TrustGraph vs Azure AI Services

TrustGraph and Azure AI services both enable intelligent application development, but they differ fundamentally: integrated open-source platform versus coordinating multiple Azure managed services.

At a Glance

Core Philosophy

TrustGraph: Unified Platform

TrustGraph provides integrated Knowledge Graph platform:

// Single SDK, everything integrated
import { TrustGraph } from "@trustgraph/sdk";

const trustgraph = new TrustGraph({
  endpoint: "https://your-deployment.com",
  // Deploy on Azure, AWS, GCP, on-premise, or hybrid
});

// Unified ingestion pipeline
await trustgraph.ingest({
  sources: [
    "azure-blob://container/docs/",
    "sql://azure-sql-db",
    "cosmosdb://account",
  ],
  graphConfig: {
    extractEntities: true,
    buildRelationships: true,
    ontology: "domain-schema.ttl",
  },
});

// Hybrid retrieval - graph + vector coordinated
const result = await trustgraph.query({
  query: "Analyze customer relationships with product lines",
  strategy: "graph-rag",
  reasoning: "multi-hop",
  graphDepth: 3,
});

// Multi-agent orchestration included
const agents = await trustgraph.createMultiAgent({
  researcher: { role: "research", graph: mainGraph },
  analyzer: { role: "analyze", graph: mainGraph },
  synthesizer: { role: "synthesize", graph: mainGraph },
});

Key characteristics:

• Single platform, single API
• All components work together
• Deploy anywhere
• Open source
• No vendor lock-in

Azure: Service Composition

Azure requires assembling multiple services:

from azure.identity import DefaultAzureCredential
from azure.ai.openai import OpenAIClient
from azure.search.documents import SearchClient
from azure.cosmos import CosmosClient
from azure.storage.blob import BlobServiceClient

# Service 1: Azure OpenAI for LLMs
openai_client = OpenAIClient(
    endpoint="https://your-openai.openai.azure.com",
    credential=DefaultAzureCredential()
)

# Service 2: Azure AI Search for vector search
search_client = SearchClient(
    endpoint="https://your-search.search.windows.net",
    index_name="documents",
    credential=DefaultAzureCredential()
)

# Service 3: Cosmos DB for graph (Gremlin API)
cosmos_client = CosmosClient(
    url="https://your-cosmos.documents.azure.com:443/",
    credential=DefaultAzureCredential()
)

# Service 4: Blob Storage for documents
blob_client = BlobServiceClient(
    account_url="https://youraccount.blob.core.windows.net",
    credential=DefaultAzureCredential()
)

# Service 5: Azure Functions for orchestration
# Service 6: Azure Logic Apps for workflows
# Service 7: Azure Monitor for observability

# You coordinate everything:
# 1. Upload to Blob Storage
# 2. Trigger Function to process
# 3. Extract entities (custom code)
# 4. Store in Cosmos DB Gremlin (custom code)
# 5. Generate embeddings with Azure OpenAI
# 6. Index in Azure AI Search (custom code)
# 7. Query AI Search for vectors
# 8. Query Cosmos DB for graph
# 9. Combine results (custom code)
# 10. Generate response with Azure OpenAI

Key characteristics:

• Multiple services to integrate
• Complex orchestration
• Azure-only deployment
• Usage-based pricing
• Vendor lock-in

Service Comparison

Knowledge Graph: TrustGraph vs Cosmos DB Gremlin

TrustGraph:

// Knowledge Graph is the foundation
await trustgraph.ingest({
  sources: ["documents/"],
  graphConfig: {
    extractEntities: true,
    linkingStrategy: "semantic",
    ontology: "enterprise-ontology.ttl",
    temporalRelationships: true,
  },
});

// Rich Cypher queries
const result = await trustgraph.query({
  cypher: `
    MATCH (customer:Customer)-[:PURCHASED]->(product:Product)
    MATCH (product)-[:BELONGS_TO]->(category:Category)
    WHERE customer.segment = 'Enterprise'
    RETURN category.name, count(product) as products
    ORDER BY products DESC
  `,
});

// Multi-hop reasoning built-in
const analysis = await trustgraph.reason({
  query: "How does trend A affect customer segment B?",
  maxDepth: 4,
  includeInferences: true,
});

// Integrated with vector search and agents
const context = await trustgraph.retrieve({
  query: "customer behavior patterns",
  strategy: "graph-rag",
});

