Most businesses should not run an LLM on-premise. Some must. The difference is usually data control, not cost.
This guide covers when self-hosting is justified in India, what the real cost looks like, and the hybrid option most teams overlook.
For the model options, see latest AI models. For compliance, see AI and the DPDP Act.
The three deployment options
| Option | Data control | Cost profile | Ops burden | Time to start |
|---|---|---|---|---|
| Vendor API | Low to moderate | Per token | Low | Hours |
| Private cloud or VPC | Moderate to high | Fixed plus usage | Medium | Days to weeks |
| On-premise | Highest | Capex plus power and staff | High | Weeks to months |
On-premise is not automatically cheaper. It trades a variable bill for a fixed one, and adds staff, power, cooling and replacement cycles.
When on-premise is justified
- Data cannot leave the premises under a contract, regulation or client requirement.
- The workload is high and stable enough that the hardware is cheaper over its life than per-token pricing.
- Latency or bandwidth makes a remote API impractical.
- The model must work offline or in an air-gapped environment.
- You need to fine-tune on data that cannot be sent to a vendor.
If none of those apply, a private cloud deployment or a vendor API with a zero-retention agreement is usually the better choice.
The cost framework
Do not start with a GPU price. Start with the monthly API bill you are trying to replace.
break-even months = fully loaded hardware cost ÷ monthly API spend saved
Fully loaded means:
- Servers and GPUs.
- Networking and storage.
- Power and cooling.
- Rack space.
- Staff time to run it.
- Refresh every three to four years.
- Utilisation, which is rarely 100%.
A model that needs a large multi-GPU cluster has a very different break-even from a small 7B or 8B model used for classification. Get quotes for both before assuming on-premise wins.
Which models fit
| Model | Size | Context | Hosted price | On-prem fit |
|---|---|---|---|---|
| Nemotron 3 Ultra | 550B | 262K | $0.60 / $2.40 | Large cluster only |
| Nemotron 3.5 Super VL | Not published | 1M | $0.15 / $0.50 | Mid-range, multimodal |
| GLM 5.3 | Not published | 1M | $0.07 / $1.40 | Mid-range |
| GLM 5.3 Flash | Not published | 1M | $0.15 / $0.50 | Smaller deployments |
Smaller open models are often enough for classification, extraction and retrieval. Reserve the large models for work that genuinely needs them.
The hidden costs
- Utilisation. A cluster sized for peak load sits idle most of the day.
- Staff. Someone has to patch, monitor and debug it.
- Power. GPUs draw serious current, and Indian commercial tariffs vary by state.
- Refresh. Hardware depreciates while API prices keep falling.
- Opportunity cost. Engineering time spent on infrastructure is time not spent on the product.
The last one is the most underestimated. API prices fell sharply in 2026; hardware does not.
Compliance in India
Under the DPDP Act, you need a lawful basis, purpose limitation, minimum necessary data, retention rules and a processor agreement. Running the model yourself removes the vendor processor, but it does not remove the obligation to secure the data.
On-premise helps with:
- Data residency.
- Access control.
- Audit logging.
- Retention enforcement.
It does not help with:
- Model accuracy.
- Prompt injection.
- Insider misuse.
- Backup and disaster recovery.
Plan for those separately. See AI security risks Indian businesses ignore.
The hybrid option
Most teams that think they need on-premise actually need a split:
- Sensitive data stays in a private environment with a small model.
- General work goes to a vendor API.
- A router decides which path a request takes.
This keeps the regulated data inside and the cost low. It is also easier to operate than a full cluster.
A decision checklist
- Can the data legally leave your environment?
- What is your monthly API spend today?
- How stable is the volume?
- Do you have staff to run the hardware?
- How will you handle failover and backups?
- What is the refresh cycle and who signs off?
- Is a private cloud or hybrid enough?
If you cannot answer 2, 3 and 4, you are not ready to buy hardware.
FAQ
Is on-premise cheaper than an API?
Only at sustained high volume with good utilisation. For most businesses, API pricing is lower once staff and idle capacity are counted.
Can I meet DPDP without on-premise?
Yes. A vendor agreement with zero data retention, a private cloud deployment and strong access controls can satisfy the requirement.
Which open model should I start with?
A smaller model for classification and extraction, and a larger one only if quality demands it. Test both on your own tasks.
Do I need a GPU cluster?
Only for large models or high concurrency. Smaller models run on far less hardware.
Next step
If you are weighing on-premise against cloud for an AI workload, contact 24Bit System. Our cloud services team handles private deployments, and best open source AI models covers the model choices.
