Inference buying guide
API vs subscription vs Ollama Cloud: cheapest way to buy open model inference
Pick official APIs, developer subscriptions, or Ollama Cloud by workload: billing clarity, privacy boundary, rate limits, and Kimi/DeepSeek/GLM usage.
鸭哥 AI 手记 / Superlinear Academy · 开源模型推理采购指南：GLM-5.1、DeepSeek V4 Pro、Kimi K2.6 的 API、订阅和 Ollama Cloud 对比
Inspired by a Chinese buying guide from 鸭哥 AI 手记 / Superlinear Academy. This whichllm page is original commentary focused on model procurement decisions, not a translation or republication.
Quick answer
Do not choose open model inference by headline monthly price. Choose the channel whose billing shape, privacy boundary, rate limits, and failure cost match the workload.
- Use official APIs when a production app needs metered billing, auditability, provider controls, and predictable usage reporting.
- Use developer subscriptions when heavy coding or agent loops fit inside the plan limits and human iteration speed matters more than exact token accounting.
- Use Ollama Cloud or a router when you are comparing Kimi, DeepSeek, GLM, and other open models before committing to one production path.
- Check privacy terms before price when prompts include customer data, private code, or internal documents.
Best buying channel by workload
| Workload | First channel to test | Why |
|---|---|---|
| Production app | Official API | Metered APIs are easiest to budget, monitor, rate-limit, and map back to customer or product usage. |
| Heavy coding / agent loops | Developer subscription | A subscription can win when request caps hold and the real cost is human time lost to slow iteration. |
| Trying Kimi + DeepSeek + GLM | Ollama Cloud or router | One plan or router keeps model switching cheap while you learn which open model handles each task. |
| Private customer or code data | Clearest retention terms | Data retention, training policy, region, and access controls can dominate the token price. |
| Budget forecast needed | Metered API | Per-token accounting gives finance and product teams a cleaner cost model than opaque monthly caps. |
The model is not the whole product
Open models are no longer a single buying decision. The same model can be bought as a metered API, bundled inside a developer subscription, routed through a third-party service, or run through a cloud inference product. Those channels create different cost and risk even when the model name is the same.
For builders, this changes the real comparison. You are not only comparing GLM-5.1, DeepSeek V4 Pro, and Kimi K2.6. You are comparing billing clarity, privacy boundary, rate limits, speed, support path, and how much friction the channel adds to an agent workflow.
Channel notes
Official API
Best when you need explicit usage accounting, provider-native settings, and production billing clarity. It is usually the cleanest way to connect product usage to inference cost.
Developer subscription
Best when one model family dominates your personal or team workflow. The risk is opacity: request caps and fair-use limits can matter more than the headline monthly price.
Ollama Cloud
Best when you want open-model flexibility without running local infrastructure. It is especially interesting for builders who switch between GLM, DeepSeek, and Kimi by task.
Privacy can beat price
A cheap channel is not automatically a good channel. If your prompts include customer records, unreleased code, legal text, or private company context, read the data retention and training terms before you optimize cost.
The practical rule: use price to choose between acceptable channels, not to make an unacceptable channel acceptable. If the privacy boundary is unclear, treat the model as a prototype tool rather than a production dependency.
Compare live model specs on whichllm
Use this guide to choose the buying channel, then check current model IDs, context windows, capabilities, and token prices before wiring Kimi, DeepSeek, GLM, or another open model into a workflow.