Question 1

How does extra usage work?

Accepted Answer

Pro and Max users can add extra usage balance. Ollama uses included plan limits first, then draws from the extra usage balance. Team usage draws from one balance shared by the organization.

Question 2

How much usage does each model use?

Accepted Answer

Models consume a different amount of usage based on how difficult they are to run. To view a model's usage level, visit the model's page, where its usage level is displayed from small, light models (level 1), like gpt-oss:20b, to extra heavy models (level 4), like deepseek-v4-pro.

Question 3

How is usage measured?

Accepted Answer

Individual plans have usage limits based on the model and the number of input, cached input, and output tokens processed. They don't cap you at a fixed number of tokens because different models use different amounts of compute. For teams, each member's usage draws from the usage included with their seat first. Once it's used, further usage draws from the team's shared extra usage balance at the model's token rate.

Question 4

What are the usage limits for each plan?

Accepted Answer

Running models on your own hardware is always unlimited. Cloud usage varies by plan: Plan Usage Example use cases Free Light usage Chatting with models, evaluating larger models, coding and AI assistants with smaller models Pro Day-to-day work Larger models, coding automation, deep research Max Heavy, sustained usage Continuous agent tasks, multiple concurrent agents, large models over extended sessions Each plan has session limits that reset every 5 hours and weekly limits that reset every 7 days.

Question 5

How fast is Ollama?

Accepted Answer

Speed depends on model size, architecture, and hardware optimization. We target and monitor for low time-to-first-token and high throughput across all cloud models. Priority tiers with faster performance may be available in the future.

Question 6

What quantization or data format do cloud models use?

Accepted Answer

Native weights, as released by the model provider. On modern NVIDIA hardware, models may use accelerated data formats supported by Blackwell and Vera Rubin architectures (e.g. NVFP4).

Question 7

Do models support tool calling?

Accepted Answer

Yes. Cloud models that are trained to support tools are tested for tool calling and with real agent workflows before they go live. If something isn't working, let us know at hello@ollama.com.

Question 8

Which models are available?

Accepted Answer

See the full list of cloud-enabled models here.

Question 9

Does joining a team change my personal account?

Accepted Answer

Ollama creates a separate team account for you when you join. You can switch between your team and personal accounts with the same login.

Question 10

How does Team billing work?

Accepted Answer

Teams start at five seats. Each seat costs $25 per month, so the minimum seat charge is $125 per month. Each additional seat adds $25 per month. Each seat includes usage. Usage beyond that draws from a shared team balance billed as you go. You can add a set amount to the balance and turn off automatic usage billing.

Question 11

Who is the Team plan for?

Accepted Answer

The Team plan is for groups that want shared billing for seats and model usage.

Question 12

Why are new Max subscriptions paused?

Accepted Answer

Ollama's cloud has more than doubled in token volume every month, and with even larger open models like kimi-k3 coming soon, demand is growing faster than we can add capacity. Max subscribers run some of the heaviest workloads, so new Max subscriptions are paused while we add more capacity to protect the experience of existing subscribers. Existing Max subscribers keep their plan, limits, and pricing. Pro and Free remain open, and Pro subscribers can add extra usage to go beyond their plan's included limits.

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Kluczowe funkcje

Cennik

Zastosowania

Czat z prywatnym modelem LLM offline

Rozwój lokalnych aplikacji AI

Asystent kodowania na Twojej maszynie

Eksperymentowanie z modelem dla badaczy

Plusy i minusy

Plusy

Minusy

Wynik bitew

Last battle

Recenzje

Years in this space

Solid for our team

Years in this space

Skeptical, then convinced

Solid for our team

Pytania i odpowiedzi

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Alternatywy dla Duże Modyfikacje Języka (LLM)

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