Ollama Pricing (2026): Free Local, $20 Pro, $125 Teams

Ollama costs $0 for unlimited local use, $20 per month for Pro, and at least $125 per month for Team. Free is the right default when your existing hardware fits the model; Pro wins when cloud capacity is cheaper than a new machine, while Team only earns its premium when shared billing and controls matter. Every price and limit here was verified against Ollama's live pricing page on August 23, 2026.

Ollama pricing at a glance

The default choice is Free for local work, monthly Pro for uncertain cloud demand, and annual Pro only after you are confident you will stay for more than 10 months. Max is not a new-buyer option today, and Team begins at a five-seat floor rather than one seat.

Ollama presents local inference, which means the model runs on hardware you control, beside hosted cloud inference under the same product. That split is why one answer can honestly be both "$0" and "$125 per month. The live Ollama pricing page is the source of record.

Sticker price is only half the decision. Ollama does not publish one fixed token allowance for Free, Pro, or Max. Consumption depends on the model and on input, cached-input, and output tokens. A 50x multiplier is useful for comparing tiers, but it does not tell a buyer how many coding sessions, reports, or agent runs will fit.

Is Ollama free?

Yes. Ollama's local software costs $0, and running models on your own hardware is unlimited. The Free account also includes light cloud usage with one concurrent cloud model.

Those are two different versions of free. Local use has no Ollama usage bill because your computer supplies the compute. Cloud Free uses Ollama's infrastructure, so it carries model-dependent limits even though the price is $0. If a request arrives after the single concurrency slot is occupied, it waits in a queue; a full queue rejects further work until capacity opens.

Free local use is the durable bargain. If your existing machine already runs the model you need at an acceptable speed, you can keep the software line at $0. You still own the hardware, storage, electricity, updates, and time spent operating it, but no subscription is required to keep generating locally.

Free cloud is better treated as an evaluation and overflow allowance. Ollama describes it as light usage, not as a fixed number of tokens. Session limits reset every 5 hours and weekly limits reset every 7 days. That is enough to learn whether a larger cloud model fits your work, but not enough information to promise a production volume.

Who never needs to pay: a solo technical builder who uses public models on hardware already owned, keeps the work local, and does not need more than occasional cloud access can stay on Free indefinitely. Paying becomes rational when a larger model does not fit, the local machine is too slow, or several cloud jobs must run at the same time.

Ollama Pro costs $20 monthly or $200 annually

Pro is the practical paid plan for one person because $20 per month buys more cloud capacity without creating a hardware project. It includes 50x the Free cloud usage, three concurrent cloud models, access to larger cloud models, and private-model upload and sharing.

The annual choice has a clean break-even:

• Twelve monthly payments cost $240.
• Annual billing costs $200.
• The annual saving is $40.
• The annual price equals 10 monthly payments.

That makes annual Pro sensible only when you expect to use it beyond month 10. At exactly 10 months, the two routes cost the same. If a project may finish earlier, month-to-month pricing buys a cheaper exit even though its full-year total is higher.

Pro also supports extra usage. Ollama applies the included allowance first, then draws from a balance you add. That gives a regular user a path through a busy week without jumping plans, but it also turns a flat subscription into a variable bill once the included pool is exhausted.

Ollama Max costs $100, but new signups are paused

Max is currently unavailable to new subscribers, so a new buyer should remove it from the budget rather than treating $100 per month as a capacity guarantee. Ollama says the pause is temporary while it adds capacity. Existing Max subscribers keep their price, limits, and plan.

For those existing subscribers, Max carries 10 concurrent cloud models and 5x the Pro usage. The price is also 5x Pro's $20 monthly price. On Ollama's published multiplier alone, Max is not a bulk discount on included usage. Its practical advantage is the move from three concurrent cloud models to 10 and support for heavy, sustained work.

The business consequence is simple. A founder planning several continuous agents cannot treat Max as a launch dependency today. The available route is Pro plus extra usage, a usage-based alternative, or local infrastructure. Waiting for Max without a fallback turns a pricing-page status into a deployment risk.

Ollama says cloud token volume has more than doubled every month, which is why it paused the heaviest new subscriptions. That is evidence of demand, but it is also evidence that capacity is a live constraint. Existing subscribers should preserve a second model path for jobs that cannot wait in a queue.

Ollama Team starts at $125 per month

Team costs $25 per seat per month with a five-seat minimum, so the smallest possible invoice is $125 per month or $1,500 over 12 months. It is marked as introductory pricing and remains on a waitlist.

The useful comparison is not $25 versus $20. It is the smallest real group bill:

• Five month-to-month Pro accounts cost $1,200 over 12 months. Team costs $300 more.
• Five annual Pro accounts cost $1,000. Team costs $500 more.

