference Throughput Start here for the biggest wins with least effort. Enabling caching (LiteLLM (YC W23), Zilliz) and strategic batch processing can reduce costs by a lot with very little effort. I have seen teams cut costs by half simply by implementing caching and batching requests that don't require real-time results. Step 2: Maximizing Token Efficiency This can give you an additional 50% cost savings. Prompt engineering, automated compression (ScaleDown), and structured outputs can cut token usage without sacrificing quality. Small changes in how you craft prompts can lead to massive savings at scale. Step 3: Model Orchestration Use routers and cascades to send prompts to the cheapest and most effective model for that prompt (OpenRouter, Martian). Why use GPT-4 for simple classification when GPT-3.5 will do? Smart routing ensures you're not overpaying for intelligence you don't need. Step 4: Self-Hosting I only suggest self-hosting for teams at scale because of the complexities involved. This requires more technical investment upfront but pays dividends for high-volume applications. The key is tackling these layers systematically. Most teams jump straight to self-hosting or model switching, but the real savings come from optimizing throughput and token efficiency first. What's your experience with AI cost optimization?

As companies look to scale their GenAI initiatives, a significant hurdle is emerging: the cost of scaling the infrastructure, particularly in managing tokens for paid Large Language Models (LLMs) and the surrounding infrastructure. Here's what companies need to know: a) Token-based pricing, the standard for most LLM providers, presents a significant cost management challenge due to the wide cost variations between models. For instance, GPT-4 can be ten times more expensive than GPT-3.5-turbo. b) Infrastructure costs go beyond just the LLM fees. For every $1 spent on developing a model, companies may need to pay $100 to $1,000 on infrastructure to run it effectively. c) Run costs typically exceed build costs for GenAI applications, with model usage and labor being the most significant drivers. Optimizing costs is an ongoing process, and the following best practices would help reduce the costs significantly: a) Techniques, like preloading embeddings, can reduce query costs from a dollar to less than a penny. b) Optimizing prompts to reduce token usage c) Using task-specific, smaller models where appropriate d) Implementing caching and batching of requests e) Utilizing model quantization and distillation techniques f) A flexible API system can help avoid vendor lock-in and allow quick adaptation as technology evolves. Investments in GenAI should be tied to ROI. Not all AI interactions need the same level of responsiveness (and cost). Leaders must focus on sustainable, cost-effective scaling strategies as we transition from GenAI's 'honeymoon phase'. The key is to balance innovation and financial prudence, ensuring long-term success in the AI-driven future. #GenerativeAI #AIScaling #TechLeadership #InnovationCosts #GenAI

