When a business decides to add AI to a product or process, one of the first technical choices is where the model runs. You can call a commercial model through an API and pay per use, or you can take an open weight model, meaning one whose trained weights are published, and run it on your own servers or rented GPUs.
Both are valid. The right answer depends on volume, data sensitivity, the quality you need and, most often overlooked, who will maintain the system.
What “open weight” means
An open weight model is one you can download and run yourself. You control the hardware, the data never needs to leave your environment, and nobody can change or retire the model under you. Open weight is not always the same as fully open source. Licences vary, and some restrict commercial use above a certain scale, so read the licence before building on one.
An API model is hosted by the provider. You send text, you get text back, and you pay per token processed. You do not see or manage the model itself.
Comparing the two honestly
Quality
The strongest commercial models generally still lead on the hardest reasoning, long documents and complex tool use. Open weight models have closed much of the gap, and for focused tasks like classification, extraction, summarisation or answering from your own documents, a good open model is often entirely sufficient.
Cost at low volume
APIs win clearly. You pay only for what you use and there is no idle hardware. For a feature used a few thousand times a month, self hosting almost never makes financial sense.
Cost at high volume
This is where self hosting can win. If you process large, steady volumes around the clock, a dedicated GPU running continuously can cost less per request than the API. The key word is steady. A GPU sitting idle overnight is money burned.
Privacy and data control
Self hosting keeps data entirely in your environment, which matters for hospitals, banks, legal work and government projects. That said, major API providers offer business terms that exclude your data from training and limit retention. Check whether those terms satisfy your actual obligations before assuming self hosting is required.
Latency
A model hosted close to your users can be fast and predictable. APIs are usually fast, but you share infrastructure and occasionally feel it during peak demand elsewhere in the world.
Maintenance
This is the cost most teams underestimate. Self hosting means someone owns GPU provisioning, scaling, monitoring, security patches, model upgrades and failures at night. With an API, the provider carries all of that.
A simple decision guide
| Situation | Usually better |
|---|---|
| Prototype or early product | API |
| Low or unpredictable volume | API |
| Hardest reasoning or long, complex documents | API (top tier model) |
| Strict rules that data cannot leave your servers | Open weight, self hosted |
| High, steady volume on a narrow task | Open weight, self hosted |
| No in house infrastructure skills | API, or a managed host for open models |
The middle path
You do not have to choose one forever. Many teams use a hybrid:
- Route by difficulty. A small open model handles routine requests, and only the hard ones go to an expensive API model.
- Route by sensitivity. Anything containing personal or confidential data goes to the self hosted model, everything else to the API.
- Use managed open model hosting. Several providers run open weight models for you at a per token price, giving you model choice without owning GPUs.
Designing your code so the model is behind one internal interface makes switching later cheap. That single decision protects you from price changes, model retirements and new better options.
Common mistakes
- Buying GPUs before proving the feature. Validate with an API first, then optimise cost once usage is real.
- Comparing API price to GPU rental alone. Add engineering time, monitoring, redundancy and the cost of downtime.
- Assuming an open model needs fine tuning. Good retrieval and clear prompts solve most business tasks without training anything.
- Ignoring licences. Some open weight licences have commercial conditions that matter as you grow.
A worked cost comparison
Numbers make the decision concrete. The figures below are illustrative placeholders to show the method, not current prices. Always use your provider’s current rates and your own measurements.
Suppose a document classification feature processes 200,000 requests a month. Each request averages 1,500 input tokens and 200 output tokens.
API route
Monthly input volume is 300 million tokens and output volume is 40 million tokens. Multiply each by the provider’s price per million tokens for a suitable model, add a margin for retries and growth, and you have a monthly cost that scales directly with usage. There is no hardware, no maintenance time, and cost drops automatically if volume falls.
Self hosted route
A smaller open weight model on one rented GPU server might handle this load if requests are spread evenly. The monthly cost is the server rental, plus a second server or fallback for reliability, storage, monitoring, and engineering time for setup and ongoing maintenance. This cost is roughly fixed whether you process 50,000 or 200,000 requests.
Comparing
Plot both costs against monthly volume. The API line rises steadily with usage. The self hosted line starts high and stays flat until you need more capacity. Where the lines cross is your break even point. If your volume sits well below it, the API is cheaper. If you are well above it and the smaller model meets your quality bar, self hosting saves money. Remember to include engineering time honestly, because it is the cost most often left out.
Testing quality before you decide
Cost only matters if quality is acceptable. Run a fair comparison:
- Build an evaluation set of real inputs with expected outcomes. See how to test an AI feature before launch.
- Run the same prompts and retrieval setup through each candidate model.
- Score accuracy, format compliance, handling of Urdu and Roman Urdu if relevant, and failure behaviour.
- Measure speed under realistic load, not single requests.
- Choose the cheapest option that clears your quality bar, not the best scoring model regardless of cost.
Self hosting operational checklist
| Area | What you need |
|---|---|
| Serving | An inference server optimised for batching and memory efficiency |
| Capacity planning | Measured requests per second per GPU at acceptable latency |
| Redundancy | Fallback server or API route when the primary fails |
| Monitoring | Latency, error rates, GPU memory and utilisation, queue length |
| Security | Private network access, authentication, patching, no public exposure |
| Updates | Process for testing and rolling out new model versions |
| Cost control | Autoscaling or scheduled shutdown for uneven workloads |
| Licensing | Documented licence terms for the chosen model |
If most rows on this list have no owner in your team, managed hosting or an API is the safer choice. See cloud and DevOps for the infrastructure side.
Data residency and regulated sectors
Banks, hospitals, telecoms and government related projects in Pakistan may face requirements about where data is stored and processed, or strict client contracts. These requirements should be confirmed with compliance and legal teams rather than assumed. Options include self hosting within approved infrastructure, using providers with suitable regional hosting where available, sending only anonymised or minimised data to external APIs, and routing sensitive workloads differently from general ones. See data protection for apps in Pakistan.
Avoiding lock in regardless of choice
- Abstract the model call behind one internal service so changing models changes one place in the code.
- Keep prompts and evaluation sets versioned so you can retest quickly with a new model.
- Avoid depending on one provider’s unique features unless they deliver clear value.
- Store your own logs and outcomes, which become the data for improving or fine tuning later.
Models improve and prices change every few months. Teams that can switch in days rather than months benefit most from that progress.
Frequently asked questions
Can open models understand Urdu?
Some handle Urdu and Roman Urdu reasonably, but quality varies a lot. Test with your own real messages. We cover this in building AI products for Urdu and Roman Urdu.
How do I estimate API costs?
Estimate average input and output length per request, multiply by monthly volume and the provider’s token price, then add a safety margin. Our breakdown of what running an AI feature costs shows the calculation step by step.
Is self hosting more secure?
It keeps data in your control, but only if your own servers are well secured. A poorly secured self hosted model can be riskier than a reputable API.
Can we start with an API and move to self hosting later?
Yes, and it is usually the smartest path. The evaluation set and logs you build with an API make the later switch much safer.
Does fine tuning require self hosting?
Not necessarily. Several API providers offer fine tuning. Many business tasks, however, do well with retrieval and good prompts without fine tuning at all.
The bottom line
Start with an API to learn what your users actually need. Move specific workloads to open weight models when volume, cost or data rules justify the extra engineering. Keep the model swappable either way.
Our AI solutions team helps businesses pick, deploy and cost AI models for real workloads, including self hosted setups on our cloud and DevOps practice.