What does Ai have to do with event streaming?

Introduction

In October 2024, at the Confluent Current Conference, an architect made an obvious comment at the InfinyOn Booth. For me it was a badge of honor! But for some of us at InfinyOn, it was a cause for concern.

“You folks are the first group of people who are not talking about Ai!”

For all of 2024, questions of Ai is ever present in exec and stakeholders meetings. With significant invstment outlook and market being shaped by Ai it is important to have a clear direction that we pursue as a data platform company.

What is our Ai play at InfinyOn? Is there one? Do we need to have one?

This is a starting point of my response to these questions.

ML. Ai. LLMs. Agents.

My first professional hype cycle in data was the Big Data hype in the mid to late 2000s. Right at the back of a disastrous Y2K and the implosion of the dot com bubble when the value of software was questionable, people were looking for hope. And they got some hype as a hope or HaaH in the form of Cloud Computing, SaaS, Big Data, and Agile. I wish I could say that the world is a better place 20 years later, but I am a realist. And I don’t like the smell of fish, which has never left all my career.

In the rapidly evolving landscape of artificial intelligence, the infrastructure supporting AI systems is becoming as crucial as the models themselves. As organizations implement increasingly sophisticated AI systems—from large language models to agentic applications and RAG pipelines—they face critical challenges in scaling, efficiency, and manageability. InfinyOn, with its core technologies Fluvio and Stateful DataFlow, offers a transformative approach to building next-generation AI pipelines. This article explores how InfinyOn’s technologies specifically enhance the AI/ML ecosystem, addressing key pain points and providing practical insights for developers and architects building modern AI applications.

The AI Infrastructure Challenge

Before diving into solutions, it’s crucial to understand the core challenges facing modern AI systems:

1. Asynchronous Complexity: Agentic AI systems rely on parallel task execution across distributed models, creating significant coordination challenges.
2. Latency Bottlenecks: Real-time AI decision-making is hampered by cloud roundtrips and data movement delays.
3. Exponential Infrastructure Demands: As AI systems grow more complex, resource requirements—and costs—grow exponentially.
4. Inadequate Telemetry: Traditional monitoring tools fail to capture the unique needs of autonomous AI systems.
5. Unsustainable Economics: The current cost structure of running advanced AI workflows is prohibitive for many organizations.

Let’s explore how InfinyOn addresses each of these challenges, providing a foundation for more efficient, scalable, and economical AI systems.

The Foundation: InfinyOn and Fluvio

At the core of InfinyOn’s offering is Fluvio, a distributed streaming engine built from the ground up to be lean, secure, and efficient. Unlike traditional streaming platforms that often struggle with complexity and resource usage, Fluvio takes a radically different approach.

Key Features of Fluvio:

• Rust-Based Performance: Built entirely in Rust, delivering exceptional performance and security.
• Lightweight Footprint: A mere 37MB binary that can run on ARM64 IoT devices.
• Edge-Native Design: Ideal for AI applications that need to process data close to the source.
• Cloud-Native Architecture: Loosely coupled components that scale on demand.
• Declarative and Self-Healing: Reduces management overhead and recovers from failures autonomously.

This foundation provides AI engineers with a reliable, efficient infrastructure for building sophisticated data pipelines, particularly suited to the demands of modern AI applications.

Stateful DataFlow: The Missing Piece for AI Applications

One of the most significant challenges in building modern AI applications is maintaining state across interactions. Traditional stateless architectures struggle with this requirement, leading to compromised user experiences or complex workarounds. This is particularly problematic for applications using large language models, which benefit significantly from context retention across interactions.

InfinyOn’s Stateful DataFlow framework addresses this challenge by providing a unified, composable distributed streaming and stream processing paradigm. This allows developers to maintain application state across distributed processing nodes—a critical capability for modern AI systems.

Benefits of Stateful DataFlow for AI:

• Enhanced Context Retention: Preserves context across interactions, enabling more coherent AI outputs.
• Personalization: Maintains user preferences and interaction history for tailored responses.
• Multi-Turn Dialogue Support: Crucial for conversational AI applications.
• Declarative Pipeline Definition: Improves collaboration between data scientists and engineers.

