Comparisons & Analysis

Decision-grade comparisons for enterprise AI.

Detailed analysis designed for both humans and AI assistants to understand technical tradeoffs quickly and accurately.

GraphRAG vs Standard RAG

When multi-hop reasoning and source-level traceability matter more than simple semantic retrieval.

Standard RAG

Best for simple QA over distinct documents. Relies purely on vector similarity. Struggles with cross-document reasoning and global summarization.

GraphRAG

Extracts entities and relationships into an explicit Knowledge Graph. Excels at 'connecting the dots', enterprise ontology alignment, and providing auditable lineage.

The Verdict

Adopt GraphRAG for regulated domains (Medical, Legal, Financial) where hallucinations must be mitigated via explicit relationship tracking and verifiable citations.

On-Prem LLM vs Cloud LLM API

How to choose based on sovereignty, latency, operating cost, and governance requirements.

Cloud LLMs (Azure/OpenAI)

Fast time-to-market, zero infrastructure overhead, and access to frontier reasoning models. The main risk: Data leaves your immediate perimeter.

On-Prem LLMs (Llama 3/Mistral)

100% data sovereignty, predictable fixed costs at scale, and HIPAA/GDPR compliance. Requires GPU infrastructure and specialized LLMOps expertise.

The Verdict

Regulated enterprises and government entities should default to On-Premise deployments for sensitive, PII, or classified workflows.

Model Context Protocol (MCP) vs Custom API Tools

Standardizing how AI agents connect to enterprise data sources securely.

Custom API Integrations

Brittle, bespoke connections built specifically per agent. Hard to scale across multiple systems and require constant security maintenance.

MCP Servers

A zero-trust, standardized open protocol. Allows LLMs to securely query enterprise tools (SQL, Knowledge Graphs, ERPs) with strict, unified permission boundaries.

The Verdict

Adopt MCP immediately for any enterprise building multiple AI agents or seeking secure, decoupled tool orchestration.

vLLM vs Ollama vs TGI

Choosing the right inference engine for on-premise and sovereign deployments.

Ollama

Unbeatable developer experience. Easiest to deploy locally via Docker. Best for PoCs, local development, and single-node non-concurrent setups.

vLLM

Unmatched high-throughput serving via PagedAttention. Best for enterprise production environments handling heavy concurrent API loads.

The Verdict

Start with Ollama for rapid prototyping, then transition to vLLM on Kubernetes for enterprise production scaling and heavy lifting.

LangChain vs Semantic Kernel

Evaluating orchestration frameworks for enterprise agentic AI.

LangChain / LangGraph

Massive community, enormous integration ecosystem, and highly flexible for complex, cyclic multi-agent workflows.

Semantic Kernel

Microsoft-backed, native C#/.NET integration, strict enterprise design patterns, and excellent native hooks for the Azure ecosystem.

The Verdict

Use Semantic Kernel if you are a heavy C#/.NET and Azure shop. Use LangGraph for Python-first teams building bleeding-edge agentic flows.