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LLM Stack Architecture

From hosted APIs to fully custom-trained models, DoggyDish helps you choose the right LLM foundation — then scale it with RAG pipelines, fine-tuning, and GPU-optimized infrastructure.

Whether you’re building a lightweight agent or an enterprise-grade multi-agent system, this is where your model strategy begins.

Choose Your Foundation

Hosted | Open-Source | Custom

Adapt Your Model

Fine-Tuning | LoRA | Quantization

Turn It Into an Agent System

RAG | Tool Calling | Multi-Agent | Memory

Model Foundations

Hosted LLM APIs

Fastest Path to Production

  • OpenAI GPT-4/5

  • Anthropic Claude

  • Google Gemini

  • Mistral API

Zero infrastructure

Ideal for MVP agent builds

Great for early-stage SaaS copilots

Best for: Rapid agent prototyping & low-ops teams

Open-Source LLMs

Full Control & Data Ownership

  • Llama 3

  • Mistral 7B / Mixtral

  • Qwen

  • DeepSeek

Deploy via Ollama, vLLM, or TensorRT-LLM

Run on cloud GPU or on-prem hardware

Control inference costs

Best for: Secure, scalable agent systems

Custom LLM Training

Own the Stack

  • Train from scratch (rare)

  • Pre-train adapters

  • Synthetic data pipelines

  • GPU cluster scaling

High-memory GPUs

Distributed training infrastructure

Optimized rack deployments

Best for: AI-native enterprises & research labs

Intelligence Architecture

Fine-Tuning & LoRA

Customize Model Behavior

  • Instruction fine-tuning

  • Domain adaptation

  • LoRA / QLoRA optimization

  • Quantization for efficiency

Improve tone, accuracy, format

Reduce prompt engineering complexity

Lower inference cost over time

Best for: Vertical AI products

Intelligence Architecture

RAG-Enhanced Systems

Train Without Retraining

  • Inject proprietary data

  • Connect vector databases (e.g. Supabase)

  • Build long-term agent memory

  • Reduce hallucinations

Faster than fine-tuning

Ideal for knowledge workers & internal AI

Works with hosted or local LLMs

Best for: Enterprise knowledge agents

Multi-Agent + Tool-Calling

Train Without Retraining

  • Function calling support

  • Structured outputs

  • Tool orchestration

  • Memory frameworks

LangChain, CrewAI, NeMo, n8n

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Best for: Autonomous workflows & SaaS copilots