That $500 is the annual Team-control premium at the minimum size. It buys a separate team account, shared billing and administration, included seat usage, a shared extra-usage balance, priority support, and zero data retention and logging. Each additional Team seat adds $25 per month.

The decision flips when those controls remove more than $500 of coordination or risk per year. A five-person startup that is comfortable with separate personal accounts should keep the $500. A regulated group that needs one bill, controlled usage, support, and a formal team boundary can justify it quickly.

Do not buy Team for features that are still promises. Ollama marks single sign-on, model access controls, and device-management installers for Windows and macOS as coming soon. Those capabilities are not current inclusions on the live page.

Enterprise has custom pricing. It adds volume terms, security and procurement support, and deployment planning. That makes it a negotiation path, not a tier that can be normalized to a public per-seat price.

Extra usage exposes the real API cost

Ollama's subscription price is predictable until included usage runs out; after that, the model's token rate matters. Pro and Max can add extra usage balance, while Team draws overage from a shared balance. Team administrators can turn off automatic usage billing.

The direct cloud API does not have a separately listed subscription tier. Ollama's cloud documentation uses the same account and an API key to call models at ollama.com. In practice, the split is local API at $0 software cost versus cloud API usage under the account's plan and extra balance.

Kimi K3 makes the overage math concrete. Its live Ollama model page lists:

• $3.00 per 1 million input tokens, or $0.003 per 1,000.
• $0.30 per 1 million cached-input tokens, or $0.0003 per 1,000.
• $15.00 per 1 million output tokens, or $0.015 per 1,000.

A job with 100,000 uncached input tokens and 10,000 output tokens costs $0.45 at those rates: $0.30 for input plus $0.15 for output. That is an honest cost per outcome for extra usage. It is not the cost of a Pro task while included capacity remains, because Ollama does not publish the fixed token quantity inside Pro.

Ollama emails an optional reminder at 90% of the plan limit. Purchased extra usage credits expire one year after they are added, so a large precautionary balance can become waste. Add only what the next workload is likely to consume.

Local hardware beats Pro only when the machine already earns its keep

Existing hardware makes local Ollama hard to beat: the software bill stays at $0. Buying a new machine only to avoid a $20 subscription is a much weaker trade.

Every $1,000 of incremental hardware cost equals 50 months of Pro before electricity, storage, maintenance, or operator time. The calculation is deliberately simple: $1,000 divided by $20 per month. At that rate, a dedicated purchase takes more than four years to recover against Pro, and the model you want may change before the machine pays back.

The word incremental matters. If a $1,000 upgrade also replaces an aging workstation, supports design or engineering work, and serves local models, charging the full amount to Ollama would overstate the AI cost. Allocate only the part bought specifically for inference.

Local still wins for reasons that a subscription comparison cannot price. Data can remain on hardware you control, local model use is unlimited, and a working offline path is not exposed to a cloud queue. Those benefits can outweigh a long cash break-even when privacy, availability, or experimentation volume is the actual requirement.

Pro wins when the need is a temporary burst, a larger model, or a clean operating expense. Pay $20 for one month, learn which jobs hit limits, and only then consider annual billing or hardware. A hardware purchase made before measuring the workload converts uncertainty into sunk cost.

Ollama versus LM Studio and OpenRouter pricing

LM Studio is the cleaner alternative for irregular pay-as-you-go cloud work, while OpenRouter is the broader choice for routing across many providers. Ollama Pro wins when its included capacity absorbs steady work and the same local-to-cloud workflow is valuable.

LM Studio: $0 local, metered cloud

LM Studio charges $0 for local models and voice transcription on your own machine, then sells cloud inference as pay-as-you-go credits. Its Free plan includes LM Link for up to five devices. Bionic Pass is marked coming soon without a published price.

The clean comparison is the same model. LM Studio lists Kimi K3 at $3.00 per 1 million input tokens, $0.30 per 1 million cached-input tokens, and $15.00 per 1 million output tokens, exactly the rates on Ollama's Kimi K3 page. The raw metered cost is therefore tied. Packaging decides the winner.

Choose LM Studio Cloud when usage is sporadic and you want every token to land on a visible credit balance. Choose Ollama Pro when regular included usage is likely to cover the month and you prefer Ollama's local and cloud model path. For a deeper runtime choice, the Ollama alternatives comparison separates desktop tools from production servers.

OpenRouter: no subscription floor, plus a credit fee

OpenRouter offers pay-as-you-go access to more than 500 models from more than 80 providers with no minimum spend. It charges a 5.5% fee when credits are purchased, subject to a $0.80 minimum, while passing through model inference prices.