Addressing Core AI Challenges with InfinyOn

Now, let’s dive deep into how InfinyOn’s technologies specifically address the critical challenges facing modern AI systems.

1. Conquering Asynchronous Complexity in Agentic Flows

Agentic AI’s power lies in parallel task execution across distributed models, but this creates coordination chaos. Traditional cloud tools struggle with state synchronization, non-deterministic workflows, and error recovery in multi-step AI chains.

InfinyOn’s Solution:

Stateful DataFlow enables atomic execution groups with built-in retry logic and exactly-once processing. Here’s an example of how this might look in practice:

services:
  ai_orchestrator:
    sources: [input_stream]
    transforms:
      - operator: branch  
        conditions:
          - predicate: "requires_llm_analysis"  
            sink: gpt4_processing
          - predicate: "needs_vector_search"  
            sink: qdrant_lookup
    sinks: [output_stream]

This declarative approach allows for complex, asynchronous workflows while maintaining consistency and recoverability.

2. Eliminating Latency Bottlenecks

Real-time AI decision-making crumbles under cloud roundtrips. LLM API delays, edge-to-cloud data movement, and in-memory state management all contribute to unacceptable latency for many AI applications.

InfinyOn’s Edge-Native Approach:

The Santa Clara Traffic Cameras example demonstrates this efficiency, processing 4K video streams locally and sending only license plate metadata to cloud LLMs. This approach reduces bandwidth costs by 92% while enabling sub-50ms response times.

3. Taming Infrastructure Demands

Agentic AI’s resource appetite grows exponentially, with projected costs reaching $84M+ for enterprise deployments. Multi-cloud agent workflows can suffer from significant performance losses, and GPU sprawl from unoptimized async pipelines further compounds the issue.

InfinyOn’s Efficiency Levers:

1. Unified State Layer: The 37MB runtime replaces separate DB/Cache/Stream processors.
2. Declarative Scaling:

// Auto-scale based on LLM token throughput 
fluvio::smartmodule::set_scale(
    ScaleConfig::throughput(10_000) // Tokens/sec
        .with_max(12)
        .with_cooldown(30)
);

3. Cold Start Mitigation: Pre-warmed execution contexts cut provisioning delays by 73%.

4. Telemetry for Autonomous Systems

Traditional monitoring fails to meet agentic AI’s unique needs, with 62% of AI incidents originating from undetected state drift and an average cost of $8.2M for unmonitored AI errors.

InfinyOn’s Observability Stack:

InfinyOn provides real-time tracing of async AI workflows with OpenTelemetry integration. Key metrics tracked per AI agent include:

• Context Window Saturation (prevent LLM amnesia)
• Vector Cache Hit Ratio (optimize RAG costs)
• Action Success Watermark (detect workflow drift)

5. Cost-Efficient AI Economics

Agentic workflows demand new financial models. InfinyOn’s approach significantly reduces costs across multiple dimensions:

Source: Santa Clara Traffic implementation metrics

Optimization Strategies:

• State-Aware Batching: Group LLM context windows into optimal token counts
• Edge Preprocessing: Filter 78% of raw data before cloud ingestion
• Pipeline Parallelism: Concurrently process multiple AI agent branches

Building Agentic RAG Systems with InfinyOn

Retrieval Augmented Generation (RAG) has emerged as a powerful technique for enhancing the capabilities of large language models. Agentic RAG takes this approach further by allowing the system to make intelligent decisions about data retrieval and response generation.

Fluvio and Stateful DataFlow provide an ideal foundation for building such agentic systems. The stateful nature of the platform enables sophisticated decision-making based on both current and historical context. This is crucial for agentic applications, which need to make intelligent decisions based on a comprehensive understanding of the user’s needs and the available information.

Practical Implementation: Real-World Examples

Let’s explore several practical examples of how InfinyOn’s technologies can be applied to AI/ML use cases.

