Open-weight vs closed AI models: which one for your Takealot store (and when to switch)

What "open-weight" and "closed" actually mean (without the jargon)

You've heard people say Llama is "open-source" and Claude is "closed." Then someone else says Llama isn't really open-source, it's open-weight. Then a third person jumps in with "but DeepSeek is Apache 2.0, so that's different from Llama's licence."

Here's what matters if you sell candles or phone cases on Takealot: open-weight models let you see the code and run them on your own hardware if you want. Closed models don't. You send your prompt to Claude or Gemini through an API, they send back an answer, and you never see what's inside. With Llama 3.3 70B or DeepSeek V3, Meta and DeepSeek published the model weights—the file that contains the trained AI—so anyone can download it, inspect it, or host it themselves.

Does that mean you should host Llama on a server in your spare room? Almost certainly not. But it does mean you have the option, and it means other companies can offer Llama or Mistral through their own APIs at prices that undercut OpenAI or Anthropic. Kryotta gives you both types in one workspace, so you can pick the right model for the job without signing up for six different accounts.

The practical difference shows up in three places: cost, privacy, and what happens when Eskom decides it's stage-four day.

The comparison table: what you actually get with each type

I've been using both types for my own Shopify store—handmade soap and skincare—for eight months. The closed models still win on tone and reliability when I need a product description that sounds like a human wrote it and doesn't invent ingredients. The open-weight models win on cost and speed when I'm generating fifty SKU titles in one sitting and I just need clean, factual text.

When closed models (Claude, Gemini) make sense for your Takealot store

Use Claude Sonnet 4.5 or Gemini Pro when the task is customer-facing and needs to sound right the first time. I use Claude for writing product descriptions that'll sit on Takealot for months. A bad description costs me sales every day it's live. Claude understands "make this sound luxurious but not pretentious" and "mention the ingredient benefits without sounding like a supplement label." Llama can do it, but I spend more time editing.

Use closed models when you're iterating on something sensitive or complex. I once asked Claude to draft a refund email for a customer whose order arrived melted (summer heat, courier left it in the sun). The reply needed to apologise, offer a replacement, and gently explain our shipping terms without sounding defensive. Claude nailed it. I tried the same prompt with Llama 3.3 70B and got something that worked, but felt slightly robotic—more "we regret the inconvenience" than "that's frustrating, let's fix it."

Use them when your budget has room and you want fewer surprises. If you're running a promotion and need twenty WhatsApp auto-replies written by Friday, the extra R2 per description is worth it if it means you're not rewriting half of them. I've had DeepSeek occasionally ignore an instruction or add a sentence I didn't ask for. It's rare, but it happens. Claude is more consistent.

The downside is cost. If you're generating 500 product titles a month—maybe you're dropshipping or you have a catalogue that changes weekly—Claude at R3 per 1,000 tokens adds up. That's R1,500/month just for titles. Llama at R0.30 per 1,000 tokens is R150. That difference buys you an extra Yoco device or pays your Shopify plan.

When open-weight models (Llama, DeepSeek, Mistral) are the better choice

Use Llama 3.3 70B or DeepSeek V3 when you need volume and the output is easy to check. I use DeepSeek for SEO meta descriptions, product tags, and SKU codes. The task is simple, the format is predictable, and if it gets one wrong I'll catch it when I skim the CSV before uploading. I generated 300 meta descriptions last month for R80 in credits. The same job with Claude would've cost R900.

Use open-weight models when you're experimenting. When I was testing whether AI could write decent Instagram captions for my soap photos, I didn't want to burn R500 finding out the answer was "sort of, but they all sound the same after the third one." I ran fifty prompts through Llama, learned what worked, then used Claude for the final batch I actually posted. The cheap model let me fail faster.

Use them when load-shedding or internet reliability is a real constraint. I know a guy in Johannesburg who sells phone accessories on Takealot and runs his product descriptions through a local Llama instance on a refurbished server. When Eskom cuts power to his neighbourhood, he's on a UPS and a 4G dongle, but his AI workflow doesn't depend on a stable connection to a US data centre. He generates descriptions in batches during stage-two days and uploads them when the power's back. You need some technical skill to set that up, but it's possible. You can't self-host Claude.

