TrustGraph vs Google Cloud AI

TrustGraph and Google Cloud AI both enable building intelligent applications, but they differ fundamentally: integrated open-source platform versus orchestrating multiple GCP managed services.

At a Glance

Core Philosophy

TrustGraph: Unified Platform

TrustGraph provides integrated Knowledge Graph solution:

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

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

// Unified ingestion pipeline
await trustgraph.ingest({
  sources: [
    "gs://bucket/documents/",
    "bigquery://project.dataset",
    "cloudsql://instance/database",
  ],
  graphConfig: {
    extractEntities: true,
    buildRelationships: true,
    ontology: "enterprise-ontology.ttl",
  },
});

// Hybrid retrieval - graph + vector automatic
const result = await trustgraph.query({
  query: "Analyze product relationships in market segment",
  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, unified API
• All components integrated
• Deploy anywhere
• Open source
• No vendor lock-in

GCP: Service Composition

Google Cloud requires composing multiple services:

from google.cloud import aiplatform
from google.cloud import discoveryengine
from google.cloud import storage
from google.cloud import bigquery
from google.cloud.graph import GraphDatabase

# Service 1: Vertex AI for LLMs
aiplatform.init(project="your-project", location="us-central1")

# Service 2: Vertex AI Vector Search
vector_search_client = aiplatform.MatchingEngineIndexEndpoint(...)

# Service 3: Enterprise Search for document retrieval
search_client = discoveryengine.SearchServiceClient()

# Service 4: Graph Database (managed Neo4j or Spanner Graph)
graph_db = GraphDatabase(...)

# Service 5: Cloud Storage for documents
storage_client = storage.Client()

# Service 6: BigQuery for structured data
bigquery_client = bigquery.Client()

# Service 7: Cloud Functions for orchestration
# Service 8: Cloud Run for services
# Service 9: Pub/Sub for messaging

# You coordinate everything:
# 1. Upload to Cloud Storage
bucket = storage_client.bucket('docs')
bucket.blob('doc.pdf').upload_from_filename('doc.pdf')

# 2. Process with Cloud Function (custom code)
# 3. Extract entities (custom NLP with Vertex AI)
# 4. Store in Graph Database (custom code)
# 5. Generate embeddings with Vertex AI
# 6. Index in Vector Search (custom code)
# 7. Query Enterprise Search
# 8. Query Graph Database
# 9. Combine results (custom code)
# 10. Generate response with Vertex AI

Key characteristics:

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

Service Comparison

Knowledge Graph: TrustGraph vs GCP Graph Solutions

TrustGraph:

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

// Rich Cypher queries
const result = await trustgraph.query({
  cypher: `
    MATCH (company:Company)-[:INVESTS_IN]->(startup:Startup)
    MATCH (startup)-[:OPERATES_IN]->(sector:Sector)
    WHERE company.region = 'Silicon Valley'
    RETURN sector.name,
           count(startup) as investments,
           sum(startup.valuation) as total_value
    ORDER BY investments DESC
  `,
});

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

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

GCP Graph Database:

# Option 1: Managed Neo4j (Google Cloud Marketplace)
from neo4j import GraphDatabase

neo4j_driver = GraphDatabase.driver(
    "bolt://neo4j-instance:7687",
    auth=("neo4j", "password")
)

# Manual setup and management
# Not integrated with other GCP AI services

# Option 2: Spanner Graph (preview)
from google.cloud import spanner

spanner_client = spanner.Client()
instance = spanner_client.instance('your-instance')
database = instance.database('your-database')

# GraphQL-like queries (not Cypher)
with database.snapshot() as snapshot:
    results = snapshot.execute_sql("""
        GRAPH FinGraph
        MATCH (c:Company)-[i:INVESTS_IN]->(s:Startup)
        RETURN c, s
    """)

# Limitations:
# - Spanner Graph in preview (limited features)
# - Neo4j on GCP not managed (manual setup)
# - NOT integrated with Vertex AI
# - NOT integrated with Enterprise Search
# - Manual orchestration required
# - Complex pricing (Spanner or Marketplace)