Azure Cosmos DB Gremlin API:

from gremlin_python.driver import client

# Limited Gremlin API (not full graph database)
gremlin_client = client.Client(
    'wss://your-account.gremlin.cosmos.azure.com:443/',
    'g',
    username="/dbs/your-db/colls/your-graph",
    password="your-key"
)

# Manual entity creation
gremlin_client.submit(
    "g.addV('Customer').property('id', 'c1').property('segment', 'Enterprise')"
).all().result()

# Gremlin queries (different from Cypher)
result = gremlin_client.submit("""
    g.V().hasLabel('Customer').has('segment', 'Enterprise')
     .out('PURCHASED').out('BELONGS_TO')
     .groupCount().by('name')
""").all().result()

# Limitations:
# - Limited graph features vs. Neo4j
# - No advanced graph algorithms
# - NOT integrated with Azure AI Search
# - NOT integrated with Azure OpenAI
# - Manual orchestration required
# - Gremlin syntax (not standard Cypher)

Cosmos DB Limitations:

• Not a full-featured graph database
• Limited graph analytics capabilities
• High latency for complex traversals
• Expensive for large graphs
• No built-in graph visualization

Vector Search: TrustGraph vs Azure AI Search

TrustGraph:

// Vector search integrated with Knowledge Graph
await trustgraph.ingest({
  sources: ["docs/"],
  // Automatically creates:
  // - Knowledge Graph entities/relationships
  // - Vector embeddings
  // - Unified index
});

// Hybrid retrieval out-of-box
const results = await trustgraph.retrieve({
  query: "product innovation strategies",
  strategy: "graph-rag",
  vectorTopK: 10,
  graphDepth: 2,
  fusion: "rrf",  // Reciprocal rank fusion
});

// Returns unified context
{
  vectorMatches: [...],  // Semantically similar content
  graphContext: {
    entities: [...],      // Related entities
    relationships: [...], // How they connect
    paths: [...],        // Reasoning chains
  },
  fusedResults: [...],   // Combined and ranked
}

Azure AI Search:

from azure.search.documents import SearchClient
from azure.search.documents.models import VectorizedQuery

# Separate service - manual setup
search_client = SearchClient(
    endpoint="https://your-search.search.windows.net",
    index_name="documents",
    credential=credential
)

# Manually create embeddings
embedding_response = openai_client.embeddings.create(
    model="text-embedding-3-large",
    input=query
)
embedding = embedding_response.data[0].embedding

# Vector search
vector_query = VectorizedQuery(
    vector=embedding,
    k_nearest_neighbors=10,
    fields="contentVector"
)

results = search_client.search(
    search_text=None,
    vector_queries=[vector_query]
)

# NOT integrated with:
# - Cosmos DB graph data
# - Automatic entity extraction
# - Relationship-aware ranking

# Manual integration required:
# 1. Query AI Search for vectors
# 2. Query Cosmos DB for graph
# 3. Write custom code to combine
# 4. Implement your own ranking

LLM Access: TrustGraph vs Azure OpenAI

TrustGraph:

// 40+ LLM providers supported
await trustgraph.configure({
  llm: {
    provider: "openai",  // or anthropic, google, azure, aws, etc.
    model: "gpt-4-turbo",
  },
});

// Switch providers without code changes
await trustgraph.configure({
  llm: {
    provider: "anthropic",
    model: "claude-3-opus",
  },
});

// Use multiple providers
await trustgraph.agents.create({
  name: "multi-model-system",
  models: {
    reasoning: "claude-3-opus",
    coding: "gpt-4-turbo",
    embedding: "openai-3-large",
    summarization: "gemini-pro",
  },
});

// Not locked to one provider

Azure OpenAI Service:

from azure.ai.openai import OpenAIClient

# Azure OpenAI only
client = OpenAIClient(
    endpoint="https://your-resource.openai.azure.com",
    credential=DefaultAzureCredential()
)

# Limited to models in Azure OpenAI:
# - GPT-4, GPT-4 Turbo
# - GPT-3.5
# - text-embedding-ada-002
# - DALL-E (images)

# Cannot use:
# - Latest OpenAI models (may have delays)
# - Anthropic Claude (not available)
# - Google Gemini (not available)
# - Self-hosted models
# - Other providers

response = client.chat.completions.create(
    model="gpt-4",  # Azure deployment name
    messages=[...]
)

