Ollama Cloud (2026): Pricing, Models, API and When to Use It

Ollama Cloud is Ollama’s hosted inference layer for running cloud-enabled open models without needing to supply the GPU capacity yourself. It keeps the familiar Ollama workflow, but lets you run larger models on hosted infrastructure through the CLI or API when local hardware is not enough.

As of September 26, 2026, the pricing model is usage-credit based rather than the older “50x more usage” system. Ollama Pro costs $20/month and includes $60 of monthly usage credits; Max costs $100/month and includes $300; Team is $500/month with $1,000 of shared monthly usage. Usage is then charged at published per-token model rates. Free accounts include starter usage and can add pay-as-you-go credits. Ollama’s pricing page is the source of truth for the live rates.

For businesses, the important decision is not “cloud or local forever.” Ollama works well as a hybrid stack: keep sensitive, predictable or offline workloads on infrastructure you control, then use cloud models when you need larger models, more compute, full cloud context lengths or short bursts of parallel capacity. That hybrid approach also fits AIMEC’s broader guidance on local and private AI.

What Is Ollama Cloud?

Ollama Cloud is the hosted side of the Ollama ecosystem. Instead of downloading every model and running inference on your own CPU or GPU, you can send requests to cloud-enabled models that run on Ollama-managed infrastructure.

That does not make Ollama a completely different product in the cloud. The main benefit is continuity: you can keep using Ollama’s CLI, libraries and API patterns while changing where inference happens. Ollama originally described cloud models as a way to run models that would not fit on a personal computer while preserving the same local tooling experience.

This makes Ollama Cloud especially relevant to teams that already prototype with Ollama locally. You do not need to redesign the entire application just because a larger model or a heavier workload needs hosted compute.

Ollama Cloud vs Local Ollama

Local Ollama and Ollama Cloud use the same ecosystem, but the operational trade-offs are different.

Area	Local Ollama	Ollama Cloud
Where inference runs	Your workstation, server or private infrastructure	Ollama-hosted cloud infrastructure
Hardware requirement	You provide CPU/GPU capacity	Ollama provides the inference compute
Usage cost	No Ollama per-token charge for local runs; you still pay for your hardware and operations	Usage credits consumed at published per-token rates
Offline use	Possible once the model and dependencies are available locally	Requires network access
Model size	Limited by your available memory and compute	Useful for models that are impractical on local hardware
Privacy boundary	Prompts can remain entirely inside your environment	Requests leave your environment and are processed by Ollama’s cloud under its stated zero-retention policy
Scaling	You scale hardware and serving infrastructure	Plan concurrency and usage credits define hosted capacity

If your main priority is keeping model execution entirely inside your own environment, see our comparisons of Ollama vs llama.cpp and Ollama vs vLLM. If you are still choosing where models should come from and how they should be managed, our Ollama vs Hugging Face guide covers that separate decision.

Current Ollama Cloud Pricing

Ollama changed its cloud pricing on August 31, 2026. New plans use monthly usage credits and published per-token rates. The older session and weekly usage-limit framing is no longer the primary pricing model for new plans.

Plan	Current price	Included cloud usage	Concurrency	Best fit
Free	$0	Starter usage for starter models; add credits to unlock all cloud models	1 concurrent request	Evaluation and light use
Pro	$20/month or $200/year	$60 of usage credits per month	3 concurrent requests	Regular individual development and shorter agent tasks
Max	$100/month	$300 of usage credits per month	10 concurrent requests	Power users and multi-agent workloads
Team	$500/month	$1,000 shared monthly credits for unlimited users	10 concurrent requests	Teams needing shared billing and administration
Enterprise	Custom	Custom volume arrangement	Custom	Larger organizations needing controls, budgets and support

Included credits refresh monthly and do not roll over. Ollama uses the included plan credits first, then purchased usage credits. The company says there are no service fees on top of the published model rates. Existing subscribers on legacy Pro, Max or Team plans can remain on those plans while they continue renewing, but changing billing cycle or plan tier moves them to the new pricing model. See Ollama’s transparent-pricing announcement for the transition details.

How Model Pricing Works

Cloud usage is measured in tokens. Ollama publishes separate prices per million tokens for input, cached input where supported, and output. That means two models on the same subscription can consume credits at very different rates.

