Forem: Kamal Rawat

AI Models: Small vs. Large - Choosing the Right Scale for ROI

Kamal Rawat — Fri, 29 Aug 2025 07:33:41 +0000

The AI Paradox: You Have the Model, But Do You Know the Problem?

In our last article, we pulled back the curtain on AI models. We learned that more parameters don't automatically mean a better or smarter solution, and a bigger model can come with a hidden "AI tax" on your budget.

But before you even choose a model, here's the bigger question:

Do you truly understand your business problem?
Why Do We Even Need AI Models ?

This article isn't about the tech; it's about the strategy.

Businesses today are data-rich but insight-poor. From retailers handling millions of transactions to logistics firms tracking shipments worldwide, data is exploding faster than companies can interpret it.

AI models turn this chaos into clarity. They help companies by:

Retail & E-commerce: Forecasting demand so shelves aren’t empty or overstocked. For example, Walmart uses AI-driven demand prediction to cut excess inventory and save millions annually.
Finance: Detecting fraud in real-time by spotting unusual transaction patterns that humans or rules-based systems would miss. JPMorgan’s fraud detection AI saves the bank millions each quarter.
Insurance: Automating claims processing by reading documents, classifying damage categories, and reducing human turnaround time from days to hours.
Healthcare: Analyzing X-rays or lab reports faster than radiologists in some cases, enabling earlier intervention and improved patient outcomes.

👉 Whether powered by a large general-purpose model or a small, domain-specific one, the goal is the same: turning raw data into actionable business outcomes.

Before we continue further, Minor acknowledgement that models exist on a continuum, not just two buckets (Small or Large).

Sharing this image for reference

While models exist across a range of sizes, for simplicity we’ll compare two ends of the spectrum: small, task-specific models vs. large, general-purpose models.

⚖️ The Core Trade-off: Small vs Large Models

Small, Specialized Models
- Trained or fine-tuned for a narrow task (e.g., contract clause extraction, sentiment analysis, medical diagnosis).
- Lower cost, faster inference, easier to deploy on edge devices or within compliance-restricted environments.
- Usually weaker in general reasoning, multi-step logic, or unexpected queries.
Massive, General-Purpose Models (GPT-4, Claude, Gemini, etc.)
- Trained on broad internet-scale data, so they’re versatile across many domains.
- Strong at multi-step reasoning, handling ambiguity, combining context.
- Costly, compute-heavy, and sometimes "overkill" if you only need narrow answers.

Lets take a scenario where there is RAG(Retrieval-Augmented Generation) pipeline attached to LLM. Lets break it down:

Vector Database
- Stores your company’s documents as embeddings.
- On query, it retrieves the most relevant chunks (knowledge grounding).
LLM(Small or Large)
- Takes the retrieved chunks.
- Generates a natural, contextually accurate response.

🔑 The Key Question: Is a Small Model Enough?

✅ Yes, small models can be enough if:

Your queries are narrow and predictable (e.g., “show me the policy clause,” “extract invoice total”).
The retrieved chunks already contain the answer in a clean format.
You mainly need language fluency to stitch together responses from your data.
You care about cost efficiency and want to scale cheaply.

❌ But larger models are valuable when:

The query requires reasoning beyond retrieval, e.g., "Compare the risk posture of Policy A vs Policy B based on clauses".
Users may ask ambiguous, incomplete, or tricky questions that need interpretation.
You need multi-hop reasoning (e.g., combining insights across multiple retrieved documents).
The data retrieved is messy, incomplete, or requires contextual stitching.

🛠️ Real-World Example

Small model case:
You ask: "What’s the interest rate in Contract #123?"
- The vector DB retrieves the exact clause.
- A small LLM (even 7B) can read that snippet and answer perfectly.
Large model case:
You ask: "Across all ~2500 contracts, which clients have the most favorable early termination rights, and what risk does that pose to revenue forecasts?"
- Requires pulling from many documents, understanding legal language nuances, and connecting business implications.
- A larger LLM is much more reliable here.

🏁 Strategic Answer

If your use case is structured, retrieval-heavy, and domain-specific, Small specialized LLM (cheaper, faster).
If your use case requires reasoning, interpretation, multi-step synthesis then Larger general-purpose LLM (better accuracy).

