Short answer:Closed LLMs (GPT-5, Gemini) are cloud-only APIs — easiest and most capable, but you rent them and send data to the provider. Open-weight LLMs (Llama, DeepSeek) can be downloaded and self-hosted — more control, privacy, and lower long-run cost, but you manage the hardware.
If you're building anything with AI in India, this is the first fork in the road: use a closed model through an API, or run an open-weight model yourself? The choice affects your bill, your data privacy, and how much you can customise. Let's break it down with GPT-5, Gemini, Llama, and DeepSeek as the examples.
What does open-weight vs closed actually mean?
Closed LLM — you can't download the model. You call it over an API and the provider runs it (e.g. GPT-5, Gemini).
Open-weight LLM — the trained model weights are published, so you can download and run them yourself (e.g. Llama, DeepSeek).
Open-source — a stricter term meaning weights and training details are both open; many 'open' models are actually only open-weight.
Self-hosting — running an open-weight model on your own hardware or rented GPU.
How do GPT-5, Gemini, Llama, and DeepSeek compare?
Model
Type
Runs where
Best at
Data leaves your control?
GPT-5
Closed
Cloud API only
General reasoning, tools
Yes (sent to provider)
Gemini
Closed
Cloud API only
Search, Google ecosystem
Yes (sent to provider)
Llama
Open-weight
Cloud or self-host
Customising, on-prem apps
No, if self-hosted
DeepSeek
Open-weight
Cloud or self-host
Cost-efficient reasoning/code
No, if self-hosted
Which is cheaper for an Indian business or developer?
Pros
Closed APIs have zero upfront cost — pay per use
No GPU or DevOps team needed
Open-weight models are free to download and run
Self-hosting can be far cheaper at high, steady volume
Cons
Closed API bills grow with every request — costly at scale
Open-weight needs you to handle updates and uptime
Idle self-hosted GPUs still cost money
Simple rule: Low or unpredictable volume → closed API is cheaper. High, steady, predictable volume → self-hosted open-weight usually wins on cost over time.
When should I choose open-weight over closed?
Data privacy — sensitive data (health, finance, legal) that can't leave your servers.
Customisation — you need to fine-tune the model on your own domain or language data.
Predictable cost — high steady volume where a fixed GPU bill beats per-call pricing.
Offline / on-prem — apps that must run inside your own network or a local device.
python
# Closed LLM: a few lines, provider runs everything.
response = client.chat(model="gpt-5", messages=[{"role": "user", "content": "Summarise this invoice in Hindi."}])
print(response.text)
# Open-weight (self-host) needs more setup first:
# 1) Rent a GPU server.
# 2) Download the model weights (Llama / DeepSeek).
# 3) Serve it with a runtime, then call your own endpoint.
Is my data safe with each option?
'Open-weight' does not automatically mean private. If you run Llama or DeepSeek through a third-party cloud, your data still passes through that provider. Privacy only holds when you control the server.
Frequently asked questions
Is open-weight the same as open-source?
Not exactly. Open-weight means the model weights are downloadable, but the training data may not be public. True open-source means weights and training details are both open. Many popular 'open' models are only open-weight.
Are Llama and DeepSeek free to use?
The weights are free to download and run under their licences, so there's no per-call fee if you self-host. But you pay for the hardware or GPU rental and the engineering time to run them reliably.
Which is better for a startup in India?
Start with a closed API like GPT-5 or Gemini to build and validate fast with no infrastructure. Move to a self-hosted open-weight model later if data privacy, customisation, or cost at scale demands it.
📊 At a glance
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AICreatorHub Team
Hands-on AI practitioners covering tools, models and news for India.
