AI Answer Library
Short answer
You do not have to pick one for the whole company — decide per workload, by data sensitivity and task difficulty. Where data cannot leave the network and volume is high and steady, run a self-hostable open family (Llama 4, Qwen 3.x, DeepSeek V4, GLM-5.x). Where you need the strongest reasoning, or volume is low and the use case is still unproven, call a closed API (GPT-5, Claude 5 and peers). Most organisations land on a hybrid: a gateway routes by task, and both sides stay replaceable.
Leaderboard gaps frequently fail to reproduce in production — whether your task is structured extraction or open-ended reasoning, whether the corpus is Chinese or mixed-language, and how long the context runs will all reorder the ranking. The safer method is to run your own evaluation set on both, then use the non-capability dimensions below to see which fits your constraints.
| Dimension | Open weights, self-hosted | Closed API | Hybrid |
|---|---|---|---|
| Data leaving your network | Stays inside; fully under your control | Leaves; vendor terms and compliance posture must be reviewed | Route by classification; sensitive content stays in |
| Capability ceiling | Bounded by your memory budget and open-model progress | Higher — flagship capability usually lands here first | Hard problems to the API, routine work to the local model |
| Cost structure | Large upfront outlay, marginal cost near electricity | Zero upfront, linear per-token growth | Steady high volume local, long tail to the API |
| Vendor lock-in | Low — you hold the weights and nothing gets deprecated | Real — repricing, rate limits and version retirement all apply | A gateway abstraction lets you switch at will; lowest exposure |
| Depth of customisation | Quantise, fine-tune and modify the serving stack freely | Limited to whatever the vendor exposes | Keep the deeply customised workloads local |
| Operational burden | High — monitoring, upgrades and scaling are yours | Low — the vendor owns availability | Medium, plus one gateway to maintain |
Three situations leave little to debate. One, a hard compliance constraint: data cannot leave your network or your jurisdiction, which makes this a feasibility question rather than a cost question. Two, high and steady volume — an assistant everyone uses daily, or a bulk document pipeline — the load profile that amortises hardware while per-token billing bleeds continuously. Three, a need for long-term determinism, where a single upstream repricing, rate limit or version retirement would force you to re-adapt and re-evaluate. There is also an easily overlooked case: workloads that need the model itself modified — compensating for quantisation loss in a narrow domain, or reworking the serving stack for domestic accelerators — which is only possible with the weights in hand.
While the use case is still unproven, use an API. You do not yet know whether anyone will use the feature, what the real call volume is, or how long the average input and output run — and those are exactly the inputs a self-hosting decision requires. Two months of real traffic on an API gives you the numbers; buying GPUs first and then hunting for a use case does not. Three other situations lean the same way: genuinely hard tasks needing the strongest current reasoning or multimodal ability; low, spiky volume where the hardware idles more than it works; and no one on the team able to operate an inference service long-term — an on-premise deployment without monitoring, staged rollout and rollback is often less available than a public API.
Where this applies
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