Example 1: OpenAI Integration for Real-time Text Generation

apiVersion: 0.5.0
meta:
  name: openai-processor
  version: 0.1.0
config:
  converter: raw
topics:
  input-prompts:
    schema:
      value:
        type: string
  generated-text:
    schema:
      value:
        type: string
services:
  generate-text:
    sources:
      - type: topic
        id: input-prompts
    transforms:
      - operator: map
        run: |
          use serde_json::{json, Value};
          use reqwest::Client;
          use std::env;
          
          pub async fn process_with_openai(input: String) -> Result {
              let client = Client::new();
              let api_key = env::var("OPENAI_API_KEY")?;
              
              let response = client.post("https://api.openai.com/v1/chat/completions")
                  .header("Authorization", format!("Bearer {}", api_key))
                  .json(&json!({
                      "model": "gpt-4",
                      "messages": [{"role": "user", "content": input}],
                      "max_tokens": 500
                  }))
                  .send()
                  .await?;
              
              let result = response.json::().await?;
              Ok(result["choices"][0]["message"]["content"].as_str().unwrap_or("").to_string())
          }          
    sinks:
      - type: topic
        id: generated-text

This example illustrates how developers can create a pipeline that processes text inputs with OpenAI’s large language models, making it valuable for chatbots, content generation, and other GenAI applications.

Example 2: Vector Database Integration for RAG

apiVersion: 0.1.0
meta:
  version: 0.1.1
  name: embedding-pipeline
  type: qdrant-sink
topic: document-embeddings
secrets:
  - name: QDRANT_API_KEY
qdrant:
  url: https://your-instance.qdrant.io:6334
  api_key: "${{ secrets.QDRANT_API_KEY }}"

This configuration shows how Fluvio can be integrated with Qdrant to stream vector embeddings, enabling efficient vector search for RAG applications.

Example 3: Real-time Data Processing for AI Applications

apiVersion: 0.5.0
meta:
  name: split-sentence-inline
  version: 0.1.0
  namespace: example
config:
  converter: raw
topics:
  sentence:
    schema:
      value:
        type: string
  words:
    schema:
      value:
        type: string
services:
  sentence-words:
    sources:
      - type: topic
        id: sentence
    transforms:
      - operator: flat-map
        run: |
          fn sentence_to_words(sentence: String) -> Result> {
            Ok(sentence.split_whitespace().map(String::from).collect())
          }          
      - operator: map
        run: |
          pub fn augment_count(word: String) -> Result {
            Ok(format!("{}({})", word, word.chars().count()))
          }          
    sinks:
      - type: topic
        id: words

This example showcases Fluvio’s ability to perform real-time transformations on streaming data, which is essential for preprocessing data before it reaches AI models.

Strategic Advantage for AI Builders

InfinyOn transforms agentic AI economics and capabilities through:

1. Predictable Scaling

autoscale:
  rules:
    - metric: llm_tokens_sec
      threshold: 10k
      action: +2 nodes
    - metric: error_rate
      threshold: 1.2%
      action: rollback

2. Architectural Consistency

InfinyOn provides a unified layer for AI state management across edge and cloud environments, ensuring consistency in your AI pipelines regardless of where they run.

3. Future-Proof Foundation

• AI-Native Protocols: Built-in support for JSON Schema, Protobuf, Avro
• Multi-LLM Routing: Optimize costs by directing queries to GPT-4, Claude, or local models
• Compliance Guardrails: Automated PII masking and audit trails

Conclusion: Empowering the Next Generation of AI Builders

As we move into an era where AI is increasingly embedded in every application and service, the ability to efficiently move, process, and analyze data in real-time becomes not just an advantage but a necessity. InfinyOn’s technologies offer not just a platform but a new paradigm—one that aligns perfectly with the demands of modern AI systems.

For teams building the next generation of AI agents, InfinyOn provides the missing infrastructure layer—turning streaming data into real-time intelligence while containing costs and complexity. The async-first architecture, stateful execution guarantees, and edge-optimized processing create a 10x improvement in agentic workflow ROI.

Final Metrics from Production Deployments:

• 63% reduction in AI pipeline latency
• $280k annual savings per 100 agents
• 89% faster incident detection in async workflows

As AI systems grow more autonomous, InfinyOn ensures the infrastructure scales smarter—not just bigger. By addressing the core challenges of asynchronous complexity, latency, infrastructure demands, telemetry, and cost efficiency, InfinyOn empowers AI builders to create more intelligent, more responsive, and more valuable applications.

The future of AI is not just about smarter models, but smarter infrastructure. With InfinyOn, that future is within reach today.

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