“Open-source is cheaper” and “proprietary models are more capable” are both half-true enough to cause real budgeting mistakes. The actual trade-off depends on usage volume, in-house ML infrastructure, and how much your use case needs the frontier capability proprietary labs ship first.
Proprietary APIs are cheaper at low volume; self-hosted open-source models become cheaper once usage volume is high enough to amortize infrastructure cost.
Proprietary frontier models typically lead on complex reasoning and multi-modal tasks; open-source models have closed much of the gap on standard tasks.
Open-source models can run entirely within your own infrastructure; proprietary models mean your data and prompts traverse the vendor’s API.
Open-source model capability has closed enough of the gap with proprietary frontier models that “just use the best proprietary model” is no longer an automatic default — but the decision genuinely depends on usage volume, in-house ML infrastructure, and how much your specific use case needs frontier-level capability rather than merely strong-enough performance. Treating this as a single company-wide choice usually means overpaying at scale for a proprietary API, or under-delivering on a hard reasoning task by defaulting to a lighter open-source model to save on infrastructure.
| Dimension | Open-Source LLMs | Proprietary LLMs |
|---|---|---|
| Cost at low volume | Self-hosting infrastructure cost dominates — often more expensive than a proprietary API for low-volume use. | Pay-per-token API pricing scales down naturally — cheap at low volume with no infrastructure investment. |
| Cost at high volume | Infrastructure cost amortizes across volume — can become significantly cheaper than per-token API pricing at scale. | Per-token cost scales linearly with volume — can become the larger cost at high usage. |
| Capability ceiling | Has closed much of the gap on standard tasks; still typically behind frontier proprietary models on the hardest reasoning and multi-modal tasks. | Frontier labs generally ship the most capable models first, especially for complex reasoning and multi-modal tasks. |
| Data residency & control | Full control — can run entirely within your own infrastructure, with full visibility into model behavior. | Data and prompts traverse the vendor’s API; data-handling terms vary by vendor and plan. |
| Operational burden | Requires in-house ML infrastructure and operational expertise to deploy, serve and maintain. | No infrastructure to manage — the vendor handles hosting, scaling and updates. |
| Best fit | High-volume use cases, strict data residency requirements, or a need for deep model customization. | Low-to-moderate volume use cases, fastest access to frontier capability, teams without ML infrastructure. |
Most mature enterprise GenAI programmes use both — a proprietary frontier model for tasks that genuinely need the strongest available reasoning or multi-modal capability, and a self-hosted open-source model for high-volume, standard tasks where the cost crossover favors infrastructure investment. Picking one model family for every use case usually means overpaying for simple tasks or under-serving the hardest ones.
Start by mapping your actual use cases against two axes: expected volume, and how close to frontier capability the task genuinely needs. High-volume, standard-capability tasks are strong open-source candidates once you have, or can build, the infrastructure to serve them. Low-to-moderate volume tasks, or tasks needing frontier-level reasoning, are usually cheaper and faster to ship on a proprietary API. Re-evaluate the crossover point periodically — both the open-source capability ceiling and proprietary pricing continue to shift.
No — it’s typically cheaper only above a certain usage volume, once infrastructure cost amortizes; at low volume, a proprietary API is usually the lower-cost option.
On many standard tasks, yes — the gap has closed significantly, but proprietary frontier models still generally lead on the hardest reasoning and multi-modal tasks.
The ongoing engineering and infrastructure cost to deploy, serve, monitor and update the model — easy to underweight against a simple per-token cost comparison.
Not necessarily — most mature programmes use proprietary models for tasks needing frontier capability and open-source models for high-volume standard tasks, matched to each use case’s actual needs.