Open-Weights LLMs vs
Proprietary LLM APIs.
Start with a hosted API unless a specific constraint forbids it. You get frontier capability, no operational burden, and immediate iteration. Move to open-weights models when data residency or regulation prohibits third-party processing, when volume makes per-token pricing more expensive than running your own hardware, or when you need version stability that no provider will guarantee.
Side by side
| Dimension | Open-Weights LLMs | Proprietary LLM APIs |
|---|---|---|
| Peak capability | Strong and closing, generally behind frontier | Frontier |
| Time to first prototype | Days — infrastructure required | Minutes |
| Data control | Complete — runs in your environment | Contractual; data leaves your boundary |
| Cost at low volume | Poor — you pay for idle GPUs | Excellent — pay per token |
| Cost at high volume | Better — fixed hardware, unlimited calls | Scales linearly, can become the largest line item |
| Version stability | Total — the weights do not change | Provider may deprecate or alter models |
| Fine-tuning freedom | Full, including deep customisation | Limited to what the provider offers |
| Operational burden | Yours: GPUs, scaling, monitoring, upgrades | Essentially none |
Why hosted APIs are the right default
The capability gap has narrowed considerably but has not closed, and on the hardest reasoning and long-context tasks frontier hosted models remain ahead. More importantly for most teams, the operational difference is enormous: an API call versus provisioning GPUs, configuring an inference server, handling batching and autoscaling, and owning the upgrade path.
That matters most early, when you do not yet know whether the feature works. Spending three weeks on serving infrastructure before validating that users want the thing is a common and avoidable mistake. Prototype against an API, prove value, and then evaluate whether self-hosting is worth the burden.
The three constraints that force open weights
Data control. The most common driver. Regulated healthcare, financial, legal, and government workloads may prohibit sending data to a third-party processor, or make the contractual due diligence so onerous that running in your own tenancy is simpler. Note this is often satisfiable with a hosted provider under the right agreement — check before assuming it is not.
Unit economics at volume. Per-token pricing is excellent until it is not. High-volume, narrow, repetitive tasks — classifying millions of records, extracting fields from large document sets — can cost more per month in API calls than dedicated hardware. The crossover depends on your traffic shape, so calculate it rather than guessing.
Version stability. Providers deprecate and update models, and behaviour shifts when they do. If you have validated a clinical or financial workflow against specific model behaviour, self-hosted weights that never change without your action may be a compliance requirement rather than a preference.
What to decide before you build either
Two pieces of work make this choice reversible and should come first. Build an evaluation set of real inputs with known good outputs, so you can measure any model against your actual task rather than a public benchmark — this is what lets you test whether a smaller open model is good enough for your use case. And put a provider abstraction between your application and the model, so switching is a configuration change rather than a rewrite.
With those in place, the sensible sequence is: prototype on a hosted API, measure what capability you actually need, then test whether a smaller or self-hosted model clears that bar at better cost. Many teams discover their task does not need a frontier model at all — which is the same lesson as in NLP more broadly, where small fine-tuned models often beat large general ones on fixed, high-volume work.
Hybrid deployments are common and sensible: an open-weights model handling bulk routine traffic in your own environment, escalating the hard minority of cases to a frontier API.
Open-Weights LLMs vs Proprietary LLM APIs — questions.
Are open-weights models good enough for production?
For a great many tasks, yes. Classification, extraction, summarisation, and retrieval-grounded answering are handled well by current open models, particularly after light fine-tuning on your data. The gap persists on the hardest reasoning, long-context, and agentic tasks. The only reliable way to answer for your case is to run both against your own evaluation set — public benchmark rankings correlate loosely with performance on any specific task.
What does self-hosting an LLM actually require?
GPU capacity sized to your model and peak concurrency, an inference server such as vLLM to handle batching and memory efficiently, autoscaling or capacity planning, monitoring for latency and throughput, and an owned upgrade path when better weights are released. Budget for the engineering time, not just the hardware — for a small team that operational load is frequently the deciding factor against self-hosting, whatever the arithmetic on tokens says.
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ReadStill not sure
which to pick?
We make this call on production systems every week. Describe your constraints and we will give you a straight recommendation.
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