# Locked into Azure OpenAI availability
# Pricing controlled by Azure

Deployment & Operations

TrustGraph: Deploy Anywhere

Multiple deployment options:

# Option 1: Deploy on Azure (your choice)
# - AKS (Azure Kubernetes Service)
# - Container Instances
# - Virtual Machines
# - Your VNet
# - Your resource groups

# Option 2: Deploy on AWS
# Option 3: Deploy on GCP
# Option 4: On-premise
# Option 5: Hybrid cloud
# Option 6: Multi-cloud

# Same code works everywhere

// Portable across clouds
const trustgraph = new TrustGraph({
  endpoint: process.env.TRUSTGRAPH_ENDPOINT,
  // Works on Azure, AWS, GCP, on-premise
});

// No cloud-specific code
await trustgraph.ingest({...});
await trustgraph.query({...});

Azure: Azure-Only Deployment

Locked to Azure:

# Must use Azure services
from azure.identity import DefaultAzureCredential

# All services in Azure
openai_client = OpenAIClient(endpoint="https://....openai.azure.com", ...)
search_client = SearchClient(endpoint="https://....search.windows.net", ...)
cosmos_client = CosmosClient(url="https://....documents.azure.com", ...)

# Cannot deploy:
# - On AWS
# - On GCP
# - On-premise (except Azure Stack)
# - Air-gapped
# - Multi-cloud

# If Azure has outage, you're down
# If Azure changes pricing, no alternatives
# If Microsoft discontinues service, must migrate

Cost Structure

TrustGraph: Predictable Costs

Infrastructure-based pricing:

Scenario: 1M documents, 10M queries/month

TrustGraph on Azure (self-managed):
- AKS cluster (3 nodes): $240/month
- Graph DB (Neo4j on VMs): $500/month
- Vector store (Qdrant): $200/month
- Storage (Managed Disks + Blob): $120/month
- Networking: $60/month
- LLM APIs (external OpenAI): $2,000/month

Total: ~$3,120/month

Benefits:
✅ Predictable monthly costs
✅ No per-request charges
✅ Unlimited queries
✅ Choose cheapest LLM provider
✅ Scale infrastructure as needed
✅ No surprise bills

TrustGraph becomes cheaper than Azure AI at:
~100K documents and 1M queries/month

Azure: Usage-Based Pricing

Pay-per-use model:

Scenario: 1M documents, 10M queries/month

Azure AI Services:
- Azure OpenAI (LLM): $6,000/month
  - Input: 500M tokens × $0.01/1K = $5,000
  - Output: 100M tokens × $0.03/1K = $3,000
  - Embedding: 1B tokens × $0.0001/1K = $100
- Azure AI Search: $2,500/month
  - Standard S2: $1,000/month (base)
  - Storage: 100GB × $0.40/GB = $40
  - Queries: 10M × $0.00015 = $1,500
- Cosmos DB: $3,000/month
  - Provisioned throughput: 10,000 RU/s
  - Storage: 500GB
- Blob Storage: $200/month
- Azure Functions: $400/month
- Networking: $300/month

Total: ~$15,400/month

Challenges:
⚠️ Costs scale with usage
⚠️ Hard to predict bills
⚠️ Azure OpenAI can be expensive
⚠️ Cosmos DB RU/s pricing complex
⚠️ AI Search query charges add up
⚠️ Multiple service charges

At low volume (less than 10K docs, less than 100K queries):
Azure may be cheaper

At scale (more than 100K docs, more than 1M queries):
TrustGraph is 5x more cost-effective

Integration Complexity

TrustGraph: Simple Integration

Single platform:

// One SDK
import { TrustGraph } from "@trustgraph/sdk";

const tg = new TrustGraph({ endpoint: "..." });

// Ingest
await tg.ingest({ sources: ["azure-blob://..."] });

// Query
const results = await tg.query({
  query: "...",
  strategy: "graph-rag",
});

// Agents
const agents = await tg.createMultiAgent({...});

// Done - ~10 lines

Azure: Complex Orchestration

Multiple services to coordinate:

# Install multiple SDKs
# pip install azure-ai-openai azure-search-documents azure-cosmos
# pip install azure-storage-blob azure-functions azure-identity

from azure.identity import DefaultAzureCredential
from azure.ai.openai import OpenAIClient
from azure.search.documents import SearchClient
from azure.cosmos import CosmosClient
from azure.storage.blob import BlobServiceClient
import azure.functions as func

# 1. Set up authentication
credential = DefaultAzureCredential()

# 2. Initialize 4+ clients
openai_client = OpenAIClient(...)
search_client = SearchClient(...)
cosmos_client = CosmosClient(...)
blob_client = BlobServiceClient(...)

