Running AI models directly on your own computer is becoming one of the biggest developer trends of 2026.
Instead of sending every prompt, document, or line of code to a cloud service, tools such as Ollama and Microsoft Foundry Local can run large language models directly on your PC.
Both platforms promise faster local inference, better privacy, offline access, and freedom from per-token API fees. But they are designed for slightly different users.
So, Foundry Local vs Ollama: which one should you use?
For most developers who want to experiment with different local AI models, build prototypes, or connect applications to a simple local API, Ollama is currently the easier choice.
If you are building a Windows or .NET application and want Microsoft-managed hardware optimisation, SDK integration, and a more production-oriented local AI runtime, Foundry Local is extremely interesting.
Here is the full comparison.
What Is Ollama?
Ollama is a local AI platform that makes it easy to download and run large language models on your own computer.
Instead of manually configuring Python environments, CUDA libraries, model weights, inference engines, and API servers, Ollama handles most of the process for you.
On Windows, Ollama runs as a native application in the background. After installation, you can interact with it from PowerShell, Command Prompt, Windows Terminal, or through its local API.
Ollama officially supports Windows 10 22H2 and newer, and its API is normally available at:
http://localhost:11434
For example, once Ollama is installed you can start a model from the terminal:
ollama run llama3.2
You can also download a model without immediately starting a chat:
ollama pull llama3.2
That simplicity is one of the main reasons Ollama has become popular among developers experimenting with local AI.
What Is Microsoft Foundry Local?
Microsoft Foundry Local is Microsoft’s on-device AI inference platform.
It allows developers to run supported AI models directly on local hardware instead of sending every request to Microsoft’s cloud services.
Microsoft announced the general availability of Foundry Local on April 9, 2026, positioning it as a production-grade local AI runtime for applications that require privacy, low latency, offline capability, or reduced cloud dependency.
Foundry Local also manages several complicated parts of local AI automatically.
According to Microsoft’s documentation, it can download models, cache them locally, manage execution providers, and choose an appropriate model variant for the available hardware.
On Windows, the CLI can be installed using:
winget install Microsoft.FoundryLocal
You can then check the installation:
foundry --version
And run a supported model using a command such as:
foundry model run phi-4-mini
Microsoft’s current Windows development quick-start lists Windows 11 version 24H2 or later, .NET 9 or later, and a DirectX 12-capable GPU as prerequisites.
Foundry Local vs Ollama: Quick Comparison
| Feature | Ollama | Microsoft Foundry Local |
|---|---|---|
| Best for | Developers, hobbyists, local AI experiments | App developers and Microsoft ecosystem |
| Installation | Very easy | Easy |
| Windows support | Windows 10 22H2+ | Current Windows quick-start targets Windows 11 24H2+ |
| Model selection | Very large ecosystem | Curated/optimised catalog |
| Local API | Yes | Yes |
| OpenAI-compatible API | Yes | Yes |
| Dedicated GPU required | No | Not necessarily, but current Windows quick-start requires a DX12-capable GPU |
| Microsoft integration | Limited | Excellent |
| Offline inference | Yes after models are downloaded | Yes after required components/models are available locally |
| Hardware optimisation | Automatic where supported | Strong hardware-aware model selection |
| Best for beginners | Yes | Moderate |
| Best for .NET apps | Good | Excellent |
The biggest difference is not simply performance.
It is philosophy.
Ollama focuses heavily on making local models easy to download, run, and expose through an API.
Foundry Local is increasingly focused on helping developers embed local AI inside applications.
Installing Ollama on Windows
Ollama provides a native Windows installer and also supports installation from PowerShell.
One option is:
irm https://ollama.com/install.ps1 | iex
After installation, open a new PowerShell window and run:
ollama --version
Then start a model:
ollama run llama3.2
The first launch downloads the required model files, so the initial run can take some time depending on the model size and your internet connection.
Local models can also require significant disk space. Ollama notes that the application itself requires at least several gigabytes of storage and downloaded models can consume tens or even hundreds of gigabytes.
Installing Foundry Local on Windows
For Foundry Local, open PowerShell or Windows Terminal and run:
winget install Microsoft.FoundryLocal
Close and reopen the terminal, then verify the installation:
foundry --version
You can explore available models using the Foundry CLI and then run a model locally.
For example:
foundry model run phi-4-mini
One useful feature is that Foundry Local can select model variants based on the hardware available on the device rather than forcing the developer to manually choose every optimisation option.
That could become especially useful as more Windows laptops include NPUs and specialised AI hardware.
Which Has the Better Model Library?
This is an area where Ollama currently has a major advantage for general experimentation.
Ollama is designed around a broad library of models and makes switching between models extremely simple.
You can test one model:
ollama run llama3.2
Then another:
ollama run gemma3
Or another model suited to coding, reasoning, or experimentation.
Foundry Local takes a more curated approach.
Instead of trying to expose every available model, Microsoft focuses more heavily on model variants that can be optimised for supported hardware and application deployment.
That means the choice depends on what you are doing.
If your goal is:
“I want to experiment with lots of local LLMs.”
Choose Ollama.
If your goal is:
“I want to ship a local AI feature inside an application.”
Foundry Local deserves serious consideration.
Local API: Ollama vs Foundry Local
Both platforms can expose local AI models through APIs.
Ollama’s default API endpoint is:
http://localhost:11434
This makes it very easy to connect a local model to applications written in Python, JavaScript, Node.js, C#, or other languages.
Ollama also supports OpenAI-compatible interfaces, which means software originally designed around an OpenAI-style API can often be adapted to work with a local model.
Foundry Local also provides OpenAI-compatible interfaces.