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

𝐌𝐨𝐬𝐭 𝐭𝐞𝐚𝐦𝐬 𝐮𝐧𝐝𝐞𝐫𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞 𝐀𝐈 𝐜𝐨𝐬𝐭𝐬. They budget for models… but forget everything around them. That’s why AI projects often look “cheap” in pilots — and expensive in production. Real AI spend isn’t just inference. 𝐈𝐭’𝐬 𝐬𝐩𝐫𝐞𝐚𝐝 𝐚𝐜𝐫𝐨𝐬𝐬 𝟏𝟐 𝐦𝐚𝐣𝐨𝐫 𝐜𝐨𝐬𝐭 𝐛𝐮𝐜𝐤𝐞𝐭𝐬 𝐞𝐯𝐞𝐫𝐲 𝐂𝐅𝐎 𝐚𝐧𝐝 𝐂𝐓𝐎 𝐬𝐡𝐨𝐮𝐥𝐝 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 👇 𝟏) 𝐂𝐨𝐦𝐩𝐮𝐭𝐞 (𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 + 𝐅𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠) GPUs, clusters, distributed runs. Costs rise with experiments, retries, and large models. 𝟐) 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 / 𝐑𝐮𝐧𝐭𝐢𝐦𝐞 (𝐓𝐨𝐤𝐞𝐧𝐬) API usage, token billing, agent tool calls. Driven by query volume and long contexts. 𝟑) 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 Warehouses, lakes, vector databases, feature stores. Embeddings, duplicates, and retention drive spend. 𝟒) 𝐃𝐚𝐭𝐚 𝐋𝐚𝐛𝐞𝐥𝐢𝐧𝐠 & 𝐇𝐮𝐦𝐚𝐧 𝐑𝐞𝐯𝐢𝐞𝐰 Annotations, SMEs, RLHF, QA checks. High-quality labeling is slow and expensive. 𝟓) 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 & 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Ingestion, ETL/ELT, cleaning, transformations. Messy data creates ongoing maintenance costs. 𝟔) 𝐌𝐨𝐝𝐞𝐥 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 (𝐏𝐞𝐨𝐩𝐥𝐞 𝐂𝐨𝐬𝐭) ML engineers, data scientists, prompt engineers. Hiring, retention, and specialist premiums add up. 𝟕) 𝐌𝐋𝐎𝐩𝐬 / 𝐋𝐋𝐌𝐎𝐩𝐬 𝐓𝐨𝐨𝐥𝐢𝐧𝐠 Model registries, prompt versioning, evaluations. Tool sprawl and enterprise licenses increase overhead. 𝟖) 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 Drift detection, hallucination monitoring, logging. Traces, alerts, and eval pipelines aren’t free. 𝟗) 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 Access control, secrets, red teaming, threat detection. Prompt injection and data exfiltration risks require investment. 𝟏𝟎) 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 & 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 Documentation, policies, audits, legal reviews. Regulations like GDPR and EU AI Act drive ongoing costs. 𝟏𝟏) 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐂𝐡𝐚𝐧𝐠𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Connecting AI to apps and workflows, training users. Adoption takes time and process redesign. 𝟏𝟐) 𝐕𝐞𝐧𝐝𝐨𝐫 & 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐂𝐨𝐬𝐭𝐬 SaaS tools, orchestration platforms, marketplaces. Watch for hidden add-ons and per-seat pricing. 𝐓𝐡𝐞 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: AI budgeting isn’t a line item. It’s a system. If you only plan for tokens, you’ll miss most of the spend. If you plan across these 12 buckets, you build AI that scales sustainably. Save this if you’re planning AI investments. Share it with your CFO or CTO. ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

Last quarter, my AI inference costs hit $100,000 annualized. I started small. Six months earlier, I was spending $200 a month on Claude. Then I added three agent subscriptions : Codex, Gemini, & Claude Code. I was paying $600 a month. Next I started using AI to transform my todo list into my done list, increasing tasks to 31 per day. $92 daily inference invoices started arriving. Then $400 per month on browser agents. Within two quarters, my inference spend grew from $7,200 to $43,000 to over $100,000 run rate. So I migrated to an open source model. It took a weekend. The key was building the right testing loops : I had six months of historical task data, so I could replay requests through the new model & hill-climb to parity with AI agents working through the night. By Sunday evening, they performed identically. At 12% of the cost. I’m not the only one paying attention to this cost. Technology companies are adding a fourth component to engineering compensation : salary, bonus, options, & inference costs. Levels.fyi pegs the 75th percentile software engineer salary at $375k. Add $100k in inference & the fully loaded cost is $475k. That’s 21% in tokens. The question CFOs will pose : what am I getting for all this inference spend? Can I do it cheaper? If the metric for a new cloud is gross profit per GPU hour, the employee equivalent is : productive work per dollar of inference. For me, the answer is 31 tasks a day at $12k annually. The engineer still burning $100k? They’d better be 8x more productive! Will you be paid in tokens? In 2026, you likely will start to be.

