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Freemium
Users wanting fully private local AI
Ollama
FreemiumText to TextOpen SourceAPISelf-Hosted
Ollama downloads open models and runs them on your own machine behind a simple command line and local API. It also offers optional cloud-hosted models, which run on Ollama's servers instead of yours.
$20/mo or $200/yr · Free tier: Run models locally, no subscription required, 3 concurrent requests, no service fees, pay as you go for cloud models · Checked Nov 3, 2026
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Summary
We ran Ollama 0.40.5 in Docker on a CPU-only server, limited to 3 cores, with a 3B model (qwen2.5:3b). Getting started was one command, and a cold start took about 8 seconds to the first answer. One catch: Ollama started 8 threads even though the container had only 3 cores, and across our three runs that made text generation 3.3 to 11.9 times slower. Setting num_thread to 3 fixed it. It also capped the context at the model's trained length of 32,768 tokens when we asked for 65,536. If everything has to stay on your hardware, Ollama documents an OLLAMA_NO_CLOUD=1 setting that turns off its cloud models and web search. You still need a network connection to download models the first time.
Bottom line: A good pick if you want model downloads and a local API handled for you. On a CPU-limited machine or in Docker, set num_thread to match your cores, and turn off the cloud features if nothing should leave your hardware.
Pricing Plans
SubscriptionLast verified 2 days ago- Price
- $20/mo or $200/yr
- Free Tier
- Run models locally, no subscription required, 3 concurrent requests, no service fees, pay as you go for cloud models
Pay as you go
Free
Pay per token for every cloud model
- Run models locally
- No subscription
- 3 concurrent requests
- No service fees
Pro
$20per month
$200/yr Save 17%
For shorter, well-defined day-to-day tasks
- Everything in Pay as you go
- $60 of usage credits per month
- Access to larger pro models
- Fast mode (coming soon)
Team
$500per month
For teams scaling with open models
- Everything in Pro
- Unlimited users
- $1,000 of usage credits per month, shared across the team
- Centralized billing and administration
- Priority support
- Shared projects, skills, and instructions (coming soon)
Enterprise
Custom
For larger teams with volume usage pricing
- Everything in Team
- Model access controls
- Limit team access to specific models
- Set cost budgets for users and API keys
- Private Slack channel with dedicated support
- Custom security questionnaires
View full pricing on ollama.com →
Pricing may have changed since last verified. Check the official site for current plans.
We installed and ran it
- How we installed it
- Official Docker image, one command
- Docker image size on disk
- 9.37 GB
- Model we used
- qwen2.5:3b (Q4_K_M GGUF, 1.9 GB)
- Server start to first answer, model file not cached
- 7.9 s
- Model load time reported by Ollama, model file not cached
- 7.1 s
- Memory used by the model process
- 2.2 GB
- Inside a container limited to 3 CPU cores, Ollama still started 8 threads. Across our three runs that made text generation 3.3x to 11.9x slower than with num_thread set to 3, the cores the container has.
- Ollama capped the context at the model's trained length (32,768 tokens for qwen2.5:3b) when we asked for 65,536.
Measured against LM Studio
| Measured | Ollama | LM Studio |
|---|---|---|
| Model load time from the runtime's own log, model file not cached (s) | 5.9 | 8.1 |
| Start-up, not the same measurement: Ollama = first answer from a fresh server; LM Studio = lms load command (s) | 6.7 | 44.6 |
| Text generation speed, 53-token prompt, relative | 1.38x | 1x |
| Text generation speed, 441-token prompt, relative | 1.22x | 1x |
| Text generation speed, 723-token prompt, relative | 1x | 1x |
| Prompt reading speed, 53-token prompt, relative | 1.32x | 1x |
| Prompt reading speed, 441-token prompt, relative | 1.77x | 1x |
| Prompt reading speed, 723-token prompt, relative | 1.58x | 1x |
| Memory used by the model process (GB) | 2.2 | 2 |
| Docker image size on disk (GB) | 9.37 | 4.68 |
Same GGUF model file, same 3 cores, measured back to back in one session. Speed is shown for each of our three prompt lengths: Ollama's lead in generation speed shrinks as the prompt gets longer. The two start-up rows are different measurements: Ollama's is the first 1-token answer from a freshly started server (the model loads on that request); LM Studio's is the lms load command, most of which passed before its engine started loading the model.
