HopBaseOn-premise LLM vs API is not a question of which is cheaper in general; it comes down to three conditions: must the data stay on your premises, is your volume high and stable, and do you need to fine-tune. If none applies, pay-as-you-go API calls are almost always simpler and cheaper. If only the first applies, the usual answer is hybrid: sensitive data goes to a local open-weight model, everything else goes to an API. This article gives a neutral decision framework and cost formulas you fill in with your own numbers; it assumes no hardware prices. To be upfront: HopBase provides API access only and does not offer private deployment.
Cost structure at a glance
| Cost item | On-premise / private deployment | Pay-as-you-go API |
|---|---|---|
| Upfront | GPU servers, networking, storage, or long-term reserved compute | None; start with a prepaid balance |
| Fixed cost | Depreciation, power and hosting, ops and ML staff, paid even when idle | None |
| Variable cost | Low (mainly power) | Per token, pay for what you use |
| Utilisation risk | Yours: idle hours raise unit cost | Carried by the provider |
| Model upgrades | Download, test, tune, maybe new GPUs for each generation | Change one model field |
| Models available | Open-weight models only | Open-weight models plus closed models such as Claude, GPT and Gemini |
Cost formulas: plug in your own numbers
- Self-hosted monthly cost C = hardware cost ÷ depreciation months + monthly power and hosting + monthly ops/ML staff + monthly share of model upgrade and migration work.
- Self-hosted monthly output T = peak throughput (tokens/second) × seconds per month × utilisation rate. Utilisation is the swing factor: workloads that peak in office hours and idle at night usually land far below what teams expect.
- Self-hosted unit cost = C ÷ (T ÷ 1,000,000), the cost per million tokens.
- API monthly cost = Σ(input tokens × input price + output tokens × output price + cache hits × cache price). Prices are each vendor's official list price × your group discount; see the pricing page.
- Break-even: self-hosting wins on paper only when C ≤ the API bill for the same volume, and that volume holds steady until the hardware is fully depreciated.
Two items teams often leave out. Quality gap: if the open model you can run locally falls short, add the cost of rework or human review. Turnover cost: models change every few months while hardware depreciates over three to five years, and every migration in between takes staff time. For vendor list prices, see AI model API list prices compared.
When private deployment is justified
- Data must not leave the premises: regulation, contracts or internal policy require data to stay in your own facility. This is the strongest reason, and cost becomes secondary.
- Very high, stable volume: round-the-clock, predictable bulk inference that keeps utilisation high for years.
- Deep fine-tuning: you need to train or fine-tune weights on your own data and maintain that model long term.
- A team to run it: someone owns the inference service, monitoring, scaling, security patches and model upgrades.
When an API is the better fit
- Variable load: pilots, seasonal work, daytime bursts. Pay-as-you-go means you never pay for idle capacity.
- Frontier closed models: Claude, GPT and Gemini do not publish weights, so they cannot be self-hosted; an API is the only way to use them.
- Fast model turnover: when a new model ships, change the model ID and compare results, with no procurement or rollout.
- Many models at once: one key can include several groups and call DeepSeek, Qwen, GLM, Kimi, Claude, GPT and more, with one balance and one usage export.
The hybrid pattern: route by data class
- Classify data first: split business data into "must stay on premises" and "may leave", and write it into policy.
- Sensitive data stays local: run one open-weight model on your internal network for that class only, sized to that slice of volume rather than the whole workload.
- Everything else goes to the API: general Q&A, coding, translation and multimodal tasks, choosing frontier models where they help.
- One calling convention: apps call both sides in OpenAI-compatible format with the same SDK; the data class decides which Base URL a request uses.
- Control API spend by key: one key per app with a quota. When the quota is used up, requests are refused before processing (HTTP 402), and usage exports to CSV by key, member or department.
The data boundary on the API side
If you choose an API, get the data terms in writing. HopBase's terms: prompts and outputs are used only to complete the request, are not kept as business data and are not used for training; billing metadata (model, token counts, latency, status) is kept for invoices and audit; all traffic uses TLS. Keys are stored server-side as one-way hashes and can carry an IP list and an expiry date, and can be revoked at any time. For a fuller evaluation list, see the enterprise AI API security checklist.
FAQ
Should my company self-host an LLM?
Ask three questions: must the data stay on premises, is volume high and stable, do you need fine-tuning. If none, use an API. If only the data requirement, go hybrid. If all three hold and you have an ops team, evaluate self-hosting.
How do I calculate on-premise LLM cost vs API cost?
Add depreciation, power and hosting, staff and upgrade work to get a monthly cost, divide by peak throughput × time × utilisation to get cost per million tokens, and compare it with the API bill for the same volume. Assume lower utilisation to stay safe.
Does HopBase offer on-premise deployment?
No. HopBase provides API access only, which fits the "may leave" side of a hybrid setup. For enterprise volume and billing, contact sales@hop-base.com.