Vector Search: TrustGraph vs Vertex AI Vector 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: "AI research breakthroughs",
  strategy: "graph-rag",
  vectorTopK: 10,
  graphDepth: 2,
  fusion: "weighted",
});

// Returns unified context
{
  vectorMatches: [...],  // Semantically similar
  graphContext: {
    entities: [...],      // Related entities
    relationships: [...], // Connections
    paths: [...],        // Reasoning chains
  },
  fusedResults: [...],   // Combined
}

Vertex AI Vector Search:

from google.cloud import aiplatform

# Separate service - manual setup
index = aiplatform.MatchingEngineIndex(
    index_name="projects/.../locations/.../indexes/..."
)

endpoint = aiplatform.MatchingEngineIndexEndpoint(
    index_endpoint_name="projects/.../locations/.../indexEndpoints/..."
)

# Manually create embeddings
from vertexai.language_models import TextEmbeddingModel

embedding_model = TextEmbeddingModel.from_pretrained(
    "textembedding-gecko@001"
)
embeddings = embedding_model.get_embeddings([query])

# Vector search
response = endpoint.find_neighbors(
    deployed_index_id="deployed_index_id",
    queries=embeddings[0].values,
    num_neighbors=10
)

# NOT integrated with:
# - Graph Database
# - Enterprise Search
# - Automatic entity extraction

# Manual integration required:
# 1. Query Vector Search
# 2. Query Graph Database separately
# 3. Query Enterprise Search separately
# 4. Write custom fusion logic
# 5. Combine results manually

LLM Access: TrustGraph vs Vertex AI

TrustGraph:

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

// Switch providers without code changes
await trustgraph.configure({
  llm: {
    provider: "openai",
    model: "gpt-4-turbo",
  },
});

// Use multiple providers for different tasks
await trustgraph.agents.create({
  name: "multi-model-system",
  models: {
    reasoning: "claude-3-opus",
    coding: "gpt-4-turbo",
    embedding: "textembedding-gecko",
    vision: "gemini-pro-vision",
  },
});

// Not locked to one provider

Vertex AI:

import vertexai
from vertexai.generative_models import GenerativeModel

vertexai.init(project="your-project", location="us-central1")

# Vertex AI models only
available_models = [
    "gemini-pro",
    "gemini-pro-vision",
    "text-bison",
    "chat-bison",
    "codechat-bison",
    # Google-curated selection
]

# Cannot easily use:
# - Latest OpenAI models (GPT-4o, o1)
# - Anthropic Claude (not integrated)
# - AWS Bedrock models
# - Self-hosted models
# - Direct access to other providers

model = GenerativeModel("gemini-pro")
response = model.generate_content("prompt")

# Locked into Vertex AI model availability
# Pricing controlled by Google Cloud

Deployment & Operations

TrustGraph: Deploy Anywhere

Multiple deployment options:

# Option 1: Deploy on GCP (your control)
# - GKE (Google Kubernetes Engine)
# - Cloud Run
# - Compute Engine
# - Your VPC
# - Your network policies

# Option 2: Deploy on AWS
# Option 3: Deploy on Azure
# 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 GCP, AWS, Azure, on-premise
});

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

GCP: GCP-Only Deployment

Locked to Google Cloud:

from google.cloud import aiplatform
from google.auth import default

# Must use GCP
credentials, project = default()
aiplatform.init(project=project, location="us-central1")

# All services in GCP
vertex_client = aiplatform.Client()
search_client = discoveryengine.SearchServiceClient()
storage_client = storage.Client()

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

# If GCP has outage, you're down
# If Google changes pricing, no alternatives
# If service discontinued, must migrate

Cost Structure

TrustGraph: Predictable Costs

Infrastructure-based pricing:

