A retail company came to Particula Tech three months into their customer support AI project. They'd picked LangChain because everyone on Reddit recommended it. When they hit month four, their system couldn't handle the request volume. We rebuilt everything from scratch. They missed their deadline and blew through budget.
You're probably facing a similar decision right now. Should you use LangChain? LlamaIndex? Or just build it yourself? Here's what actually matters: LangChain orchestrates multi-step workflows. LlamaIndex retrieves data from documents. Custom code gives you control but means writing everything. Each works in specific situations. Let me show you how to pick the right one for your project.
What These Tools Actually Do
LangChain coordinates multiple AI operations into workflows. It connects your language model to databases, APIs, and external tools. You're basically getting the plumbing that handles conversation memory, tool calling, multi-step reasoning, and agent decisions.
LlamaIndex solves one problem: getting your data into language models. It indexes your documents, builds vector stores, and implements RAG so your AI can search through your data accurately. In 2025, it's 35% better at retrieval than previous versions. If your challenge is "I need to search 10,000 documents and get accurate answers," LlamaIndex was built for exactly that.
Building from scratch means you're using language model APIs directly—OpenAI's SDK, Anthropic's Claude API, whatever you prefer. You write the code that calls the model, manages context, handles errors, and connects to your systems. No framework abstractions. You control everything but you build everything.
The LangChain Ecosystem (This Is Actually the Biggest Advantage)
LangChain isn't just a framework anymore—it's a complete ecosystem. If you're considering LangChain, you need to understand this part.
LangGraph for Stateful Workflows: LangGraph handles stateful agent workflows. When your AI needs to maintain context across multiple interactions, pause for human approval, or branch based on conditions, LangGraph gives you the architecture. Building this yourself? You're looking at months of work. We used it for a financial services client's advisor bot that maintains portfolio context across conversations and routes to different analysis tools based on what you ask. Building that architecture custom would've been painful.
LangSmith for Production Observability: LangSmith gives you production observability. You can trace every step in your workflow—which prompts were sent, how the model responded, where it failed, token usage, latency. Without this, debugging multi-step agents is nearly impossible. You're just guessing why something broke instead of seeing exactly what happened.
LangFlow for Visual Prototyping: LangFlow lets you prototype with drag-and-drop visual workflows before you write any code. I use this with clients at Particula Tech when we're designing workflows with their product teams. Your non-technical stakeholders can actually see what you're building and suggest changes before you write production code. It cuts alignment time by weeks.
When You Should Use LangChain
Use LangChain when you're connecting multiple AI capabilities into complex workflows. If your AI needs to check databases, call APIs, process transactions, and maintain conversation state—all in one interaction—LangChain handles that orchestration.
We worked with a logistics company on a shipment tracking assistant. Their AI checks shipment status across three different systems, queries weather APIs for delays, looks up customer history, calculates ETAs, and escalates to humans when needed. Each step involves different tools and data sources. LangChain orchestrated all of it. Building that coordination layer custom would've taken six months. They had a working prototype in three weeks.
Here's the tradeoff: when something breaks in a multi-step chain, you're debugging through framework abstractions. You need to understand both your code and how LangChain works internally. The framework updates frequently too. One of our mid-size clients spends about 10 hours monthly just on LangChain maintenance and updates.
LangChain makes sense when the orchestration complexity you're avoiding is greater than the framework complexity you're taking on. If your application is simple, you might be adding complexity you don't need.
When LlamaIndex Solves Your Problem
If your core challenge is "how do I let my AI search through thousands of documents accurately," start with LlamaIndex.
LlamaParse handles complex PDFs with nested tables, charts, and images. We worked with a legal tech client who implemented it to search 50,000 case files. The built-in citation tracking saved them four months of development time. They didn't have to build that from scratch. For more details on making citations work correctly in RAG systems, see our guide on how to fix RAG citations.
You'll see LlamaIndex in: SEC financial analysis bots parsing 10-K reports, knowledge bases built on company wikis, Q&A systems searching archived technical docs, construction companies analyzing RFP documents, real estate platforms simplifying condo purchase documents.
The framework handles chunking strategies, embedding models, vector database integration, hybrid search (combining semantic and keyword search), and re-ranking results. These are solved problems. You shouldn't rebuild them unless LlamaIndex can't meet your specific requirements. To understand when re-ranking becomes critical for your retrieval quality, explore our article on reranking in RAG and when you need it.
Here's the limitation: LlamaIndex focuses narrowly on retrieval. When you need complex logic beyond "search documents and return answers," you'll need something else. Many systems we've built in 2025 combine LlamaIndex for retrieval with LangChain for orchestration or custom code for business logic.
When You Should Build Custom
Custom development works in three scenarios: your requirements are simple enough that frameworks add unnecessary overhead, you need maximum performance optimization, or you're integrating with systems that frameworks don't support well.
A SaaS company came to us wanting AI summarization in their document platform. They needed it to work with their existing auth system, respect their rate limits, integrate with their logging, and follow their error handling patterns. Wrapping everything in LangChain would've created integration headaches. We built it custom with direct OpenAI API calls—200 lines of Python, production-ready in a week.
