Ollama Cloud's New Billing Plan Tested: A Hidden Price Hike of 2.9–6.7x
Ollama Cloud's billing overhaul is effectively a 2.9–6.7x hidden price hike, with existing subscribers temporarily protected.
Ollama Cloud switched from GPU-time billing to per-token billing on September 1st, and community testing via API probing reveals this is effectively a 2.9–6.7x hidden price increase. The old model let power users amortize costs by maxing out inference within time limits; the new model charges a fixed per-token rate that eliminates that advantage. GLM-5.2 is hit hardest (6.2–6.7x), while DeepSeek v4 Pro is more moderate (2.9–3.3x). Existing subscribers can avoid the hike by not canceling or upgrading their plans, though any active change locks them into the new pricing with no automatic rollback.
Overview
Ollama Cloud's quota billing overhaul, which took effect on September 1st, has sparked widespread discussion in the community. According to hands-on research by Reddit user u/Flaky-Maybe-7556, what appeared to be a routine billing model change actually constitutes a hidden price increase of 2.9 to 6.7x.
This research wasn't based on official statements or subjective impressions — it involved repeatedly calling the /api/usage endpoint before the billing switch went live to empirically record how quickly the old Pro plan's quota was consumed per token. Tests ran from August 28–31 with a margin of error of ±7–13%. The methodology, scripts, and raw logs are all publicly available.
The Fundamental Difference Between Old and New Billing Models
Understanding this price hike requires grasping how the two billing models actually differ.
Old Model: Billed by GPU Runtime
The old plan was essentially billed by GPU runtime, not by tokens — consistent with Ollama's own explanation. Under this model, users consumed models within session and weekly quota limits, and the effective per-token cost depended on inference speed: the faster the inference, the more tokens produced per unit of GPU time, and the lower the per-token cost.
Empirical data shows that the effective per-token price under the old model was roughly 9 to 20 times cheaper than the new plan's listed per-token rates, depending on the model.
New Model: Monthly Credits plus Per-Token Pricing
The new plan issues a fixed monthly credit allowance ($60/month for Pro, billed at $20/month — effectively a 3x multiplier) and deducts usage at publicly listed per-token rates. Ollama has explicitly stated that "the session and weekly limits of the old plan no longer apply."
On the surface, this looks like a transparency improvement. But once you convert everything to effective per-token costs, the price increase becomes unmistakable.
Empirical Results: 2.9x to 6.7x Price Increases
The researcher compared three popular models. Key figures (in USD per million tokens):
| Model | Token Type | Old Effective Price | New Effective Price | Increase |
|---|---|---|---|---|
| GLM-5.2 | Input (cache miss) | $0.072 | $0.467 | 6.5x |
| GLM-5.2 | Input (cache hit) | $0.013 | $0.087 | 6.7x |
| GLM-5.2 | Output | $0.237 | $1.467 | 6.2x |
| GLM-5.3 | Input (cache miss) | $0.105 | $0.467 | 4.4x |
| GLM-5.3 | Output | $0.343 | $1.467 | 4.3x |
| DeepSeek v4 Pro | Input (cache miss) | $0.133 | $0.440 | 3.3x |
| DeepSeek v4 Pro | Input (cache hit) | $0.005 | $0.015 | 2.9x |
| DeepSeek v4 Pro | Output | $0.396 | $1.320 | 3.3x |
The data shows that the impact varies significantly by model. GLM-5.2 is hit hardest, with increases of 6.2–6.7x at full quota utilization. GLM-5.3 comes in at roughly 4.3–4.4x. DeepSeek v4 Pro is comparatively moderate at 2.9–3.3x.
Note: the "new effective price" is calculated by dividing the listed rate by 3, since the Pro plan now offers $60 in credits for a $20 monthly fee. The "old effective price" is the empirically measured quota deduction rate; output prices are lower-bound estimates.
Why the Old Model Was So Much Cheaper
Why did GPU-time billing feel so affordable? The core assumption is quota exhaustion at maximum intensity.
The low per-token cost of the old model was premised on users completely "draining" their quota — running inference as heavily and continuously as possible within the GPU time limit. For power users, this was an exceptionally cost-effective arrangement. The new model locks in a fixed per-token price regardless of usage intensity, completely eliminating the marginal cost advantage that heavy users previously enjoyed.
In other words, this change isn't simply a price increase — it's a fundamental shift in billing logic, and its impact varies dramatically depending on how you use the service.
What Existing Users Should Do
Fortunately, there are protections in place for existing subscribers.
Current subscribers can keep their existing plan unchanged. The price hike only affects new sign-ups and users who actively switch plans. If you're already on the old Pro plan and satisfied with your current quota, the safest move is to leave things as they are.
The researcher's update notes specifically warn:
- Do not cancel your existing plan
- Do not "upgrade" to a new plan (including upgrading from old Pro to new Max)
Once you do either of these things, you'll be switched to the new per-token billing logic with no automatic way back. If you accidentally make a change and want to revert, you can try contacting Ollama support by email or reaching out via X or Discord to request a switch back to the grandfathered plan.
Closing Thoughts
This incident is a textbook example of how AI infrastructure service pricing evolves. The shift from "billed by resource time" to "billed by token usage" is fundamentally a move toward more granular cost accounting and predictable subscription revenue for providers. For users, it's a reminder that evaluating cloud AI costs requires more than just looking at listed unit prices — you need to model your own actual usage patterns.
What's most commendable about this research is its rigorous empirical methodology: API probing, multiple complete test runs, and publicly released raw logs that transform a "billing transparency improvement" — the kind of change that official language can easily obscure — into a quantifiable, reproducible price increase. In the increasingly complex pricing landscape of AI services, this kind of community-driven independent testing is becoming more valuable than ever.
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