LLM Inferencing Costs are Going to $0

Why the Next Wave of AI will feel (almost) free and what that means for businesses

LLM Inferencing Costs are Going to $0

Peter Zatloukal – Partner, Techquity

Zero is pretty cheap. It changes many business decisions and opens up entirely new business models, some of which previously seemed impossible. The price of AI “thinking” (aka inferencing) is going to zero in a hurry. Let’s explore what this means and how CEOs, CTOs and Boards of Directors should plan for a new reality of intelligence that is extremely cost effective.

Shrinking the bill for AI inferencing

Inferencing is simply the moment when a trained machine learning model is asked a question and has to calculate an answer. Think of it as the runtime electricity and cloud-GPU minutes to solve an AI task. Inferencing is not the heavy R&D that went into training the model in the first place. Thanks to ever-faster chips, better software optimizations, and a flood of high-quality open source models, the per-query cost of that calculation is falling fast.

Over the next two-to-three product cycles, we can expect that everyday AI workloads – summarizing documents, sorting customer emails, answering questions, transcribing speech, and even lifelike text-to-speech – will run directly on a user’s laptop or phone, or in a commodity cloud container that costs only a few dollars a day to run.

For a CEO, that means AI features can be bundled into existing products without blowing up COGS or forcing them to enact premium price tiers. AI just becomes another part of the product, like search. This has already happened in many popular SaaS products, like Canva, Zapier, and Slack.

Yes, "frontier" models such as Claude, ChatGPT, and Gemini will continue to thrive and underpin inferencing. This will remain necessary for certain massive reasoning tasks, like discovering new molecules or processing large volumes of chats or medical records. Their larger parameter counts, huge context windows, and built-in safety tooling still demand huge compute budgets. But you really only need them for cases that require deeper reasoning, broader modality coverage, or strict compliance guarantees. Everything else is on a glide path toward near-zero marginal cost. This opens the door to aggressive product bundling, inexpensive usage-based pricing experiments, and margin-accretive AI differentiation at scale in every product. It also means building new, AI-first products that are centered around models that will be far cheaper and faster than previous software development efforts in this space.

The LLaMA floodgate moment

When Meta released the original LLaMA weights in early 2023, it triggered a chain reaction the field had never seen. Releasing the weights meant Meta gave the world a powerful, working AI engine, and not just a demo. It let developers build on it, customize it, and innovate fast, jumpstarting the open-source AI boom and breaking Big Tech’s monopoly on cutting-edge language models.For the first time, a model that matched (and in some tasks exceeded) high-quality proprietary AI was freely downloadable. LLaMA was free to use, basically open source, and runnable on your own hardware instead of being locked behind a proprietary cloud API. Within weeks the weights leaked beyond the academic-research license, hobbyists ported them using llama.cpp, and 16-bit checkpoints were squeezed down to 8-, 6-, and even 4-bit formats that could run on a single laptop CPU. Fine-tuning shortcuts such as LoRA let you customize the model with a few thousand examples and a rented NVIDIA RTX 4090, rather than a data-center cluster. The result felt like handing machine guns to toddlers. Suddenly every indie hacker, student lab, and tinkerer could build chatbots, translators, and code assistants that previously required hundreds of millions of dollars in R&D.

Within a matter of weeks, hundreds of derivative models built atop of LlaMA appeared on HuggingFace (an AI developer hub, similar to GitHub).

LLaMA cracked open the “closed‑weight” paradigm, inspiring a cascade of successors: Alpaca, Vicuna, WizardLM, StableLM, Mistral, and Qwen. Each iteration arrived faster, smaller, and cheaper while community tooling hardened around them, from GGUF file formats to one‑click installers on Windows and macOS. And many of these now use Apache 2.0 / MIT licenses, vs. the original LLaMA licensing which was more restricted. The psychological barrier fell, too. Business leaders who once assumed AI meant a painful monthly OpenAI invoice saw interns spinning up local models for zero cloud spend and asked, “Why can’t we do that in production?”