$7,225 for one day of coding. And Cursor isn't even the worst example. Replit's margins went negative. Anthropic throttles its best users. I mapped pricing across 50 AI startups. Six distinct patterns emerged. The core tension: traditional SaaS has near-zero marginal cost per user. AI products pay for compute on every interaction. A casual Claude user costs pennies. A developer running Claude Code all day costs tens of thousands per month. Your best users are your most expensive users. That tension is breaking every pricing model in the market. Cursor charged a flat 500 requests/month. Worked fine until users leaned into multi-step agent workflows. They switched to credit pools. One developer burned 500 requests in a single day. The plan description changed from "Unlimited" to "Extended" twelve days after launch. Replit grew 15x in ten months ($16M to $252M ARR). But they were buying revenue with compute. When they launched a more autonomous agent, margins crashed to negative 14%. They had to invent "effort-based pricing" mid-flight. Anthropic played it differently. Their $17/$100/$200 tiers map to genuinely different user personas, not volume bands. A casual user and a Claude Code developer are different products with different willingness to pay. The lesson across all 50 companies: before you set any price, pull the cost distribution. What does your P10 user cost? P50? P90? If the ratio exceeds 10x, flat pricing will break. In AI products, it almost always exceeds 10x. Full guide with all 6 models, 4 case studies, and a decision tree: https://lnkd.in/gdKaQSMk

Have you noticed how AI models are getting more affordable? OpenAI just cut GPT-4o’s API costs by 50% for input tokens and 33% for output tokens. Google didn’t hold back either, slashing Gemini 1.5 Flash prices by 75%. You can now access GPT-4o mini for as low as $0.15 per million tokens, and Llama 3.1 for just $2.70 per million tokens—a whopping 66% less than similar Azure offerings. All sounds great! ... But here’s the overall picture: lower prices don’t automatically mean you’re saving money directly. Here’s why: Testing: It’s not just about plugging in the model and hitting go. Continuous testing is key to ensuring your AI performs smoothly across all the different scenarios you throw at it. Safety: AI is powerful, but with great power comes the need for robust safety measures. Protecting data privacy and staying compliant with regulations isn’t just a nice-to-have—it’s a must. Risk Management: AI isn’t without its risks—think biases, cybersecurity threats, and more. Managing these risks is crucial to keeping your AI ethical and trustworthy. So yes, AI is getting cheaper to access, but responsible organisations know that the real cost involves much more than just the price of entry. Investing in testing, safety, and risk management is what will truly unlock AI’s potential and ensure it’s used effectively and ethically. Image credit: Shawn Wang, Smol.ai #AI #ArtificialIntelligence #TechInnovation #AICosts #AIPricing #AIEthics #DataPrivacy #RiskManagement #AITesting #DigitalTransformation #ResponsibleAI #generativeAI

The most expensive AI cost is often not the largest line item. It is the one that remains unseen long enough to be mistaken for normal. Most teams managing AI spend are still looking at one visible surface - usually cloud, sometimes tokens, sometimes model bills - and treating that as the whole system. But AI behaves like a stack. NVIDIA described that stack well this week: Energy → Chips → Infrastructure → Models → Applications. And the important part is the implication: every application request pulls on the layers beneath it. This matters because a user request in an AI system is not just a software event. It is a live economic event. A request enters at the application layer, but its cost is shaped by what happens underneath: which model is selected, how much context is passed, whether retrieval is invoked, whether tools are called, and how long the serving infrastructure works to produce the response. In traditional systems, cost was more closely tied to provisioned capacity. You allocated resources, and the workload ran within a relatively understandable boundary. AI breaks that comfort. Two requests that appear similar to a user can produce completely different execution paths underneath, and therefore completely different cost outcomes. That is why I keep saying AI cost is behavioral cost. Not because infrastructure stopped mattering. It still matters a lot. But infrastructure cost by itself cannot explain why one task cost 3x more than another task that looked almost identical from the outside. In traditional cost management, a large part of the problem was allocation, utilization, commitment strategy, and waste reduction on provisioned resources. In AI, those still exist, but now there is another layer of complexity on top: the cost of intelligence is being generated dynamically, in real time, by how the system behaves per request. To understand actual AI cost, teams need visibility across the full execution path. Not because every team needs to obsess over every watt or chip cycle, but because without cross-layer visibility, you cannot explain causality. And if you cannot explain causality, you are only observing spend after the fact. That distinction matters more than most people realize. Spend visibility is not the same thing as cost understanding. A dashboard can show totals and still hide the mechanism. And when the mechanism is hidden, teams make the wrong optimizations. They cut the visible line item, miss the behavioral driver, and conclude AI economics are irrational when in reality they are just incomplete. So my view is simple: if your tooling, governance model, or cost strategy cannot connect these layers into one understandable cost path, then you are not managing AI cost end to end. You are managing one piece of it. And in AI, a piece of the picture is often what creates the illusion that you have the whole one. (See link in comments) #AICostGovernance (Image Credit: Nvidia Blog)

