Running large language models locally with Ollama can be significantly cheaper than using cloud APIs like OpenAI’s or Anthropic’s when you’re processing a substantial volume of requests.
Let’s see what that looks like in practice. Imagine you’re running a service that needs to summarize customer feedback. Each feedback item is about 500 tokens. You have 1 million feedback items to process.
Using OpenAI’s gpt-3.5-turbo-0125 (a common, cost-effective model):
- Input cost: $0.50 per 1 million tokens
- Output cost: $1.50 per 1 million tokens
- Total tokens per item: 500 input + 100 output (for the summary) = 600 tokens
- Total tokens for 1M items: 600 tokens/item * 1,000,000 items = 600,000,000 tokens = 600 million tokens
- Cost breakdown:
- Input: (500B / 1M) * $0.50 = 300M tokens * $0.50/1M tokens = $150
- Output: (100B / 1M) * $1.50 = 300M tokens * $1.50/1M tokens = $450
- Total OpenAI cost: $150 + $450 = $600
Now, let’s look at Ollama. We’ll use llama3:8b, a capable open-source model.
First, you need hardware. A decent GPU, like an NVIDIA RTX 4090 (24GB VRAM), costs around $1,600. Let’s assume a 3-year lifespan for the hardware.
- Hardware cost per year: $1600 / 3 years = ~$533 per year.
- Power consumption: An RTX 4090 can draw up to 450W. Let’s say it idles at 50W and under load averages 300W. For simplicity, let’s average 200W for the GPU over a typical workday (8 hours).
- Daily power: 200W * 8 hours = 1600 Wh = 1.6 kWh
- Monthly power (30 days): 1.6 kWh/day * 30 days = 48 kWh
- Assuming $0.20/kWh: Monthly power cost = 48 kWh * $0.20/kWh = $9.60
- Annual power cost: $9.60/month * 12 months = $115.20
Ollama itself is free. The llama3:8b model is also free to download. The key is the inference speed and how many requests your hardware can handle concurrently.
A single RTX 4090 can run llama3:8b quite efficiently. Let’s say it can process 10 requests per second for your 500-token input / 100-token output task.
- Total requests: 1,000,000 feedback items.
- Time to process: 1,000,000 requests / 10 requests/second = 100,000 seconds.
- Total hours: 100,000 seconds / 3600 seconds/hour = ~27.8 hours.
This 27.8 hours is the actual GPU compute time. You might run this over a few days, or even hours if you scale up to multiple GPUs. The critical point is that the hardware is yours, and once purchased, the marginal cost per inference is very low.
- Ollama annual cost (estimated): $533 (hardware amortization) + $115.20 (power) = $648.20
For 1 million items, the OpenAI cost is $600. The Ollama cost is ~$648 for a whole year of potential operation, assuming you amortize the hardware over 3 years. If you process more than 1 million items in that year, the Ollama cost per million items drops dramatically.
If you needed to process 10 million items in a year:
- OpenAI cost: $600/million * 10 million = $6,000
- Ollama cost: The hardware and power costs are largely fixed for the year. The marginal cost is near zero. Your total cost is still around $648. The effective cost per million items becomes $64.80.
This "break-even" point where Ollama becomes cheaper depends on the model’s performance on your hardware, the model’s VRAM requirements, your electricity costs, and the hardware’s lifespan. For llama3:8b and a 4090, processing around 1-2 million items per year is where Ollama starts to significantly undercut cloud APIs. For larger, more powerful, or multiple GPUs, this break-even point shifts to even higher volumes.
The core of Ollama’s cost advantage is that you’re paying for hardware and electricity, which are relatively fixed and predictable, rather than per-token API calls, which scale linearly with usage. This makes it incredibly attractive for high-volume, consistent workloads where you can justify the upfront hardware investment and the operational overhead of managing your own inference servers.
The total cost of ownership for Ollama includes not just hardware and electricity, but also the engineering time to set up, maintain, and monitor the inference servers, manage model updates, and handle scaling. This can be a significant hidden cost that needs to be factored into the comparison, especially for smaller teams or less critical applications.