TrustGraph vs. Cloud AI Platforms
Google Vertex AI, AWS Bedrock, and Azure AI Studio provide powerful managed AI services focused on inference, model fine-tuning, and simplified integration within their cloud ecosystems. These platforms excel at rapid AI deployment with minimal operational overhead but impose constraints around data sovereignty, vendor lock-in, and infrastructure flexibility.
TrustGraph offers a fundamentally different approach:
A self-hosted, containerized agentic context stack that integrates streaming data ingestion, knowledge graph construction, vector database management, LLM orchestration, and post-training pipelines—all under your control, on any infrastructure.
Feature Comparison
Feature | Google Vertex AI | AWS Bedrock | Azure AI Studio | TrustGraph |
|---|---|---|---|---|
Deployment Model | Fully managed (GCP) | Fully managed (AWS) | Fully managed (Azure) | Self-hosted (any cloud, on-prem) |
Data Sovereignty | Limited to cloud regions | Limited to cloud regions | Limited to cloud regions | Full control, run anywhere |
Foundation Model Access | Google models, Anthropic, and some open models | Anthropic, Cohere, etc. | OpenAI first-party access | Any model - OpenAI, Anthropic, local Ollama, others |
Data Ingestion & Streaming | Separate GCP services | Separate AWS services | Separate Azure services | Built-in streaming data control plane |
Knowledge Graph Construction | Not built-in | Not built-in | Not built-in | Automated, integrated |
Vector Database Integration | Third-party or built-in | Third-party or built-in | Third-party or built-in | Built-in support |
Post-Training Infrastructure | Limited to fine-tuning | Limited to fine-tuning | Advanced fine-tuning | Full stack for continuous post-training workflow |
Pricing Model | Pay-per-tokens and provisioned throughput | Pay-per-tokens and provisioned throughput | Pay-per-tokens and provisioned throughput | Infrastructure cost only, no token pricing |
Vendor Lock-in | High | High | High | None |
Observability & Governance | Cloud tools | Cloud tools | Cloud tools | Full control with Prometheus & Grafana |
Customizability | Calibrated to platform | Calibrated to platform | Calibrated to platform | Fully open, modular, extensible |
Why Choose TrustGraph?
True Data Sovereignty: Keep your AI and data fully on premises, private clouds, or hybrid environments without constraints of cloud vendor lock-in.
Complete Agentic AI Stack: From data ingestion through to agent orchestration, no more stitching together multiple systems.
Cost Predictability: Avoid escalating per-token fees—pay only for infrastructure.
Open & Modular: Bring your own LLMs, vector stores, and knowledge graphs; swap components without vendor restrictions.
Built for Post-Training: Design AI agents that continuously learn from live data, not just rely on static pre-trained models.
TrustGraph on GitHub: https://github.com/trustgraph-ai/trustgraph