Performance matters at scale. A fintech client processes thousands of document analysis requests per minute. Framework abstractions added measurable latency and cost—$8,000 monthly. We invested $50,000 in custom development. They broke even in seven months.
When you build from scratch, you understand exactly how everything works. No black box. When something breaks, you're debugging code you wrote. You're not fighting framework abstractions or waiting for upstream fixes.
The downside: you're building everything. Conversation memory, retry logic, error handling, prompt management, tool orchestration—features that frameworks give you in 50 lines take 500 lines custom. You need strong engineering fundamentals. One startup built their entire AI platform custom, then struggled to hire engineers willing to maintain it. Framework experience is more common than willingness to dive into custom AI orchestration code.
Does Your Team Have the Skills?
LlamaIndex has a gentler learning curve. The API is straightforward, focus is narrow—just data retrieval. Developers new to AI can be productive quickly. A manufacturing client with experienced software engineers but limited AI experience chose LlamaIndex for their document search. They were building useful prototypes within days.
LangChain requires deeper understanding. You need to grasp how agents make decisions, how chains compose, how memory works, how tools integrate. Steeper learning curve. But once your team gets it, they can build sophisticated applications quickly. To avoid common pitfalls when building with LangChain, review our guide on avoiding common AI agent mistakes.
Custom development requires ML engineering maturity. You need people who understand prompt engineering, embedding models, vector databases, and language model APIs. A startup with two senior ML engineers built their document Q&A system custom in three weeks—they knew exactly what they needed and didn't want framework overhead.
Think about your debugging capabilities honestly. Frameworks mean you need to understand both your code and the framework internals when things break. One team we worked with spent three days debugging a LangChain agent only to discover the issue was in how they'd configured memory persistence—not obvious from the error messages. Custom code means you're only debugging what you wrote, but you're responsible for implementing everything correctly.
The Real Costs and Timelines
Frameworks cut initial development time by 60-70%. A prototype that takes you three weeks custom might take one week with frameworks. This advantage is real for early-stage projects.
But the gap narrows as your project matures. I've watched clients start with LangChain, spend months four through six working around framework limitations, then rebuild critical sections custom anyway. This happens when you choose based on popularity instead of technical fit.
Custom development costs more upfront. A document processing service calculated they'd need six weeks and $75,000 to build custom versus three weeks and $30,000 with frameworks. They went custom because framework overhead would cost them $8,000 monthly at scale. They'd break even in six months and save money long-term.
Budget for ongoing costs realistically. Framework projects need time for updates, dependency management, and handling breaking changes. Custom projects need continued development as AI capabilities evolve. Neither option is "set and forget." The work just comes in different forms.
The Hybrid Approach (What Most Production Systems Actually Use)
Many systems we've built in 2025 combine tools. LlamaIndex for retrieval, LangChain for orchestration, custom code for business logic. Each handles what it does best.
We built a healthcare client's clinical decision support this way: LlamaIndex indexes medical literature and patient records with optimized retrieval. LangChain orchestrates the workflow—routes questions to specialty knowledge bases, runs compliance checks, coordinates with their EMR system. Custom code handles their proprietary clinical protocols and connects to internal systems.
The hybrid approach requires clear architectural boundaries. You need to decide where retrieval ends and orchestration begins, where framework code stops and custom logic starts. But for complex applications, it delivers better results than forcing one tool to do everything.
How to Make Your Decision
Start with your core problem. Building a document search system? LlamaIndex fits. Creating complex multi-agent workflows? LangChain works. Integrating AI into an existing product with simple requirements? Custom might be faster. For a broader perspective on choosing between pre-built solutions and custom development, see our guide on when to build vs buy AI.
Consider your timeline honestly. Need a prototype in weeks? Frameworks accelerate development. Building a multi-year product where you'll eventually need deep customization? Starting custom often proves faster overall.
Think about scale. Thousands of requests monthly? Frameworks are fine. Millions of requests where latency and cost matter? Custom optimization pays off. One company calculated that 80ms of framework overhead across 10 million monthly requests translated to compute costs they didn't want to pay.
Evaluate your team realistically. Got experienced ML engineers who understand the stack? They can build custom efficiently. Team newer to AI? Framework guardrails help significantly. Don't let ego drive this decision—frameworks exist because they solve real problems.
What Actually Matters When You Choose
LangChain excels at orchestrating complex AI workflows, especially with its ecosystem of LangGraph, LangSmith, and LangFlow. LlamaIndex dominates document retrieval and RAG applications. Custom development gives you control and performance when you need it.
Most successful projects I've seen match the tool to the problem. Your document search doesn't need LangChain's orchestration. Your multi-agent system shouldn't be built entirely custom. The framework you choose today doesn't lock you in forever—I've helped clients migrate in both directions.
Start with the simplest approach that solves your problem. Prove the concept. Expand based on what you learn. Make the choice deliberately based on your specific requirements, not what's trending on Twitter.