Last week, a VC firm invited me to eval an AI startup. The founder pitched building a custom 70B parameter model for insurance underwriting. Tailored inference. Vertical SaaS. The works. The ask? $1M seed round. I stayed quiet during the pitch. Every founder deserves respect — they're risking everything to build something. That courage is real. But afterwards, I told the VC: "This math doesn't work. Not even close." Here's why 👇 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰 𝗠𝗮𝘁𝗵 𝗡𝗼𝗯𝗼𝗱𝘆 𝗗𝗶𝗱 Training a 70B model needs ~8.4 × 10²³ FLOPs. One FLOP = one math operation (a multiply or add). A 70B model does 6 of these per parameter, per token. Trained on 2 trillion tokens. 6 × 70B × 2T = 840,000,000,000,000,000,000,000 operations. That's 8.4 × 10²³. Let that sink in. 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗖𝗼𝘀𝘁𝘀 → 512 H100 GPUs × 40 days × $2.50/hr = $1.2M just for ONE training run → First run WILL fail. Budget 2-3 attempts = $2.5-3.5M → That's just training. No team. No data. No infra. His entire $1M? Gone in 3 weeks. Not even one complete training run. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗖𝗼𝘀𝘁𝘀 (𝗣𝗼𝘀𝘁-𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴) Serving 70B model to customers: → 4x H100 GPUs minimum = $7,200/month (cloud) → 1000 concurrent users? 16x H100s = $28,800/month → That's $3.5L/month just to keep the lights on 𝗪𝗵𝗮𝘁 𝗛𝗲 𝗦𝗵𝗼𝘂𝗹𝗱 𝗗𝗼 𝗜𝗻𝘀𝘁𝗲𝗮𝗱 Fine-tune Llama 3 70B with QLoRA on insurance data. Cost: $200-500. Time: 2 days. Same business outcome. Spend the $1M on distribution, not GPUs. 𝗧𝗵𝗲 𝗟𝗲𝘀𝘀𝗼𝗻 Bangalore has incredible founders. Brilliant people building real things. But VCs — please do the FLOP math before writing cheques. Founders — please learn compute economics before pitching "we'll build our own model." Not every AI startup needs to train from scratch. Most shouldn't. The moat isn't the model. It's the data, distribution, and domain expertise. $1M buys you a world-class fine-tuned product. $1M doesn't buy you 1% of a foundation model. Know the difference. 💡 Quick Reference: 70B Model Costs Training: $1.2-3.5M (cloud GPUs) Inference: $7,200-28,800/month Fine-tuning instead: $200-500 Engineering team (12 months): $1-3M Total realistic budget: $5-8M minimum 1 FLOP = 1 floating point operation 70B model training = 8.4 × 10²³ FLOPs Your H100 does 10¹⁵ FLOPs/sec Do the division before the pitch. #AI #Startups #Bangalore #VentureCapital #DeepTech #GPUEconomics #FounderAdvice