For example, the official table on September 26, 2026 lists the following standard rates:

Model	Input / 1M tokens	Cached input / 1M	Output / 1M
gpt-oss:20b	$0.07	$0.035	$0.30
Gemma 4	$0.14	$0.05	$0.40
Qwen 3.5 397B	$0.60	Not listed	$3.60
GLM 5.3	$1.40	$0.26	$4.40
Kimi K3	$3.00	$0.30	$15.00

Some models also have lower off-peak rates. Ollama currently defines off-peak time as outside 12:00–18:00 UTC on weekdays and all day on weekends for models where off-peak pricing is listed. Because this table changes as models are added or repriced, use the official model-pricing table for budgeting rather than relying on a static third-party list.

Which Cloud Models Are Available?

The cloud catalogue changes quickly, so the useful way to think about it is by workload rather than by memorizing a fixed list.

As of September 26, 2026, Ollama’s cloud catalogue includes models and families such as GLM 5.3 and GLM 5.3 Flash, DeepSeek V4 variants, Kimi K3 and Kimi K2.7 Code, MiniMax M3, Gemma 4, Qwen 3.5, NVIDIA Nemotron 3, Mistral Large 3 and gpt-oss. Many of the current cloud models are tagged for tools, reasoning, vision or combinations of those capabilities.

Typical workload	Cloud models to evaluate
Coding and agentic workflows	GLM 5.3, Kimi K2.7 Code, Qwen 3.5, gpt-oss
Multimodal work	Gemma 4, MiniMax M3, Kimi K3, GLM 5.3 Flash
Long-running reasoning or multi-agent tasks	Nemotron 3 Super/Ultra, DeepSeek V4 Pro, Kimi K3
Lower-cost general inference	gpt-oss:20b, Gemma 4, selected Flash models

Check the live Ollama cloud-model catalogue before committing to a model. Availability, context windows and model pricing can change faster than an evergreen guide can be updated.

How Ollama Cloud Works With the CLI and API

There are two common ways to use Ollama Cloud.

1. Use the normal Ollama CLI. Sign in to Ollama and run a cloud-enabled model through the same command-line workflow you already use locally. Ollama then offloads inference to hosted infrastructure rather than requiring the model to fit on your machine.

2. Call the hosted API directly. Create an Ollama API key, set it as OLLAMA_API_KEY, and send authenticated requests to https://ollama.com/api. Ollama’s examples use a Bearer token in the Authorization header.

The local native API is normally served from http://localhost:11434/api, while the hosted native API uses https://ollama.com/api. Ollama also supports OpenAI-compatible endpoints: locally you can point compatible clients at http://localhost:11434/v1, while official cloud examples use https://ollama.com/v1 with an Ollama API key.

That API continuity is one of the strongest reasons to use Ollama Cloud if you already build with Ollama locally. The base URL and authentication change, but you do not necessarily need a separate application architecture for cloud inference.

Coding-Agent and Workflow Integrations

Ollama is increasingly positioning cloud models as infrastructure for coding agents and tool-using workflows, not just chat.

The official ollama launch workflow supports integrations including Claude Code, OpenCode, Codex and Droid. Ollama’s current site also lists tools such as n8n among its integrations. For direct application integration, teams can use the native Ollama API or compatibility layers such as OpenAI-style and Anthropic-style APIs where the required features are supported.

This matters for agent systems because you can keep the orchestration layer stable while changing which local or cloud model handles a task. A coding agent can use a local model for routine work and a larger cloud model when the task needs more reasoning capacity or context.

Why I Still Use Ollama Cloud With Qwen

In my own AI engineering work, the part of Ollama Cloud I find most useful is not simply access to a bigger model. It is the ability to keep a local-first development pattern and selectively escalate harder tasks to hosted compute.

Qwen remains one of the model families I regularly evaluate for agentic and tool-using workflows. Ollama’s current catalogue includes Qwen 3.5 cloud models, while its wider Qwen releases continue to emphasize tool use, reasoning and multimodal capabilities. That makes the family relevant when I am building assistants that need to call search, retrieval, automation or external business logic.

The practical benefit is flexibility. I can test a workflow locally, keep sensitive steps inside a private environment, and then route only the model calls that genuinely need more capacity to the cloud. That is more useful to me than treating “local” and “cloud” as two unrelated stacks.