Scenario: 1M documents, 10M queries/month

TrustGraph on GCP (self-managed):
- GKE cluster (3 nodes): $250/month
- Graph DB (Neo4j on VMs): $500/month
- Vector store (Qdrant): $200/month
- Storage (Persistent Disk + GCS): $130/month
- Networking: $70/month
- LLM APIs (external): $2,000/month

Total: ~$3,150/month

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

TrustGraph becomes cheaper at:
~100K documents, 1M queries/month

GCP: Usage-Based Pricing

Pay-per-use model:

Scenario: 1M documents, 10M queries/month

GCP AI Services:
- Vertex AI (LLM - Gemini Pro): $7,000/month
  - Input: 500M tokens × $0.000125/1K = $62.50
  - Output: 100M tokens × $0.000375/1K = $37.50
  - But: Per-request overhead adds up
  - Actual cost higher with API calls
- Vertex AI Embeddings: $800/month
  - 1B tokens × $0.0001/1K = $100
  - Processing overhead: $700
- Vertex AI Vector Search: $2,500/month
  - Index hosting: $1,500/month
  - Query costs: $1,000/month
- Enterprise Search: $10,000/month
  - Advanced tier required
  - Per-query charges
- Graph Database (Spanner): $3,000/month
  - Or Neo4j Marketplace: $2,000/month
- Cloud Storage: $200/month
- Cloud Functions: $500/month
- Networking: $400/month

Total: ~$17,400/month

Challenges:
⚠️ Costs scale with usage
⚠️ Hard to predict
⚠️ Enterprise Search expensive
⚠️ Vertex AI has request overhead
⚠️ Multiple service charges
⚠️ Network egress fees

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

At scale (more than 100K docs, more than 1M queries):
TrustGraph is 5-6x 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: ["gs://bucket/"] });

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

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

// Done - ~10 lines

GCP: Complex Orchestration

Multiple services to coordinate:

# Install multiple libraries
# pip install google-cloud-aiplatform google-cloud-discoveryengine
# pip install google-cloud-storage google-cloud-bigquery

from google.cloud import aiplatform
from google.cloud import discoveryengine
from google.cloud import storage
from google.cloud.graph import GraphDatabase
from google.cloud import functions_v2
import vertexai
from vertexai.language_models import TextEmbeddingModel

# 1. Initialize multiple clients
aiplatform.init(project="project", location="us-central1")
storage_client = storage.Client()
search_client = discoveryengine.SearchServiceClient()
graph_db = GraphDatabase(...)

# 2. Upload to Cloud Storage
bucket = storage_client.bucket('documents')
blob = bucket.blob('doc.pdf')
blob.upload_from_filename('doc.pdf')

# 3. Create Cloud Function for processing
@functions_framework.cloud_event
def process_document(cloud_event):
    # Extract text
    text = extract_text(cloud_event.data)

    # Extract entities (custom NLP)
    entities = extract_entities_custom(text)

    # Store in Graph Database
    for entity in entities:
        graph_db.create_node(entity)

    # Generate embeddings
    model = TextEmbeddingModel.from_pretrained("textembedding-gecko")
    embedding = model.get_embeddings([text])[0]

    # Index in Vector Search
    index.upsert_datapoints([embedding])

    # Index in Enterprise Search
    search_client.import_documents(...)

# 4. Create hybrid query function
def hybrid_query(query_text):
    # Vector search
    embedding_model = TextEmbeddingModel.from_pretrained(...)
    query_embedding = embedding_model.get_embeddings([query_text])[0]
    vector_results = vector_index.find_neighbors(query_embedding)

    # Enterprise Search
    search_results = search_client.search(query=query_text)

    # Graph query
    graph_results = graph_db.query(...)

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

    # Generate response with Vertex AI
    model = GenerativeModel("gemini-pro")
    response = model.generate_content(
        f"Context: {combined}\n\nQuery: {query_text}"
    )

    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 agents ✅ Graph analytics: PageRank, communities, centrality ✅ Hybrid retrieval: Graph + vector unified ✅ Provenance: Complete audit trails ✅ Relationship types: Typed, weighted edges ✅ Graph algorithms: Built-in graph algorithms

