Introducing Sovereign AI: private, traceable, and secure
State-of-the-art AI, where the data never leaves your network. Instead of sending your data to the AI model, Redpanda sends the model to your data. This lets you leverage the latest AI models in your own environment without sharing your information. Sensitive workloads demand end-to-end transparency of how data is managed, used, and protected. Beyond the cost/performance advantage of purpose-specific, fine-tuned models, there's no better custodian for your data than you.
Before Sovereign AI
Data leaves your environment and goes over the public internet to your AI model.
With Sovereign AI
Run secure, performant models locally with Redpanda while reducing costs.
Ship the model to data
Get end to end transparency and ownership of your data while delivering low latency, real-time inferencing with LLM models on your own hardware. Dynamically scale without sacrificing data privacy with cluster-level elasticity through Redpanda Connect. Additionally, deploy LLMs inside the C++ Redpanda binary for ultra low latency inferencing, high availability, atomicity of model swaps, and versioning.
Trace AI lineage
Track the data lineage of every LLM execution to its source via lightweight headers. Opt-in to tracing record-metadata like topics, offsets & timestamps, detailing what was generated, when, by which subsystem, and under which principal. This creates a comprehensive audit trail for state-of-the-art LLM all the way to the source data. Deploy LLM models without fear for your mission critical workloads.
Secure by design
Confidently launch and govern these large scale systems knowing that the highest level of integrity, authentication, authorization, access controls, and audit logs are at your fingertips. Integrate the deployment of all of your AI models with your standard OCSF audit logs tooling like Splunk and monitor the deployments with your standard open telemetry stack.
Best-in-class AI platform for developers
All the power of Redpanda with just a few lines of configuration.
input:
kafka:
addresses: ["${REDPANDA_BROKER}"]
topics: ["articles"]
consumer_group: "redpanda-connect-ai-consumer-group"
pipeline:
processors:
- ollama_chat:
model: llama3.1
system_prompt: "You are to summarize user input in a concise sentence"
output:
kafka:
addresses: ["${REDPANDA_BROKER}"]
topic: "summarized-articles"