👉 Many companies use a hybrid approach:

Use small LLMs for 80% of simple, repetitive queries.
Fall back to larger LLMs only when complexity is high. (This is called an orchestration strategy—think of it as a "model router.")

This isn’t a one-size-fits-all problem. What’s the most complex business problem you've seen that AI could solve? Share your thoughts below!

AIstrategy #BusinessLeader #LLM

AI Models Demystified: What Really Happens Inside an AI Model?

Kamal Rawat — Thu, 28 Aug 2025 08:11:45 +0000

💡 Every AI headline sounds the same: "This new model has 70B parameters" or "Trained on 2 trillion tokens".

Sounds impressive, right? But what does that actually mean for your business - and more importantly, your budget?

Let’s break it down with a practical lens.

🚀 Meet ShopEase: A Startup at a Crossroads
ShopEase, a mid-sized e-commerce startup, launched a chatbot to handle customer queries.

On a small AI model, it worked fine for FAQs.
But when customers asked about refunds, order tracking, or warranty overlaps → the bot fumbled.
The CTO was tempted: "Let’s just upgrade to a bigger model like GPT-4. More parameters = smarter bot, right?"

Not so fast.

🧩 What Parameters Really Mean (Without the Jargon)
Think of parameters as the brain cells of an AI model. More parameters = more "memory" of patterns.

GPT-2 → 1.5B parameters.
GPT-3 → 175B parameters.
GPT-4 → 1.76 to 1.8 trillion parameters.

Training GPT-3 reportedly cost $4.6M in compute. That’s before you even use it.

So when you hear "70B parameters", don’t think "smarter". Think "heavier to run, more expensive to maintain".

💵 Tokens: The Meter That Never Stops Running
Here’s the gotcha most leaders miss: even if you didn’t train it, you still pay per token when you use it.

GPT-4o-mini: ~$0.15 per 1M tokens.
GPT-4: ~$30 per 1M tokens.

👉 That’s a 200x difference in cost.

Back to ShopEase:

Their chatbot handles 1M queries/month.
Average query & answer = 1,000 tokens.
On GPT-4o-mini → $150/month.
On GPT-4 → $30,000/month.

Same queries. Same customers. But $29,850 of “AI tax” each month.

📉 The Hidden Trap of Scaling Blindly
This is why “bigger model = better results” is a dangerous oversimplification.

Scaling without strategy can:

Burn budgets (AI bills growing faster than revenue).
Add latency (customers waiting 5+ seconds per answer).
Hurt ROI (extra cost may not mean happier customers).

ShopEase realized: instead of jumping to a mega-model, they could fine-tune a medium model with their support transcripts for far cheaper — and better aligned to their domain.

✅ Key Takeaway

Parameters = capacity (how much the AI can "know").
Tokens = cost (every interaction charges you).
Bigger ≠ automatically better.

If you don’t understand these two levers, your AI project isn’t a strategy - it’s a gamble.

👉 Coming next in this series: "Small vs Medium vs Large Models: The Trade-Offs That Matter."

Have you ever faced the “bigger vs cheaper” AI debate in your org? Did you go for scale or optimize what you had? Drop your story 👇

Renting GPT vs. Building Your Own AI: The True Cost of Chatbots

Kamal Rawat — Wed, 27 Aug 2025 06:37:48 +0000

AI feels like magic until you get your first bill.

When teams discuss whether to rent a general-purpose LLM (like GPT, Gemini, or Claude) or build their own smaller domain-specific model, the conversation often gets stuck on price tags and technical complexity. But there’s another critical detail that many articles gloss over: general LLMs don’t magically know your company’s data. If you want them to answer real product or order questions, you have to wire them into your systems.

This blog takes a clear look at both paths, using the same example of retail chatbot answering "Where’s my order?"—to highlight the tradeoffs.

Option A: Renting General-Purpose LLMs

At first glance, this feels like the easy button. You call GPT or Gemini’s API, pass in a customer question, and get a natural-language answer. But here’s the reality:

They don’t know your data out of the box

GPT has no access to your product catalog, your order database, or your policies.
If a customer asks "Where’s my order?" and you just pass that raw text to GPT, it will respond generically:

"You can usually track your order on the company’s website."