GCP: Manual Implementation

You build:

⚠️ Multi-hop reasoning: Write custom graph queries ⚠️ Knowledge organization: Design your system ⚠️ Temporal queries: Implement manually ⚠️ Visualization: Use separate tools ⚠️ Schema management: Manual database schema ⚠️ Integrations: Custom Cloud Functions ⚠️ Agent orchestration: Build with Workflows ⚠️ Graph analytics: Limited or separate service ⚠️ Hybrid retrieval: Coordinate services manually ⚠️ Audit trails: Configure Cloud Logging ⚠️ Relationship handling: Database-specific ⚠️ Algorithms: Implement yourself

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",
});

GCP: GCP Regions

GCP-defined compliance:

# Compliance depends on GCP certifications
# - HIPAA: GCP is HIPAA-compliant (sign BAA)
# - PCI-DSS: GCP is PCI-DSS certified
# - SOC 2: GCP has SOC 2 attestation
# - FedRAMP: Limited availability

# Limitations:
# - Must use GCP
# - Cannot deploy on-premise (except Anthos)
# - Cannot do air-gapped
# - Region availability varies
# - Some AI services limited regions
# - Vertex AI not in all regions

# Example: HIPAA deployment
aiplatform.init(
    project="your-project",
    location="us-central1",  # HIPAA-compliant region
)

# Must sign BAA with Google
# Must use HIPAA-eligible services
# Still in GCP cloud
# Data residency controlled by Google

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 GCP 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. Open Source Preference

   • Need code transparency
   • Want to contribute
   • Avoid vendor lock-in
   • Community-driven development

Choose GCP Services For:

1. GCP-Native Applications

   • Heavily invested in GCP
   • GCP expertise in team
   • BigQuery integration
   • Other GCP services used

2. Low Volume Projects

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

3. Google Ecosystem

   • Google Workspace integration
   • Google services dependency
   • Vertex AI specific features
   • Google enterprise support

4. Simple RAG Use Cases

   • Don't need Knowledge Graph
   • Enterprise Search sufficient
   • Basic vector search
   • Limited integration needed

Migration Paths

From GCP to TrustGraph

Migration process:

# Export from GCP services
# 1. Export Graph Database
if using_neo4j:
    graph_data = export_neo4j()
elif using_spanner_graph:
    graph_data = export_spanner_graph()

# 2. Export Enterprise Search documents
documents = export_enterprise_search()

# 3. Export Cloud Storage
storage_data = export_gcs()

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

# Benefits:
# - Simplify from 7+ services to 1 platform
# - Reduce costs (5-6x reduction typical)
# - Gain portability
# - More advanced graph features
# - Unified API
# - Multi-LLM support

TrustGraph on GCP

Use GCP infrastructure, not GCP AI services:

// Deploy TrustGraph on GCP infrastructure
const trustgraph = new TrustGraph({
  deployment: {
    cloud: "gcp",
    region: "us-central1",
    compute: "gke",
    storage: "persistent-disk",
  },
  llm: {
    provider: "google",  // Or any provider
    model: "gemini-pro",
    fallback: "gpt-4-turbo",
  },
});

// Benefits:
// ✅ Use GCP infrastructure
// ✅ Not locked to Vertex AI services
// ✅ 5-6x lower costs
// ✅ Can migrate to other clouds
// ✅ Unified platform
// ✅ Choose any LLM provider
// ✅ Advanced graph features

Conclusion

TrustGraph vs Google Cloud AI:

Choose TrustGraph when you need:

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

Choose GCP Services when you need:

• GCP-native integration
• Low volume pay-as-you-go
• Google Workspace integration
• Vertex AI specific features
• Corporate GCP mandate
• BigQuery integration
• Simple RAG without graphs

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

Additional Resources

Google Cloud AI:

• Official Website
• Documentation

TrustGraph:

• TrustGraph Website
• TrustGraph Documentation
• TrustGraph GitHub

Next Steps

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