# 3. Upload documents to Blob Storage
container_client = blob_client.get_container_client("docs")
container_client.upload_blob(...)

# 4. Create Azure Function for processing
@app.function_name("ProcessDocument")
@app.blob_trigger(...)
def process_document(blob: func.InputStream):
    # Extract text
    text = blob.read()

    # Extract entities (custom NLP code)
    entities = extract_entities(text)

    # Store in Cosmos DB
    for entity in entities:
        cosmos_client.create_item(...)

    # Generate embeddings
    embedding = openai_client.embeddings.create(...)

    # Index in AI Search
    search_client.upload_documents(...)

# 5. Create query function
def hybrid_query(query_text):
    # Vector search in AI Search
    embedding = openai_client.embeddings.create(...)
    search_results = search_client.search(vector=embedding)

    # Graph query in Cosmos DB
    graph_results = cosmos_client.query_items(...)

    # Manual result fusion
    combined = merge_results(search_results, graph_results)

    # Generate response
    response = openai_client.chat.completions.create(
        messages=[{"role": "system", "content": str(combined)}, ...]
    )

    return response

# Result: 100s of lines of code
# Complex error handling
# Service-specific monitoring
# Distributed debugging

Advanced Capabilities

TrustGraph: Built-In Features

Platform capabilities:

✅ Multi-hop reasoning: Graph traversal with inference ✅ Knowledge Cores: Modular knowledge bases ✅ Temporal graphs: Time-aware relationships ✅ Three-dimensional visualization: Interactive graph explorer ✅ Ontology support: Schema-driven graphs (OWL, RDFS) ✅ MCP integration: Model Context Protocol native ✅ Multi-agent orchestration: Coordinated intelligent agents ✅ Graph analytics: PageRank, communities, centrality ✅ Hybrid retrieval: Graph + vector unified ✅ Provenance: Complete audit trails ✅ Relationship types: Typed, weighted edges

Azure: Manual Implementation

You build:

⚠️ Multi-hop reasoning: Write Gremlin traversals ⚠️ Knowledge organization: Design your system ⚠️ Temporal queries: Implement manually ⚠️ Visualization: Use separate tools ⚠️ Schema management: Manual Cosmos DB schema ⚠️ Integrations: Custom Azure Functions ⚠️ Agent orchestration: Build with Logic Apps ⚠️ Graph analytics: Limited in Cosmos DB ⚠️ Hybrid retrieval: Coordinate services manually ⚠️ Audit trails: Configure Azure Monitor ⚠️ Relationship handling: Basic Gremlin edges

Data Sovereignty & Compliance

TrustGraph: Deploy Anywhere

Complete control:

// Healthcare (HIPAA)
const trustgraph = new TrustGraph({
  deployment: "on-premise",
  region: "us-healthcare-dc",
  encryption: {
    atRest: "AES-256",
    inTransit: "TLS 1.3",
    keyManagement: "hsm",
  },
  compliance: ["HIPAA"],
});

// Financial (PCI-DSS, SOX)
const trustgraph = new TrustGraph({
  deployment: "private-cloud",
  region: "us-financial-dc",
  compliance: ["PCI-DSS", "SOX"],
});

// Government (FedRAMP)
const trustgraph = new TrustGraph({
  deployment: "air-gapped",
  clearanceLevel: "secret",
});

// EU (GDPR)
const trustgraph = new TrustGraph({
  deployment: "eu-datacenter",
  dataResidency: "EU",
  rightToErasure: "enabled",
});

Azure: Azure Regions

Azure-defined compliance:

# Compliance depends on Azure certifications
# - HIPAA: Azure is HIPAA-compliant (sign BAA)
# - PCI-DSS: Azure is PCI-DSS certified
# - SOC 2: Azure has SOC 2 attestation
# - FedRAMP: Use Azure Government