LLaMA did not just lower costs. It rewired expectations about who gets to wield cutting‑edge language AI tech.

Every new open model release delivers either more capability at the same parameter count or similar capability at a fraction of the footprint. Quantization, LoRA adapters, and speculative decoding compound those gains. The result: near‑real‑time generative AI tasks can be solved on consumer silicon with token costs converging towards zero.

Beyond text: the rest of the AI modality stack

• Speech‑to‑Text (ASR). Whisper‑tiny (<40M parameters) now transcribes and translates on‑device and Google’s AudioPaLM point to sub‑second latency with no server round‑trip.

• Neural TTS. Models like XTTS and Meta’s VoiceCraft can synthesise natural speech in <100 ms on mobile GPUs.

• OCR & Vision Encoders. PaddleOCR, TrOCR, and lightweight ViT variants run comfortably on CPUs, unlocking document ingestion without paying an API toll.

These components historically made up a sizable chunk of “AI cost of goods sold.” By late 2025 they will be table stakes, a free layer in many software stacks because of these excellent open source alternatives.

Where does value live now?

“If the model is free, what are we actually charging for?”– Common board‑room refrain, 2025

• Domain knowledge and unencoded data. Industries such as construction, manufacturing, and biotech still possess tacit workflows never captured in public datasets. Encoding that expertise into prompts, retrieval pipelines, and fine‑tunes creates defensibility. Multimodal approaches matter too (pixels, videos, and audio are all vital signals to train unique solutions to AI tasks).

• User experience. When everyone has the same raw model, how you wrap it,memory, tooling, delightful UX, becomes the potential moat. Think Figma‑level polish, not command‑line hacks. This is why OpenAI has maintained their lead even if the underlying models have become far more similar in capabilities.

• Integration & agent orchestration. LangChain, LlamaIndex and Agent‑Hypervisors turn a model into an end‑to‑end workflow with observability, retries, anti-hallucination “judge” models, and cost controls. That plumbing – and not only the question/answer LLM capabilities – is what enterprises will sign SOWs for.

What does this all mean?

The marginal price of intelligence, at least for the everyday, non‑frontier variety, is trending toward zero. The history of computing tells us that when a fundamental resource becomes effectively free, the battleground shifts. In AI, that battleground will be data curation, product design, and integrated workflows. The winners will be those who redeploy the savings from GPU bills into deeper customer understanding and faster shipping cycles.

One example is how, as data communications became cheap and trending towards zero marginal costs, WhatsApp took over communication of voice, video and data and became the global standard combining "free" with an amazing user experience. The incumbent telcos have been disintermediated into primarily providers of commodity pipes and people consider it quite normal to have group discussions globally or video calls with friends and family in distant locations.

Alongside this, the cost of experimentation plummets. Marginal costs of AI will cease to be a determining factor in product, architecture and user experience considerations. “Which LLM?” becomes a far less important concern than creating something magical for users.

For advanced reasoning tasks, the frontier models – ChatGPT, Gemini, Claude, etc. – will likely still have continued strong usage, particularly as they keep innovating on extremely compute-intensive tasks like processing huge context windows (which is something beyond the reach of these fancy new open source models and consumer hardware).

Cost aside, since it’s now possible to host very powerful models locally (on a MacBook, or a relatively small server-class machine), companies can build fully-private behind-the-firewall solutions to business tasks. It’s reasonable to expect “productized” solutions with local language models will show up in multiple verticals soon.

Get ready: the AI free‑lunch era is about to begin.

Peter Zatloukal is a Partner at Techquity, focused on artificial intelligence and machine learning. He is a former Director of AI/ML Engineering at Apple, where he led a 100+ person team advancing applied machine learning, computer vision, and software systems. Previously at Amazon, he launched Alexa’s voice shopping experience and led major initiatives across Fire TV, search, and multimodal interfaces.