TrustGraph vs. Redpanda’s Agentic Data Plane
Overview
Redpanda’s Agentic Data Plane (ADP) introduces a powerful platform for governed, observable, and auditable real-time data access tailored for agents. Leveraging Redpanda’s high-performance streaming engine and Oxla’s distributed SQL query capabilities, ADP emphasizes secure, policy-driven data connectivity for AI systems.
TrustGraph takes a broader approach, providing not only connectivity and governance but the entire data and agent context stack. TrustGraph manages data ingestion, knowledge graph construction, vector search, and LLM orchestration in a single containerized platform designed for production-grade agentic AI and post-training workflows.
Feature Comparison
Feature | Redpanda Agentic Data Plane | TrustGraph |
|---|---|---|
Core Focus | Streaming data connectivity & governance | Unified agentic AI context stack |
Data Connectivity | 300+ connectors, event streaming, SQL query | Pulsar streaming, APIs. natural language GraphQL query |
Governance & Access Control | Identity-based, per-agent policies, auditing | Supports governance frameworks, full observability |
Querying | Powerful SQL queries on streaming data | Knowledge graph + vector search + GraphQL + retrieval |
Knowledge Graph & Semantic Layer | Not native | Automated knowledge graph construction |
Data Streaming Infrastructure | Built on Redpanda streaming engine | Flexible streaming engines (Kafka, Pulsar, Redpanda) |
Post-Training AI Support | Limited — focuses on data access and queries | Full post-training AI pipeline |
Deployment Model | SaaS/Managed / Redpanda-hosted | Self-hosted, BYOC, or any public cloud |
Multi-LLM Integration | Limited | Any LLM integration supported |
Observability and Debugging | Strong telemetry and audit logs | Full Prometheus and Grafana dashboards |
How They Complement and Differ
Redpanda ADP is well-suited when your primary concern is secure, auditable access to event streams and enterprise data for AI agents. It excels in data governance, compliance, and runtime data connectivity.
TrustGraph provides the full data-to-agent context stack, making it easy to construct knowledge graphs and run retrieval-augmented agents out of the box. It's ideal for enterprises who want complete control of their post-training AI infrastructure, including LLM orchestration and agent pipelines.
Why Choose TrustGraph?
Full-stack Production Readiness: Not just data access, but integrated infrastructure for streaming, knowledge graphing, vector search, and agent orchestration.
Post-training Agent Support: Built explicitly to power agents that continuously learn from enterprise data.
Self-hosted & Open-Standard: Avoid vendor lock-in; run where you want.
Flexible Architecture: Swap streaming engines, vector stores, and LLM providers as needed.
Summary
Aspect | Redpanda ADP | TrustGraph |
|---|---|---|
Primary Focus | Data connectivity & governance | End-to-end agentic AI platform |
Deployment | Managed / SaaS | Self-hosted, containerized |
Knowledge Graph Support | No | Yes |
Post-training Workflows | No | Yes |
Streaming Engines | Redpanda only | Kafka, Pulsar, Redpanda |
LLM Integration | Limited | Multi-provider |
Observability & Auditing | Detailed | Full stack metrics & dashboards |
This refined comparative messaging clearly differentiates TrustGraph’s holistic, flexible agentic AI platform approach from managed cloud AI platforms and from Redpanda’s focused data governance and streaming platform, highlighting strengths for enterprise users who demand open, complete, and vendor-agnostic AI production infrastructure.
TrustGraph on GitHub: https://github.com/trustgraph-ai/trustgraph