Microsoft documents a /v1/chat/completions endpoint compatible with the OpenAI Chat Completions API.
This is important because it reduces vendor lock-in at the application level.
A developer could theoretically build an application around an OpenAI-style client and then point it toward:
- a cloud model,
- Ollama,
- Foundry Local,
- or another compatible inference server.
That architecture makes hybrid AI applications much easier to build.
Privacy: Is Local AI Really Private?
Privacy is one of the biggest reasons developers are experimenting with local AI.
With a truly local inference workflow, your prompt is processed on your own hardware instead of being sent to a remote cloud inference service.
That can be useful when working with:
- private documents,
- internal company data,
- source code,
- customer information,
- research notes,
- development environments,
- or offline systems.
However, local does not automatically mean completely disconnected from the internet.
You still need internet access when downloading models, updates, packages, or other components.
You should also verify what any third-party application connected to your local model is doing with your data.
The safest way to think about local AI is:
The model inference can stay local, but the complete privacy of your workflow still depends on every application connected to it.
Which Is Faster?
There is no universal winner.
Local AI performance depends on several factors:
- CPU
- GPU
- NPU
- system RAM
- GPU VRAM
- model size
- quantisation
- context length
- driver support
- execution provider
A small model on a modern laptop can feel surprisingly responsive.
A much larger model on the same computer may be painfully slow or may not fit into available memory at all.
Foundry Local’s strongest performance advantage is its hardware-aware approach. Microsoft says the platform can select optimised model variants based on the hardware available on the device.
Ollama also supports GPU acceleration, including supported NVIDIA and AMD hardware on Windows.
For normal users, the most important rule is simple:
Do not automatically download the largest model you can find.
Start with a smaller model and increase model size only when your hardware can comfortably handle it.
Ollama Is Better If You…
Ollama is probably the better choice if you:
- want the fastest path to experimenting with local LLMs;
- like working from the terminal;
- want access to a broad range of models;
- are building local AI prototypes;
- need a simple localhost API;
- want to connect tools such as Python or Node.js applications to a local model;
- use Windows 10 as well as Windows 11;
- want a large community and ecosystem around local AI.
For developers learning about local LLMs, Ollama remains one of the easiest places to start.
Foundry Local Is Better If You…
Foundry Local becomes more attractive if you:
- primarily develop for Windows;
- build applications using .NET;
- want Microsoft-supported local AI tooling;
- need hardware-aware model deployment;
- want to embed inference directly into an application;
- are working toward enterprise or production deployment;
- expect to combine local inference with Microsoft cloud AI later.
Microsoft is also integrating local AI more deeply into its broader development ecosystem. For example, Microsoft’s Agent Framework documentation now includes both Ollama and Foundry Local as model-provider options, showing how important local inference is becoming in modern AI application development.
Can Foundry Local Replace Ollama?
For most people, not completely.
The two products overlap, but they are not identical.
Ollama is excellent as a flexible local model runner.
Foundry Local is increasingly becoming an application-focused local AI runtime.
There is also no rule saying you must choose only one.
A developer might use Ollama for quickly testing different models and then use Foundry Local when developing a Windows application that needs a more controlled deployment strategy.
In more advanced projects, developers can even design applications where the model provider is interchangeable.
Microsoft has already published examples of architectures that route workloads between cloud Microsoft Foundry and local runtimes such as Ollama or Foundry Local.
That hybrid approach could become increasingly common.
Foundry Local vs Ollama: Which Should You Choose?
Here is the simplest answer.
Choose Ollama if:
You want to download models and start experimenting immediately.
It has a mature local AI workflow, a simple command-line interface, a broad model ecosystem, and an easy local API.
Choose Foundry Local if:
You want to build local AI directly into an application, particularly in the Microsoft and Windows ecosystem.
Its hardware-aware runtime, SDK approach, and OpenAI-compatible interfaces make it one of the most interesting local AI platforms to watch in 2026.
Final Verdict
For most individual developers today, Ollama is still the best starting point.
It is simple, flexible, widely supported, and makes experimenting with different local models extremely easy.
However, Microsoft Foundry Local may be the more important platform for Windows application developers over the long term.
Microsoft is treating local AI as more than a chatbot running on a laptop. Foundry Local is being positioned as infrastructure developers can use to add private, offline-capable AI directly to applications.
The real winner may therefore depend on what you are building.
Ollama wins for experimentation.
Foundry Local wins for Microsoft-focused application integration.
And for developers building hybrid AI applications, the best answer may eventually be:
use both.
Frequently Asked Questions
Is Foundry Local free?
Microsoft describes Foundry Local as running models locally without cloud dependency or per-token inference costs. Individual models and related software can still have their own licence terms.
Is Ollama free?
Ollama can be installed and used locally without paying per-token inference fees. Always check the licence of the specific model you download.
Does Ollama work on Windows 11?
Yes. Ollama supports Windows 10 22H2 and newer, including Windows 11.
Does Foundry Local work without an internet connection?
Once the required runtime components and model files have been downloaded, local inference can operate without relying on a cloud inference API. Some setup, downloading, and updates still require connectivity.
Is Foundry Local better than Ollama?
Neither platform is better for every use case. Ollama is generally better for broad model experimentation and straightforward local APIs, while Foundry Local is particularly attractive for application developers who want Microsoft integration and hardware-aware deployment.
Can I connect my own application to Ollama?
Yes. Ollama runs a local API on localhost:11434, making it straightforward to connect applications written in languages such as Python, JavaScript, or C#.
Does Foundry Local support OpenAI-compatible APIs?
Yes. Microsoft documents OpenAI-compatible request formats, including local chat-completion APIs.