Clearly, that’s not useful.

How companies make it work

To bridge the gap, teams layer in one (or both) of these approaches:

1. RAG (Retrieval-Augmented Generation)

At runtime, your backend retrieves the needed info (e.g., from your order system).
Example flow:
- User: "Where’s my order #12345?"
- Backend queries DB → Order #12345: in transit, delivery tomorrow.
- This context is inserted into the GPT prompt:
```
Customer asked: "Where’s my order #12345?"
Order system response: "In transit, delivery expected tomorrow."
Respond politely.
```
- GPT outputs: "Your order #12345 is on the way and should arrive tomorrow."

👉 GPT didn’t "know" your data. You injected it just-in-time.

2. Fine-tuning / Custom Training

You can fine-tune GPT on your company’s FAQs, chat transcripts, and policies.
This ensures consistent tone and brand voice.
But: fine-tuning still doesn’t give live access to customer data—you still need APIs or RAG for dynamic info.

Let’s do the math:

Say your chatbot processes 2 million tokens per day (1.2M input, 0.8M output).

 Input: 1.2M × $75 / 1M = $90/day

 Output: 0.8M × $150 / 1M = $120/day

 Total = $210/day ≈ $6,300/month

Benefits

No infra to manage.
Constantly updated model quality.
Fastest path to a working chatbot.

Option B: Building Your Own Domain Model

This is the opposite extreme: you train a small foundation model (say 7B parameters) on your own data + domain knowledge.

Why it’s attractive

You own the weights → no per-call API fees.
You can bake in domain knowledge deeply.
Potentially cheaper long-term if usage is massive.

What it takes

1. Data preparation

Collecting, cleaning, and labeling product info, chat history, policies.
Cost can hit hundreds of thousands if annotation is manual.

2. Training infra

A 7B parameter model needs multiple A100/H100 GPUs running for weeks.
Infra costs can run into millions depending on training scale.

3. Inference Infrastructure

Once trained, you still need GPU servers to host it.
Each customer query requires an inference, which adds to your power consumption and can increase latency.

4. Maintenance

You’re now responsible for updates, bias fixes, safety, scaling.

Benefits

Total control.
No API vendor lock-in.
Can fine-tune deeply for efficiency.

Costs

Initial build: high (millions).
Ongoing hosting: significant.
Only makes ROI sense at very high scale.

Comparing the Two Approaches

Factor	Renting GPT/Gemini	Building Own Domain Model
Access to your data	Needs RAG/fine-tuning integration	Fully embedded during training, but still needs APIs for live data
Cost model	Pay per token	Pay upfront infra + ongoing GPU costs
Time to deploy	Days/weeks	Months/years
Control	Limited	Full
Best for	Startups, mid-size orgs	Hyperscale, regulated industries

The Key Takeaway

If you need a chatbot to answer "Where’s my order?", GPT won’t magically know. You either:

Inject the live order data (RAG),
Or train/fine-tune it on your policies.

That’s why many companies start with Option A (renting), it’s pragmatic and fast. But if your volumes explode, costs spiral, or compliance requires self-hosting, Option B becomes worth considering.

Final Word

The debate isn’t really LLM vs. custom model. It’s about how you balance cost, control, and time-to-market. Smart teams often start with renting, layer in RAG/fine-tuning, and only move to building their own once the business case is undeniable.

Forem: Kamal Rawat

AI Models: Small vs. Large - Choosing the Right Scale for ROI

AIstrategy #BusinessLeader #LLM

AI Models Demystified: What Really Happens Inside an AI Model?

Renting GPT vs. Building Your Own AI: The True Cost of Chatbots

Option A: Renting General-Purpose LLMs

They don’t know your data out of the box

How companies make it work

Let’s do the math:

Benefits

Option B: Building Your Own Domain Model

Why it’s attractive

What it takes

Benefits

Costs

Comparing the Two Approaches

The Key Takeaway

Final Word

✍️ That’s my breakdown. Curious, if you were building that retail chatbot, would you rent GPT forever or take the plunge on your own model?