Orchestrating AI at Scale: Building Intelligent Applications with Temporal, AWS, and Kubernetes

# Orchestrating AI at Scale: Building Intelligent Applications with Temporal, AWS, and Kubernetes

Modern AI applications are not just model calls — they're complex, multi-step workflows that touch vector databases, foundation models, retrieval pipelines, and external APIs. When one step fails, the entire chain breaks. Temporal brings durable execution to this chaos, and AWS + Kubernetes provide the infrastructure muscle. Here's how we combine them.

The Problem: AI Workflows Are Fragile

A typical RAG (Retrieval-Augmented Generation) pipeline involves:

1. Chunk a document
2. Generate embeddings for each chunk
3. Store embeddings in a vector database
4. Accept a user query
5. Generate query embeddings
6. Perform vector similarity search
7. Retrieve top-k chunks
8. Re-rank results with a cross-encoder
9. Assemble context and prompt the LLM
10. Stream the response back to the user

Now imagine any of those steps timing out, returning malformed data, or hitting a rate limit. Without durable execution, you're rebuilding state by hand — debugging partial runs, replaying manual steps, and losing sleep over edge cases.

Temporal: Durable Workflows for AI

Temporal is an open-source workflow engine that guarantees exactly-once execution. Your workflow code looks synchronous but runs durably: if your service crashes mid-RAG-pipeline, Temporal replays from the last successful step.

Architecture

User Request
  → API Gateway
    → Temporal Worker (on EKS)
      → Workflow steps:
          1.  Chunk document (Lambda)
          2.  Generate embeddings (Bedrock / SageMaker)
          3.  Upsert to vector DB (OpenSearch Serverless / Pinecone)
          4.  Await user query signal
          5.  Semantic search (vector similarity)
          6.  Re-rank results (cross-encoder model)
          7.  LLM generation (Bedrock / Anthropic / OpenAI)
          8.  Stream response (SSE via API Gateway WebSocket)

Each step in the workflow is a Temporal Activity — retryable, timeout-controlled, and idempotent. If the embedding service is throttled, Temporal retries with exponential backoff. If the LLM call hangs, the activity times out and falls back to a secondary model. The workflow state is persisted in Temporal's event history, not in your application memory.

Code Sketch: RAG Workflow in TypeScript

export async function ragWorkflow(documentId: string, query: string): Promise<string> {
  // Step 1: Chunk the document
  const chunks = await chunkDocument(documentId);

  // Step 2: Generate embeddings in parallel
  const embeddings = await Promise.all(
    chunks.map(chunk => generateEmbedding(chunk, { 
      model: 'amazon.titan-embed-text-v2',
      startToCloseTimeout: '30 seconds'
    }))
  );

  // Step 3: Batch upsert to vector store
  await upsertVectors(embeddings, { 
    index: 'knowledge-base',
    retry: { maximumAttempts: 5 }
  });

  // Step 4: Query embedding + semantic search
  const queryEmbedding = await generateQueryEmbedding(query);
  const results = await vectorSearch(queryEmbedding, { topK: 10 });

  // Step 5: Re-rank
  const reranked = await crossEncoderRerank(query, results);

  // Step 6: LLM generation with fallback
  const response = await generateWithFallback(reranked, query, {
    primary: 'anthropic.claude-sonnet-4-20250514-v1:0',
    fallback: 'anthropic.claude-haiku-3-5-20241022-v1:0'
  });

  return response;
}

Notice: no error handling, no retry logic, no state management. Temporal handles all of it.

AWS AI Services: The Foundation Layer

Amazon Bedrock for Foundation Models

Bedrock gives us API access to Claude, Llama, Titan, and other foundation models without managing inference endpoints. For RAG workloads, we use:

• Claude 3.5 Sonnet / Claude 4 — Primary reasoning and generation
• Titan Embeddings V2 — Document and query embeddings (256–1024 dims)
• Claude Haiku — Fallback for cost-sensitive or low-priority queries

SageMaker for Custom Models

When off-the-shelf models aren't enough — say, a domain-specific re-ranker fine-tuned on fitness content — we deploy custom models to SageMaker real-time endpoints. Temporal activities invoke SageMaker via the AWS SDK, with auto-scaling policies that spin endpoints down to zero during quiet hours.

OpenSearch Serverless for Vector Search

We run vector search on Amazon OpenSearch Serverless with the k-NN plugin. Benefits over self-managed solutions:

• Serverless scaling — No cluster management, pay per OCU-hour
• Hybrid search — Combine BM25 text search with vector similarity in a single query
• IAM-native auth — No separate credential management

For larger deployments, Pinecone or Weaviate plug in as drop-in replacements via a vector-store abstraction layer.

Kubernetes on EKS: The Execution Layer

Temporal Workers run on Amazon EKS (Elastic Kubernetes Service) with the following topology:

KEDA for Workflow-Driven Autoscaling

Instead of scaling on CPU, we use KEDA to scale Temporal worker pods based on task queue depth. When a batch of documents arrives for indexing, KEDA spins up additional workers. When the queue drains, pods scale down. This keeps costs aligned with actual workload — crucial for bursty AI pipelines.

GPU Node Pools

For embedding generation and cross-encoder inference, we maintain a GPU node pool (g5.xlarge instances) with NVIDIA device plugins. These nodes only spin up when needed, controlled by taints, tolerations, and Karpenter for rapid provisioning.

Performance Optimization: The RAG Stack Tuned

Embedding Caching

Duplicate documents and repeated queries are expensive. We cache embeddings in ElastiCache (Redis) with document hash keys. A 70% cache hit rate cuts embedding costs proportionally and reduces latency from ~2s to ~200ms for cached documents.

Query Classification

Not every query needs the full RAG pipeline. A lightweight classifier (fastText, deployed as a Lambda) routes queries:

• Factual lookup → Direct vector search, skip LLM re-ranking
• Complex reasoning → Full RAG pipeline with cross-encoder
• Conversation → Direct LLM call with conversation history

This triage reduces average pipeline latency by 40%.

Streaming Responses

Users hate waiting. We stream LLM tokens via Server-Sent Events through an API Gateway WebSocket, so the first word appears in under 500ms. Temporal workflows handle the orchestration; the streaming gateway handles the UX.

Observability: Knowing What Your AI Is Doing

AI pipelines fail in non-obvious ways. We instrument every layer:

• Temporal UI — Workflow history, stack traces, retry counts
• CloudWatch — Embedding latency, token usage, cache hit rates
• OpenSearch Dashboards — Query patterns, result relevance trends
• Custom metrics — RAG pipeline stage latency, model fallback frequency

When a workflow fails at the re-ranking stage 50 times in an hour, an alert fires before users notice degradation.

The Bottom Line

Combining Temporal's durable execution with AWS's AI infrastructure and Kubernetes' orchestration creates a foundation for AI applications that are:

• Reliable — Every step retries, every workflow replays
• Scalable — Workers scale with actual demand, not peak provisioning
• Observable — Every decision in the pipeline has a trail

The tools exist. The hard part is stitching them together into a system that doesn't crumble under real-world edge cases. That's where we come in.

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Building AI workflows that need to survive production? Let's talk architecture.