Measured against llama.cpp
| Measured | Ollama | llama.cpp |
|---|---|---|
| Server start to first answer, model file not cached (s) | 7.9 | 8.7 |
| Text generation speed, 53-token prompt, relative | 1.07x | 1x |
| Text generation speed, 441-token prompt, relative | 1.05x | 1x |
| Text generation speed, 723-token prompt, relative | 1.17x | 1x |
| Prompt reading speed, 53-token prompt, relative | 1.06x | 1x |
| Prompt reading speed, 441-token prompt, relative | 1.11x | 1x |
| Prompt reading speed, 723-token prompt, relative | 1.07x | 1x |
| Memory used by the model process (GB) | 2.2 | 1.6 |
| Docker image size on disk (GB) | 9.37 | 1.21 |
Same GGUF model file, same 3 cores, measured back to back in one session. The VM's CPU steal was higher during the llama.cpp run (13.6% vs 7.2%), so differences under about 10% are within noise.
Test machine: 8-vCPU AMD EPYC 9354P cloud VM, each test limited to 3 cores and 10 GB RAM, no GPU, no internet (isolated Docker network). Times in seconds are from this CPU-only machine; a desktop with a GPU will be much faster. The VM is shared, so text-generation speed is shown only as a ratio between tools run back to back, not as tokens per second. Docker image size is the on-disk size from docker image ls for every tool. Local AI tools we tested · How we test · raw data
Find your fit
Before you choose Ollama
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Field notes
All field notes →- Ollama in Docker: check the thread count before changing hardware
In our three-core container, Ollama started eight threads. Explicitly matching the thread setting to the available cores changed generation time substantially.
- Ollama vs llama.cpp: a paired test with the same 3B model
Our controlled pair showed modest generation differences and different process-memory and Docker-image footprints. It did not establish a universal runtime winner.
Pros
- Pulls and runs a model with one command and serves it over a local API.
- Quick cold start in our test: about 8 seconds from server start to first answer.
- OLLAMA_NO_CLOUD=1 turns off its cloud models and web search for local-only use.
- Generated text faster than LM Studio on the same model and cores, by up to 1.38x with short prompts.
Cons
- Ignores a container's CPU limit: it started 8 threads on 3 cores, and generation was 3.3 to 11.9 times slower until we set num_thread to 3.
- Caps context at the model's trained length (32,768 tokens for our model) even when you ask for more, so check what you actually get.
- The largest Docker image of the runtimes we tested: 9.37 GB, against 1.21 GB for llama.cpp.
About
- Platforms
- macOS, Windows, Linux, Docker
- API Available
- Yes
- Self-Hosted
- Yes
Best For
Who it's for
- Developers who want a local model API without managing model files by hand
- People trying open models on their own machine with a short setup
What it does well
- Download and run open models on your own hardware
- Give your apps a local model endpoint
Frequently Asked Questions
- Is Ollama free?
- Ollama offers a free option alongside paid upgrades. Pricing: $20/mo or $200/yr. Check the current allowances and terms before choosing a plan.
- Is Ollama open source?
- Yes. Ollama is open source — the source repository is at https://github.com/ollama/ollama.
- Does Ollama have an API?
- Yes. Ollama exposes a developer API. See the official documentation at https://ollama.com for details.
- Can I self-host Ollama?
- Yes. Ollama supports self-hosting on your own infrastructure.
- What are the alternatives to Ollama?
- Common alternatives include LM Studio, llama.cpp, vLLM, LocalAI. Compare them on AIDiveForge for pricing, features, and platform support.
- When was Ollama released?
- Ollama was first released in 2023.
- What platforms does Ollama support?
- Ollama is available on: macOS, Windows, Linux, Docker.
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Ollama downloads open models and runs them on your own machine behind a simple command line and local API. It also offers optional cloud-hosted models, which run on Ollama's servers instead of yours.
We ran Ollama 0.40.5 in Docker on a CPU-only server, limited to 3 cores, with a 3B model (qwen2.5:3b). Getting started was one command, and a cold start took about 8 seconds to the first answer. One catch: Ollama started 8 threads even though the container had only 3 cores, and across our three runs that made text generation 3.3 to 11.9 times slower. Setting num_thread to 3 fixed it. It also capped the context at the model's trained length of 32,768 tokens when we asked for 65,536. If everything has to stay on your hardware, Ollama documents an OLLAMA_NO_CLOUD=1 setting that turns off its cloud models and web search. You still need a network connection to download models the first time.
A good pick if you want model downloads and a local API handled for you. On a CPU-limited machine or in Docker, set num_thread to match your cores, and turn off the cloud features if nothing should leave your hardware.
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