Use them when you're handling data you don't want leaving South Africa. If you're using AI to summarise customer support tickets that include ID numbers, addresses, or payment details, open-weight models let you (or your developer) route everything through a local server or a provider with a data residency guarantee. Anthropic and Google say they don't train on your API data, and I believe them, but "we don't train on it" isn't the same as "it never touches a server in California." For most Takealot sellers this doesn't matter. For some—especially if you're in finance, health, or legal—it does.

The verdict: I use both, and you probably should too

I don't think this is an either/or question. I use Claude for product descriptions, refund emails, and anything a customer will read. I use DeepSeek or Llama for meta descriptions, tags, internal notes, and rough drafts I'll edit anyway. Kryotta lets me switch models mid-conversation, so I'll start with Llama to brainstorm ten headline options, then ask Claude to polish the two I like best.

The mistake I see other sellers make is picking one model and using it for everything because they don't want to learn a second interface. That's like owning one knife and using it to chop vegetables, carve meat, and open boxes. It works, but you're making life harder than it needs to be.

If you're just starting, begin with an open-weight model—Llama 3.3 70B is fast, cheap, and good enough for most tasks. Use it until you hit a job where the output feels off or you're spending too long editing. Then try Claude Sonnet 4.5 for that specific task and compare. You'll know within three prompts whether the quality jump is worth the cost.

If you're already using Claude for everything and your monthly bill is making you wince, move your high-volume, low-stakes work to DeepSeek V3. Keep Claude for the tasks where tone and nuance matter. I cut my AI spend by 60% doing this, and the quality of my customer-facing content didn't change.

What doesn't work (so you don't waste time testing it yourself)

Don't use open-weight models for legal or financial advice. I tried asking Llama to explain VAT rules for digital products sold to EU customers. It gave me an answer that sounded confident and was about 70% correct. The 30% that was wrong would've cost me a penalty if I'd followed it. Claude got it right, but even then I checked with my accountant. AI is a research assistant, not a lawyer.

Don't assume "open-weight" means "safe to share anything." If you're using Llama through an API (which most people are), your data is still going to someone's server. Read the terms. Some providers log prompts for abuse monitoring. If you need true privacy, you have to self-host, and that's a weekend project with a learning curve.

Don't use closed models for tasks where you need the output to be deterministic. If you're auto-generating SKU codes and you need them to follow an exact format every time—no variation, no creativity—a simple script or a spreadsheet formula will beat any AI model. Claude might give you SKU-2024-001 one day and SKU_2024_001 the next. Llama does the same. AI is probabilistic. It's not a templating engine.

Questions people ask

Can I use Llama or DeepSeek offline during load-shedding?
Technically yes, if you download the model and run it on your own hardware—you'll need a machine with at least 16GB of RAM for smaller models, or access to a local server. Most people don't bother; they just use the API when the power's back. But if load-shedding is killing your workflow and you have a UPS, self-hosting is an option.

Is my Takealot customer data safe if I use Claude to write replies?
Anthropic says they don't train on API data, and their terms are clearer than most. But your prompts do go to their servers. If you're pasting in full customer emails with addresses or ID numbers, redact the sensitive bits first. Or use an open-weight model through a provider with a data residency guarantee.

Which model is cheapest for generating 500 product descriptions?
DeepSeek V3 or Llama 3.3 70B. At roughly R0.30 per 1,000 tokens, you'll spend around R150–R200 for 500 descriptions (assuming ~200 tokens per description). Claude Sonnet 4.5 would cost you R900–R1,200 for the same job. The quality gap is real but not ten-times-the-price real.

Do I need to learn Python to use open-weight models?
Not if you're using them through Kryotta or another workspace. You just pick the model from a dropdown and type your prompt. Self-hosting requires technical skill, but most sellers never need to do that.

I've been using Kryotta because it gives me Claude, Gemini, Llama, DeepSeek, and Mistral in one place—no juggling API keys or comparing prices in three currencies. If you're selling on Takealot or Shopify and you're tired of guessing which AI model to use, it's worth trying both types for a week and seeing where each one saves you time.