# Limitations:
# - Must use Azure
# - Cannot deploy on-premise (except Azure Stack)
# - Cannot do air-gapped
# - Region availability varies
# - Some services not in all regions
# - Azure OpenAI has limited region availability

# Example: HIPAA deployment
openai_client = OpenAIClient(
    endpoint="https://your-resource.openai.azure.com",
    # Must be in HIPAA-compliant region
    # Must sign BAA with Microsoft
    # Must use HIPAA-eligible services
)

# Still in Azure cloud, not on-premise
# Data residency controlled by Azure

Use Case Recommendations

Choose TrustGraph For:

1. Cost-Sensitive Applications

   • High document/query volumes
   • Need predictable costs
   • Want to optimize spend
   • Budget constraints

2. Data Sovereignty

   • On-premise required
   • Air-gapped environments
   • Specific geographic requirements
   • Cannot use public cloud

3. Multi-Cloud Strategy

   • Don't want Azure lock-in
   • Need portability
   • Hybrid architecture
   • Cloud flexibility

4. Complex Graph Reasoning

   • Multi-hop queries essential
   • Relationship understanding critical
   • Graph analytics needed
   • Knowledge Graph is core

5. Custom Requirements

   • Need specific technologies
   • Custom processing pipelines
   • Source code access
   • Proprietary integrations

Choose Azure Services For:

1. Azure-Native Applications

   • Heavily invested in Azure
   • Azure expertise in team
   • Azure Enterprise Agreement
   • Other Azure services used

2. Low Volume Projects

   • less than 10K documents
   • less than 100K queries/month
   • Pay-as-you-go attractive
   • Experimentation phase

3. Simple RAG Use Cases

   • Don't need Knowledge Graph
   • Basic vector search sufficient
   • AI Search capabilities adequate
   • Limited integration needed

4. Microsoft Ecosystem

   • Office 365 integration
   • Power Platform usage
   • Microsoft enterprise support
   • Azure AD integration

Migration Paths

From Azure to TrustGraph

Migration process:

# Export from Azure services
# 1. Export Cosmos DB graph
cosmos_data = export_cosmos_graph()

# 2. Export AI Search documents
documents = export_ai_search_docs()

# 3. Export Blob Storage
blobs = export_blob_storage()

# Import to TrustGraph
trustgraph.import_graph(cosmos_data)
trustgraph.ingest(documents)

# Benefits:
# - Simplify from 7+ services to 1 platform
# - Reduce costs (typical 5x reduction)
# - Gain portability
# - More advanced graph features
# - Unified API

TrustGraph on Azure

Use Azure infrastructure, not Azure AI services:

// Deploy TrustGraph on Azure infrastructure
const trustgraph = new TrustGraph({
  deployment: {
    cloud: "azure",
    region: "eastus",
    compute: "aks",
    storage: "managed-disk",
  },
  llm: {
    provider: "openai",  // Not Azure OpenAI
    // Or: "anthropic", "google", etc.
  },
});

// Benefits:
// ✅ Use Azure infrastructure
// ✅ Not locked to Azure AI services
// ✅ 5x lower costs than Azure AI services
// ✅ Can migrate to other clouds
// ✅ Unified platform
// ✅ Choose any LLM provider

Conclusion

TrustGraph vs Azure AI Services:

Choose TrustGraph when you need:

• Cost-effective scaling (more than 100K docs, more than 1M queries/month)
• Unified Knowledge Graph platform
• Deploy anywhere (Azure, AWS, GCP, on-premise)
• No vendor lock-in
• Predictable costs
• Advanced graph reasoning
• Open source flexibility
• Multi-LLM provider support
• Full-featured graph database

Choose Azure Services when you need:

• Azure-native integration
• Low volume pay-as-you-go
• Microsoft ecosystem integration
• Azure Enterprise Agreement
• Corporate Azure mandate
• Simple RAG without graphs

For production applications at scale, TrustGraph delivers a more cost-effective, flexible, and integrated solution. You can deploy TrustGraph on Azure infrastructure to get Azure benefits without the vendor lock-in and high costs of Azure AI services.

Additional Resources

Azure AI Services:

• Official Website
• Documentation

TrustGraph:

• TrustGraph Website
• TrustGraph Documentation
• TrustGraph GitHub

Next Steps

• TrustGraph Documentation
• Deploy TrustGraph on Azure
• TrustGraph vs AWS AI
• TrustGraph vs GCP AI