The Modern AI Stack: What Infrastructure Do You Need to Orchestrate LangChain and LlamaIndex at Scale?

LangChain and LlamaIndex make it easier to build LLM powered applications, but once you move beyond demos, you need a real modern AI stack infrastructure behind them. That means reliable data pipelines, vector stores, model serving, orchestration, observability, and governance that can support many apps and teams, not just a single prototype.

• A robust modern AI stack infrastructure separates concerns: data, retrieval, orchestration, models, and governance.
• LangChain and LlamaIndex sit in the middle, orchestrating tools and retrieval, not replacing your infra.
• You need production grade observability, CI/CD, and security for chains and indexes just like for microservices.
• Multi model, multi provider support and cost controls matter as usage grows.
• Codieshub helps teams design modern AI stack infrastructure that runs LangChain and LlamaIndex safely at scale.

• Data and storage layer – sources of truth, warehouses, lakes, document stores.
• Embedding and vector layer – embeddings, vector DBs, and retrieval indexes.
• Model serving layer – LLMs, smaller models, safety and routing.
• Orchestration layer – LangChain, LlamaIndex, tools, agents, workflows.
• Platform and governance layer – CI/CD, observability, security, and policy.

• Operational systems: CRM, ERP, ticketing, HRIS, product databases.
• Content systems: SharePoint, Confluence, Google Drive, file shares.
• Analytics platforms: data warehouses or lakehouses (Snowflake, BigQuery, Redshift, Databricks).

• Object storage (S3, GCS, Azure Blob) for PDFs, docs, and logs.
• Indexable formats (HTML, Markdown, text) for easier chunking and embedding.
• Versioning and lifecycle policies so stale content does not pollute retrieval.

• Centralized services that generate and manage embeddings for documents and queries.
• Support for multiple embedding models for different languages or domains.
• Batch and streaming pipelines to keep embeddings up to date.

• Managed or self hosted vector DBs (Pinecone, Weaviate, Qdrant, Milvus, pgvector, OpenSearch).
• Index design for different corpora with metadata and filters.
• Permission aware retrieval integrated into your modern AI stack infrastructure.

• LangChain and LlamaIndex retrievers built on vector and hybrid search.
• Reranking with cross encoders or LLM based re scoring for higher relevance.
• Configurable retrieval strategies per application and use case.

• Vendor APIs (OpenAI, Anthropic, Azure OpenAI) with enterprise controls.
• Self hosted or private LLMs (Llama, Mistral) on Kubernetes or specialized infra.
• Abstraction layer so LangChain and LlamaIndex call unified endpoints.

• Smaller models for classification, routing, safety, and reranking.
• Embedding models for the vector layer.
• Model registry for versions, metadata, and rollout control.

• Content filters for PII, toxicity, and compliance violations.
• Output validation against schemas and business rules.
• Integrated into the model gateway so all LangChain and LlamaIndex flows pass through.

• Task specific chains and agents that call tools and APIs.
• Standardized tool interfaces for internal services.
• Versioned chain configurations so changes are traceable.

• Document indexes per domain mapped to vector stores.
• Query engines defining retrieval, context assembly, and answering.
• Composable with LangChain agents or other orchestrators.

• Deploy orchestration services on Kubernetes or serverless runtimes.
• Isolate workloads by tenant, region, or application.
• Include health checks, rate limiting, and circuit breakers.

• Infrastructure as code for model gateways, vector DBs, and orchestrators.
• CI/CD for chains, prompts, and index configs.
• Rollback for prompts, retrieval settings, and model versions.

• Metrics for latency, errors, cost, tokens, and retrieval quality.
• Logs for prompts, responses, tool calls, and retrieval results.
• Evaluation pipelines for LangChain and LlamaIndex flows.

• SSO and IAM integration for all APIs.
• Row and document level permissions enforced.
• Policies for acceptable AI use and incident response.

• Central retrieval and model access services reused across apps.
• Shared LangChain and LlamaIndex components packaged as libraries.
• Keeps the modern AI stack infrastructure maintainable as teams grow.

• Centralized visibility into LLM and embedding usage.
• Quotas, budgets, and cost optimization strategies.
• Capacity planning for GPUs or API limits.

• Regional deployments to meet residency laws.
• Region specific LangChain and LlamaIndex configurations.
• Audit trails and documentation for regulators.

• Assess your current LangChain and LlamaIndex experiments and architecture gaps.
• Design a modern AI stack infrastructure that reuses what you have and fills in missing layers.
• Implement secure model gateways, retrieval services, and orchestration runtimes.

• Standardize patterns for RAG, agents, safety, and evaluation.
• Build shared services and SDKs that make LangChain and LlamaIndex easier and safer to use.
• Set up governance, monitoring, and cost controls for your AI platform.

• Inventory current LLM use cases and LangChain/LlamaIndex usage.
• Map them to modern AI stack infrastructure layers and identify gaps.
• Prioritize shared vector, model, and observability services, then refactor key apps.

1. Do we need both LangChain and LlamaIndex in our stack?
Not always, but they often complement each other: LlamaIndex focuses on indexing and retrieval, while LangChain focuses on orchestration and tools. Your modern AI stack infrastructure can support either or both, depending on patterns you adopt.

2. Can we run everything on a single cloud service?
You can centralize much of the stack on one cloud, but you still need to design clear layers, IAM, and observability. A monolithic approach without structure will not scale, even on a single provider.

3. How important is a vector database versus using our existing search?
For RAG and semantic search, vectors are essential. Sometimes you can extend existing search platforms with vector capabilities. The key is integrating vectors, metadata, and permissions properly in your modern AI stack infrastructure.

4. When should we consider self hosting LLMs instead of vendor APIs?
When data residency, cost at scale, or deep customization needs outweigh the simplicity of APIs. Your architecture should abstract model access so you can switch between APIs and self hosted models as needs evolve.

5. How does Codieshub help build a modern AI stack infrastructure?
Codieshub designs your modern AI stack infrastructure end to end, selects and integrates vector DBs, model gateways, LangChain and LlamaIndex orchestration, observability, and governance, then helps you migrate and scale real applications on top of that platform.