llama-stack-mirror/docs/source/introduction/index.md
Chris Khanoyan 5b0d778871
Update index.md (#888)
Fixing the bullets

# What does this PR do?

The bullets were not there as intended so I helped fix them. 

- [x] Addresses issue (#issue)

## Test Plan

Please describe:

Ran the test, and the bullets are there now to be consistent with the
page.

## Sources

N/A

## Before submitting

- [x] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
2025-01-28 04:55:41 -08:00

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Markdown

# Why Llama Stack?
Building production AI applications today requires solving multiple challenges:
**Infrastructure Complexity**
- Running large language models efficiently requires specialized infrastructure.
- Different deployment scenarios (local development, cloud, edge) need different solutions.
- Moving from development to production often requires significant rework.
**Essential Capabilities**
- Safety guardrails and content filtering are necessary in an enterprise setting.
- Just model inference is not enough - Knowledge retrieval and RAG capabilities are required.
- Nearly any application needs composable multi-step workflows.
- Finally, without monitoring, observability and evaluation, you end up operating in the dark.
**Lack of Flexibility and Choice**
- Directly integrating with multiple providers creates tight coupling.
- Different providers have different APIs and abstractions.
- Changing providers requires significant code changes.
### Our Solution: A Universal Stack
```{image} ../../_static/llama-stack.png
:alt: Llama Stack
:width: 400px
```
Llama Stack addresses these challenges through a service-oriented, API-first approach:
**Develop Anywhere, Deploy Everywhere**
- Start locally with CPU-only setups
- Move to GPU acceleration when needed
- Deploy to cloud or edge without code changes
- Same APIs and developer experience everywhere
**Production-Ready Building Blocks**
- Pre-built safety guardrails and content filtering
- Built-in RAG and agent capabilities
- Comprehensive evaluation toolkit
- Full observability and monitoring
**True Provider Independence**
- Swap providers without application changes
- Mix and match best-in-class implementations
- Federation and fallback support
- No vendor lock-in
**Robust Ecosystem**
- Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies).
- Ecosystem offers tailored infrastructure, software, and services for deploying Llama models.
### Our Philosophy
- **Service-Oriented**: REST APIs enforce clean interfaces and enable seamless transitions across different environments.
- **Composability**: Every component is independent but works together seamlessly
- **Production Ready**: Built for real-world applications, not just demos
- **Turnkey Solutions**: Easy to deploy built in solutions for popular deployment scenarios
- **Llama First**: Explicit focus on Meta's Llama models and partnering ecosystem
With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations.