diff --git a/docs/source/concepts/index.md b/docs/source/concepts/index.md
index df46e0134..27eb74f00 100644
--- a/docs/source/concepts/index.md
+++ b/docs/source/concepts/index.md
@@ -33,7 +33,7 @@ Providers come in two flavors:
 - **Remote**: the provider runs as a separate service external to the Llama Stack codebase. Llama Stack contains a small amount of adapter code.
 - **Inline**: the provider is fully specified and implemented within the Llama Stack codebase. It may be a simple wrapper around an existing library, or a full fledged implementation within Llama Stack.
 
-Most importantly, Llama Stack always strives to provide at least one fully "local" provider for each API so you can iterate on a fully featured environment locally.
+Most importantly, Llama Stack always strives to provide at least one fully inline provider for each API so you can iterate on a fully featured environment locally.
 ## Resources
 
 Some of these APIs are associated with a set of **Resources**. Here is the mapping of APIs to resources:
diff --git a/docs/source/conf.py b/docs/source/conf.py
index a876333db..fd105a6cf 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -15,7 +15,7 @@
 from docutils import nodes
 
 project = "llama-stack"
-copyright = "2024, Meta"
+copyright = "2025, Meta"
 author = "Meta"
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/index.md b/docs/source/index.md
index cb2355bfd..b6fd314b7 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -67,6 +67,7 @@ A number of "adapters" are available for some popular Inference and Vector Store
 |  **Provider** |  **Environments** |
 | :----: | :----: |
 |  FAISS | Single Node |
+|  SQLite-Vec| Single Node |
 |  Chroma | Hosted and Single Node |
 |  Postgres (PGVector) | Hosted and Single Node |
 |  Weaviate | Hosted |
@@ -88,6 +89,7 @@ self
 introduction/index
 getting_started/index
 concepts/index
+providers/index
 distributions/index
 distributions/selection
 building_applications/index
diff --git a/docs/source/providers/index.md b/docs/source/providers/index.md
new file mode 100644
index 000000000..cc654823e
--- /dev/null
+++ b/docs/source/providers/index.md
@@ -0,0 +1,59 @@
+# Providers Overview
+
+The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include:
+- LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, etc.),
+- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, FAISS, PGVector, etc.),
+- Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
+
+Providers come in two flavors:
+- **Remote**: the provider runs as a separate service external to the Llama Stack codebase. Llama Stack contains a small amount of adapter code.
+- **Inline**: the provider is fully specified and implemented within the Llama Stack codebase. It may be a simple wrapper around an existing library, or a full fledged implementation within Llama Stack.
+
+Importantly, Llama Stack always strives to provide at least one fully inline provider for each API so you can iterate on a fully featured environment locally.
+
+## Agents
+Run multi-step agentic workflows with LLMs with tool usage, memory (RAG), etc.
+
+## DatasetIO
+Interfaces with datasets and data loaders.
+
+## Eval
+Generates outputs (via Inference or Agents) and perform scoring.
+
+## Inference
+Runs inference with an LLM.
+
+## Post Training
+Fine-tunes a model.
+
+## Safety
+Applies safety policies to the output at a Systems (not only model) level.
+
+## Scoring
+Evaluates the outputs of the system.
+
+## Telemetry
+Collects telemetry data from the system.
+
+## Tool Runtime
+Is associated with the ToolGroup resouces. 
+
+## Vector IO
+
+Vector IO refers to operations on vector databases, such as adding documents, searching, and deleting documents.
+Vector IO plays a crucial role in [Retreival Augmented Generation (RAG)](../..//building_applications/rag), where the vector
+io and database are used to store and retrieve documents for retrieval.
+
+#### Vector IO Providers
+The following providers (i.e., databases) are available for Vector IO:
+
+```{toctree}
+:maxdepth: 1
+
+vector_io/faiss
+vector_io/sqlite-vec
+vector_io/chromadb
+vector_io/pgvector
+vector_io/qdrant
+vector_io/weaviate
+```
diff --git a/docs/source/providers/vector_io/chromadb.md b/docs/source/providers/vector_io/chromadb.md
new file mode 100644
index 000000000..4a7caf2e1
--- /dev/null
+++ b/docs/source/providers/vector_io/chromadb.md
@@ -0,0 +1,36 @@
+---
+orphan: true
+---
+# Chroma 
+
+[Chroma](https://www.trychroma.com/) is an inline and remote vector 
+database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database. 
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+Chroma supports:
+- Store embeddings and their metadata
+- Vector search
+- Full-text search
+- Document storage
+- Metadata filtering
+- Multi-modal retrieval
+
+## Usage
+
+To use Chrome in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use chroma.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install chroma using pip:
+
+```bash
+pip install chromadb
+```
+
+## Documentation
+See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general.
diff --git a/docs/source/providers/vector_io/faiss.md b/docs/source/providers/vector_io/faiss.md
new file mode 100644
index 000000000..f894190eb
--- /dev/null
+++ b/docs/source/providers/vector_io/faiss.md
@@ -0,0 +1,33 @@
+---
+orphan: true
+---
+# Faiss
+
+[Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It 
+allows you to store and query vectors directly in memory.
+That means you'll get fast and efficient vector retrieval.
+
+## Features
+
+- Lightweight and easy to use
+- Fully integrated with Llama Stack
+- GPU support
+
+## Usage
+
+To use Faiss in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Faiss.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install Faiss using pip:
+
+```bash
+pip install faiss-cpu
+```
+## Documentation
+See [Faiss' documentation](https://faiss.ai/) or the [Faiss Wiki](https://github.com/facebookresearch/faiss/wiki) for 
+more details about Faiss in general.
diff --git a/docs/source/providers/vector_io/pgvector.md b/docs/source/providers/vector_io/pgvector.md
new file mode 100644
index 000000000..919eb88d8
--- /dev/null
+++ b/docs/source/providers/vector_io/pgvector.md
@@ -0,0 +1,31 @@
+---
+orphan: true
+---
+# Postgres PGVector
+
+[PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It 
+allows you to store and query vectors directly in memory.
+That means you'll get fast and efficient vector retrieval.
+
+## Features
+
+- Easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use PGVector in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Faiss.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install PGVector using docker:
+
+```bash
+docker pull pgvector/pgvector:pg17
+```
+## Documentation
+See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general.
diff --git a/docs/source/providers/vector_io/qdrant.md b/docs/source/providers/vector_io/qdrant.md
new file mode 100644
index 000000000..c374ade98
--- /dev/null
+++ b/docs/source/providers/vector_io/qdrant.md
@@ -0,0 +1,31 @@
+---
+orphan: true
+---
+# Qdrant
+
+[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It 
+allows you to store and query vectors directly in memory.
+That means you'll get fast and efficient vector retrieval.
+
+## Features
+
+- Easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use Qdrant in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Faiss.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install Qdrant using docker:
+
+```bash
+docker pull qdrant/qdrant
+```
+## Documentation
+See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
diff --git a/docs/source/providers/vector_io/sqlite-vec.md b/docs/source/providers/vector_io/sqlite-vec.md
new file mode 100644
index 000000000..f5ce4c003
--- /dev/null
+++ b/docs/source/providers/vector_io/sqlite-vec.md
@@ -0,0 +1,33 @@
+---
+orphan: true
+---
+# SQLite-Vec
+
+[SQLite-Vec](https://github.com/asg017/sqlite-vec) is an inline vector database provider for Llama Stack. It 
+allows you to store and query vectors directly within an SQLite database. 
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+
+- Lightweight and easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use SQLite-Vec in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use SQLite-Vec.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install SQLite-Vec using pip:
+
+```bash
+pip install sqlite-vec
+```
+
+## Documentation
+
+See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) for more details about sqlite-vec in general.
diff --git a/docs/source/providers/vector_io/weaviate.md b/docs/source/providers/vector_io/weaviate.md
new file mode 100644
index 000000000..47321781c
--- /dev/null
+++ b/docs/source/providers/vector_io/weaviate.md
@@ -0,0 +1,33 @@
+---
+orphan: true
+---
+# Weaviate 
+
+[Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack. 
+It allows you to store and query vectors directly within a Weaviate database. 
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+Weaviate supports:
+- Store embeddings and their metadata
+- Vector search
+- Full-text search
+- Hybrid search
+- Document storage
+- Metadata filtering
+- Multi-modal retrieval
+
+## Usage
+
+To use Weaviate in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use chroma.
+3. Start storing and querying vectors.
+
+## Installation
+
+To install Weaviate see the [Weaviate quickstart documentation](https://weaviate.io/developers/weaviate/quickstart). 
+
+## Documentation
+See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.