Choosing Components for Your AI Stack

Start with Your Use Case

The single most important factor in component selection is what you are building. A personal chatbot for one user has fundamentally different requirements than a document processing pipeline serving a team of fifty. A coding assistant that generates code has different model needs than a customer support agent that answers questions from a knowledge base. Before comparing tools, write down your answers to three questions: how many concurrent users do you expect, what type of tasks will the AI perform, and how much hardware budget do you have.

Personal and development use cases (one to three users, experimentation, learning) favor simplicity above all else. Ollama for inference, Open WebUI for the interface, and SQLite or built-in storage for memory. You can have this running in twenty minutes and upgrade components individually as your needs grow. The goal at this stage is to get something working, not to build the optimal architecture.

Team and production use cases (ten or more users, reliability requirements, specific performance targets) require more careful selection. Concurrency, persistence, monitoring, and error handling become important. You might need vLLM instead of Ollama for inference, PostgreSQL instead of SQLite for storage, and a proper orchestration framework instead of ad-hoc scripts.

Choosing an Inference Engine

The inference engine decision usually comes down to Ollama versus vLLM. Ollama wins on simplicity: single binary installation, one-command model downloads, automatic GPU detection, and a model library that makes trying new models trivial. If you are running a single-user setup or a development environment, Ollama is the right choice. It handles model swapping, quantization, and GPU memory management automatically.

vLLM wins on throughput and concurrency. Its PagedAttention mechanism and continuous batching let it serve many simultaneous requests efficiently, sharing GPU memory between active requests instead of allocating fixed blocks per session. If you expect more than five concurrent users or need to maximize requests per second on your hardware, vLLM is the better foundation. The tradeoff is more complex setup and configuration.

llama.cpp deserves consideration if you are running on CPU-only hardware or very constrained GPU memory. It achieves the highest tokens-per-second on limited hardware through aggressive optimization, but lacks the convenience features of Ollama and the concurrency handling of vLLM. For embedded systems, edge devices, or machines without GPUs, llama.cpp may be your only practical option.

Choosing a Vector Database

If you already run PostgreSQL, start with pgvector. Version 0.8 introduced HNSW indexing that performs competitively with dedicated vector databases, and using your existing database eliminates an entire component from your stack. You get vector search alongside relational queries, transactions, and backups with a single database to manage.

If you want a dedicated vector database, Qdrant is the strongest choice for self-hosted deployments. Written in Rust for performance, it supports filtering alongside vector search, handles millions of vectors without degradation, and offers excellent Docker support. ChromaDB is simpler for prototyping but lacks production features like clustering, replication, and backup automation.

For small projects with fewer than 100,000 vectors, the choice barely matters. Any vector database handles this scale easily. The decision becomes important when you have millions of documents, need filtered search (for example, searching only documents from a specific user or date range), or require persistence guarantees that survive container restarts.

Choosing an Orchestration Framework

n8n is the right choice if your team includes non-developers who need to build or modify AI workflows. Its visual canvas lets you drag, connect, and configure nodes without writing code. It integrates with over 400 external services and supports complex branching, error handling, and scheduling. The learning curve is gentle and results are immediately visible.

LangGraph fits teams with strong Python skills who need fine-grained control over agent behavior. It models workflows as state machines with explicit transitions, which makes complex agent loops debuggable and testable. If your agents need to make iterative decisions, backtrack on errors, or maintain sophisticated state across many steps, LangGraph provides the control that visual builders cannot.

Dify occupies the middle ground: more structured than LangGraph, more capable than basic n8n workflows. It includes built-in RAG pipeline management, model provider configuration, and a visual workflow builder specifically designed for AI applications. If you want an all-in-one platform rather than assembling individual components, Dify reduces the number of moving parts.

Choosing a Memory Strategy

Start with the simplest memory that solves your problem. For a chatbot, conversation history stored in PostgreSQL or SQLite is sufficient. For a document question-answering system, the vector database already provides the knowledge memory through RAG. Only add complex memory (summarization, knowledge graphs, hierarchical memory management) when you have a concrete problem that simpler approaches cannot solve.

If your agents need to remember information across sessions (user preferences, project context, past decisions), implement embedding-based memory retrieval. Store important facts as embedded vectors and retrieve them alongside document context during each interaction. This approach scales well and integrates naturally with your existing vector database.

Choosing an Interface

The interface layer determines how users interact with your AI stack. Open WebUI is the default recommendation for most deployments because it provides a familiar chat-style interface, stores conversation history, supports multiple models, handles document uploads for RAG, and includes user authentication for team access. It runs as a lightweight Docker container using approximately 200 MB of RAM and connects to any OpenAI-compatible API endpoint, meaning it works with Ollama, vLLM, and cloud APIs without modification.

API-only deployments skip the web interface entirely. If your AI stack serves automated workflows, integrates with existing applications, or powers features embedded in other products, you may not need a standalone chat interface. In this case, your orchestration layer receives requests directly from your applications and returns results through API responses. This approach reduces the number of components and avoids the overhead of maintaining a user-facing web application.

Custom frontends make sense when you need a user experience that Open WebUI cannot provide. If your AI application requires a specific layout, custom data visualization, integration with your company's design system, or specialized input methods like voice input, structured forms, or collaborative editing, building a custom frontend that communicates with your stack's API layer gives you full control. React, Vue, and similar frameworks can call your orchestration API directly, treating the AI stack as a backend service.

Testing and Validating Your Choices

Before committing to a component combination, build a minimal prototype with your actual data and realistic queries. Run your real documents through the RAG pipeline and evaluate whether the retrieved context is relevant. Send your actual use case prompts to the model and assess whether the responses meet your quality bar. Test with your expected concurrent user count to verify that the inference engine handles the load without unacceptable latency. Component selection based on benchmarks and documentation will always be less reliable than testing with your own workload.

Allocate time for a structured evaluation period where you run two or three candidate configurations side by side. Use the same set of test queries against each configuration and compare results on dimensions that matter for your use case: response accuracy, latency under load, resource consumption, and operational complexity. The best configuration for your deployment may differ from community recommendations because your documents, queries, and usage patterns are unique. A week of parallel testing prevents months of frustration with a poor component choice.

Pay particular attention to failure modes during testing. What happens when the GPU runs out of memory? How does the system behave when the vector database is temporarily unavailable? Does the orchestration layer recover gracefully from LLM timeouts? Production systems encounter these situations regularly, and discovering that your stack handles them poorly is much better during evaluation than after deployment. Document the failure behaviors you observe and factor them into your component decisions.