Run your own AI models only when it makes sense.
We set up open-weight language models on servers you control, for teams whose data cannot leave their walls or whose volume makes rented tokens too costly. We also tell you plainly when a hosted API is the cheaper and better choice. The work is delivered from Lahore.
When does running your own AI model beat calling an API?
Self-hosting wins in two situations: data that must stay inside your own infrastructure, and steady volume high enough that renting GPU time costs less than paying per token. Otherwise an API is usually cheaper and better once engineering time is counted. The real bill also includes monitoring, patching, model updates, spare capacity and the people who respond when something fails. Most teams should build against an API first, measure usage, and move later with evidence.
Hosted API or your own servers?
This is the first conversation we have. A fair comparison often shows the API as the right answer for now, and that is a useful result, because it saves you months of infrastructure work.
| Criteria | Self-hosted model | Hosted API |
|---|---|---|
| Data location | Stays on your servers | Sent to a provider |
| Starting effort | Weeks of setup | Hours to first call |
| Cost pattern | Fixed capacity, fixed bill | Pay per use |
| Best quality | Good for narrow tasks | Strongest on hard tasks |
| Operations load | Your team or ours | Provider handles it |
| Switching later | Possible, with tests | Possible, with tests |
Situations where self-hosting is the right call.
Three reasons come up again and again. A fourth, curiosity, is not enough to justify the work.
The cases we support
- Data cannot leave. Contracts, regulation or client promises may forbid sending records to an outside provider. A private model solves that cleanly.
- Steady high volume. Bulk classification, extraction or summarising can reach a point where owned capacity costs less than metered tokens.
- Predictable cost. A fixed monthly bill is easier to budget than usage that grows with success, as long as the servers stay busy.
- Offline or remote sites. Factories, ships and field offices with poor internet need a model that works without a constant connection.
- Specialised tuning. A small model adjusted to your vocabulary and format can beat a general one on one narrow task.
If your need is answers from documents rather than a private model, look at RAG development first.
The parts of a self-hosted setup.
The model is only one piece. These are the parts we choose, install and document, so your own staff can run them after hand-over without special knowledge.
Open-weight models
Families such as Llama, Qwen, Mistral and DeepSeek, picked by task, language support, size and licence terms.
Inference engines
vLLM or Text Generation Inference for shared production servers, llama.cpp and Ollama for small or on-device use.
GPU hosting
Rented cloud GPUs, a dedicated server or hardware in your office, sized from measured load and not from guesses.
Quantisation
Compressed model versions that fit smaller GPUs, tested so the quality loss is known before you accept it. A smaller file is only a saving if answers stay good enough.
API gateway
An OpenAI-compatible endpoint with keys, rate limits and logging, so your apps can switch models easily and one team cannot use up capacity meant for another.
Monitoring
Dashboards for speed, queue length, GPU use, errors and cost per finished task, with alerts to a named person. Monitoring without a responder is only noise.
Checking whether an open model is good enough.
Open-weight models have improved a lot, but the gap on hard tasks is real. We measure on your work, not on public leaderboards, because leaderboard scores rarely match the shape of real business tasks.
How we test
- A test set from your data. We collect real examples with answers your team agrees on, so quality is a number everyone can read.
- Side-by-side runs. The same cases go through a hosted model and each candidate open model, with cost and speed recorded.
- Narrow tasks first. Classification, extraction, routing and summarising usually suit smaller models. Open-ended reasoning often does not.
- Language checks. If you serve Urdu, Arabic or other languages, we test those directly, since model quality varies widely between languages.
- A fallback path. Hard cases can be sent to a hosted model, if your data rules allow, so quality does not depend on one engine.
Picking which model fits can also depend on the product around it. See AI API integration for how that layer is built.
Six stages from question to production.
We keep the early stages cheap, so you can stop before buying hardware if the numbers do not support it. Many clients stop after stage two with a decision they trust.
- 01
Usage review
We measure your real token volume, peak times and data rules, from logs or a short trial on an API.
- 02
Cost model
A written comparison shows total cost of both paths, including staff time, spare capacity and monitoring.
- 03
Model trial
Candidate models are tested on your examples using rented GPUs for a short, capped period.
- 04
Build
Servers, engine, gateway, access control and logging are installed with infrastructure code you can reuse.
- 05
Harden
Network limits, secrets, backups, update rules and load tests are completed before real data arrives.
- 06
Hand over
Runbooks, dashboards and a support option let your team or ours keep the system healthy.
The costs that appear after launch.
A GPU rental line is only part of the bill. Teams who plan for the rest are rarely surprised.
What we plan for
- Idle capacity. Servers cost money when quiet. We size for typical load, add scaling where practical, and plan for peaks honestly.
- Updates. New model versions, engine releases and security patches need testing before they reach users.
- Incidents. Someone must respond when a GPU fails or a queue grows. We define who, how fast and what the user sees meanwhile.
- Security reviews. Exposed model servers have been targets before, so we keep ports closed and review settings on a schedule.
- Licence terms. Each open model has its own licence. We check it against your use, especially for commercial products and large audiences.
A common rule of thumb is that all-in cost lands well above the headline rental price, which is why we model it early.
What a private model does and does not protect.
Keeping data on your servers removes one risk. It does not remove the others, so we cover them too.
Protections we set up
- Network isolation. The model server sits behind private networking, with access only through the gateway and named service accounts.
- Logging with care. Prompts and outputs are logged for debugging only when needed, with masking and strict retention, since logs can leak data.
- Access rules. Users see only the documents and results they are allowed to, enforced before the model is called.
- Backups. Configuration, prompts and model files are saved so a failed server can be rebuilt in a known time, not guessed at.
- Output checks. Validation and review steps stay in place, because a private model can be wrong just as easily as a hosted one.
Self-hosting questions, answered.
Straight answers to what clients ask before they start.
Are open-weight models as good as ChatGPT or Claude?
On many narrow, well-defined tasks they are close enough to be a sensible choice. On hard reasoning and long, complex work, leading hosted models are usually stronger. We test on your data to find out.
What hardware do I need?
It depends on the model size, speed target and number of users. Small models run on a single modern GPU, and larger ones need several. We size it after testing, and often start on rented cloud GPUs so you can learn the real load before committing money to hardware you may not need.
Is a private model automatically secure?
No. It keeps data from leaving your control, but you must still secure the servers, the access rules, the logs and the application around it. We design all of these together, and we write down who is responsible for each one after launch.
Can you fine-tune a model on our data?
Sometimes, but it is rarely the first step. Better prompts, retrieval or a cleaner test set often give more value. If tuning is justified, we explain the data, cost and risks first, including how you will know the tuned model is actually better than the original.
What affects the price?
Model and hardware choice, the size of the test work, number of environments, security and compliance needs, integration effort and whether you want ongoing monitoring and support from us.
Which models do you work with?
Common open-weight families such as Llama, Qwen, Mistral and DeepSeek, plus smaller specialist models. We choose by task, language, size and licence, and we do not tie you to one vendor.
Can it run fully offline?
Yes, for suitable models and hardware. Offline setups need a plan for updates, model files and monitoring, which we include in the design.
What if we want to go back to an API later?
The gateway keeps the interface the same, so apps need little change. The test set lets you compare quality fairly, and nothing locks you in. We avoid custom formats and keep prompts, test sets and settings in files you own.
Find out if your own model pays for itself.
Share your use case, rough monthly volume and data rules. We will give you a plain comparison of an API and a private setup, including the costs people forget.