Buying $100 of usable credits costs $105.50 before tax when the 5.5% fee applies. That makes the platform cost explicit. The Free plan offers more than 25 free models across four providers and allows 50 requests per day; those limits make it an evaluation route, not a dependable production allowance.

Choose OpenRouter when model choice, provider routing, and a usage-based bill matter more than local execution. Choose Ollama when running locally is part of the design and cloud is an extension of the same workflow. The OpenRouter pricing breakdown covers its fee in more detail, while the cheapest AI API comparison is the better next step when no subscription shape fits.

The explicit rule is this: steady individual use that fits Ollama Pro favors Ollama; irregular token use favors LM Studio; multi-provider production routing favors OpenRouter. None is cheapest in every workload because the billing units differ.

The hidden costs in Ollama pricing

The biggest hidden cost is uncertainty, not a surprise line item. Included cloud quotas are described by relative usage, while actual consumption changes with the model and token mix. That makes a monthly invoice predictable but the work delivered by that invoice harder to forecast.

Other terms belong in the budget:

• Paid subscriptions renew automatically unless cancelled before renewal.
• Customers are responsible for applicable taxes.
• Purchased extra usage credits expire after one year.
• New Max subscriptions are paused, and Team is waitlisted.
• Cloud models can be retired as newer models arrive. Ollama says affected users will receive notice, but applications may still need a model change.
• Requests above the concurrency allowance are queued, and a full queue rejects additional work.
• The current Terms explain cancellation but do not promise a standard refund window.

The refund point needs precise wording. Ollama does not state that every payment is non-refundable, but it also does not publish a routine refund entitlement in the current Terms. If an annual purchase depends on a particular model or capacity level, confirm the exception path with support before paying.

Skip paid Ollama entirely when your existing hardware does the job. Skip annual Pro until the eleventh month is likely. Skip Team when five separate annual Pro accounts are operationally acceptable. Skip Ollama Cloud for a critical workflow that cannot tolerate an opaque quota, queue, or model retirement path without a fallback.

The Monday move

Start with a one-week budget check, not a hardware order or annual lock.

1. 1

   Use the machine you already own

   Run the intended local model on existing hardware. If it meets the job, keep the software budget at $0 and record only the infrastructure and operating cost you truly incur.

2. 2

   Use Free cloud across both reset windows

   Try the real workload through a 5-hour session window and a 7-day weekly window. Record model choice, concurrency, queueing, and the point where work stops. Do not translate the result into a universal token quota.

3. 3

   Buy one month of Pro before committing

   If Free blocks useful work, pay $20 for Pro and measure again. Annual billing only improves the total after 10 monthly payments, so uncertainty belongs on the monthly plan.

4. 4

   Price the control layer separately

   For five people, compare the $1,500 annual Team floor with $1,000 for five annual Pro accounts. Join the Team waitlist only if shared billing, administration, support, and data controls are worth the $500 difference.

That sequence keeps the budget reversible. Free proves hardware fit, monthly Pro proves cloud fit, annual Pro rewards persistence, and Team pays for governance rather than cheaper inference.

Frequently asked questions

Is Ollama free?

Yes. Local use is $0 and unlimited on your own hardware. Ollama also includes light cloud usage on Free, with one concurrent cloud model and model-dependent limits.

Does Ollama cost money?

Only when you choose paid cloud capacity or buy infrastructure. Pro costs $20 per month or $200 per year, Max is listed at $100 per month but closed to new signups, and Team starts at $125 per month.

Does Ollama offer a student discount?

Ollama does not publish a student-specific discount on its current pricing page. Students can use the $0 Free plan or pay the standard Pro rate.

What is Ollama's refund policy?

Ollama's current Terms explain cancellation and automatic renewal but do not promise a standard refund window. Confirm any exceptional refund case with support before committing to annual billing.

Did Ollama pricing change in 2026?

The material current change is availability: new Max signups are paused, while existing Max subscribers keep their price and limits. Team is also marked introductory pricing and remains on a waitlist.

How does Ollama Cloud API pricing work?

The direct cloud API uses an Ollama account and API key under the same cloud plan. Included usage is model-dependent; after it is consumed, extra balance is charged at the selected model's token rates.

Does Ollama require a GPU?

Local models use your own hardware, so model size and acceptable speed determine whether the machine is sufficient. Ollama Cloud offloads larger models to hosted infrastructure, which lets you use them without buying a powerful local GPU.

Want the wider stack mapped to the job each tool can do for a business? Get the AI tools map for business owners and a concise weekly briefing on what changed and what deserves attention.