Privacy and Data Retention

Ollama’s current cloud privacy claims are unusually direct. Its pricing documentation says prompt and response data is never logged or trained on, and its August 2026 pricing announcement describes zero data retention for cloud requests.

For hosting location, Ollama says models and compute resources are hosted primarily in the United States and may route to Europe and Singapore for additional capacity. Its August pricing announcement is more specific, saying dedicated compute is in the U.S. and Europe, with Singapore used for a limited set of Qwen models.

Those claims are useful, but they do not make every cloud workload automatically suitable for confidential or regulated data. A business still needs to map data classes, contractual requirements, approved regions and vendor controls before sending sensitive information to any hosted service. For a deeper architecture view, see our guide to private AI architecture.

Concurrency and Usage Controls

Concurrency is separate from token spend. The current plan limits are 1 concurrent request on Free, 3 on Pro, and 10 on Max and Team.

Requests above your plan’s concurrency limit are queued until a slot becomes available. Ollama notes that the queue has a fixed limit, so requests can be rejected if the queue is full. On paid plans, Ollama also sends an email reminder when you reach 90% of included monthly usage, and the usage dashboard shows current consumption.

For agent systems this can matter more than the headline subscription price. A workflow that launches many agents in parallel may hit concurrency before it exhausts its credit balance, while a single long-running workflow may be constrained mainly by token cost.

When to Use Ollama Cloud vs Local Models

Use local Ollama when data must remain inside your environment, offline operation is important, workload volume is predictable, or you already have the hardware to serve the models you need. Local deployment also gives you more control over model versions, serving configuration and network boundaries.

Use Ollama Cloud when the model you want is too large for your hardware, you need to test a stronger model quickly, demand is bursty, you need more parallel inference than your workstation can provide, or buying additional GPU capacity would be premature.

Use both when you want a local-first architecture with cloud escalation. In practice, that is often the most sensible business design: keep high-sensitivity and routine tasks local, then send selected tasks to hosted inference when the added model capability or compute justifies it.

If you are deciding whether a company should own the serving layer instead of paying for hosted inference, our guide to LLM hosting for business covers the operational side of that decision.

Ollama Cloud vs Other Hosted Model Routers

Ollama Cloud is most compelling when you already use Ollama and want one workflow across local and hosted open models. A broader hosted model router is designed for a different priority: giving one API access to models from multiple providers and ecosystems.

That means the choice is less about which category is universally better and more about architecture. If local Ollama is already part of your stack, Ollama Cloud reduces the gap between local development and hosted inference. If your priority is switching among many unrelated commercial and open-model providers through a single gateway, a multi-provider router may fit better.

Frequently Asked Questions

Is Ollama Cloud free?

Yes. Ollama has a Free plan with starter usage credits and access to starter models. You can also add pay-as-you-go usage credits to unlock all cloud models without subscribing. Running models on your own hardware remains unlimited from Ollama’s usage-billing perspective.

Does Ollama train on my prompts?

Ollama says prompt and response data is never logged or trained on, and its current cloud pricing documentation states that providers must follow no-logging, no-training and zero-data-retention requirements.

Is Ollama Cloud hosted in the U.S.?

Primarily, yes, but not exclusively. Ollama says compute is hosted primarily in the United States and may also route to Europe and Singapore. Its August 2026 pricing announcement says Singapore is used for a limited set of Qwen models.

Can I use the same API locally and remotely?

You can use the same Ollama API style with different base URLs and authentication. Local native API calls normally go to http://localhost:11434/api; hosted calls go to https://ollama.com/api with an API key. Ollama also offers OpenAI-compatible endpoints locally and in the cloud.

How many Ollama Cloud requests can run at once?

The current limits are 1 concurrent request on Free, 3 on Pro, and 10 on Max and Team. Requests above the limit are queued until a slot opens, subject to a finite queue.

What happens when I use all of my monthly credits?

You can continue at the same published per-token rates using purchased usage credits. Included credits refresh every month and do not roll over.

Product facts and pricing in this guide were checked against official Ollama sources on September 26, 2026. Because cloud models and rates change frequently, verify the live pricing page and cloud-model catalogue before making a production or budget decision.