building ai chat saas, multi-llm chat app development, chatfaster -...

Source: Dev.to

Ever felt limited by using just one AI model for your work?
In January 2026 the AI world is moving faster than ever, so I set out to build a solution that gives users the best of every major model in one place. This article shares my personal story of building ChatFaster – a production‑grade, multi‑LLM chat SaaS.

Why a Multi‑Model Platform?

Each model has its own strengths:

Having all of them behind a single UI lets users:

• Avoid vendor lock‑in – switch providers if one goes down.
• Save costs – use cheaper models for simple tasks.
• Get better results – compare answers side‑by‑side.
• Pick the right tool – e.g., a coding‑focused model vs. a summarisation‑focused one.

My Background

• 7+ years as a Senior Full‑Stack Engineer.
• Built systems for DIOR, IKEA, M&S.
• Wanted to create a production‑grade product that solves real problems, not just a hobby wrapper.

The Tech Stack

Front‑end

Back‑end

• NestJS 11 – Structured framework ideal for large applications.
• MongoDB Atlas – Stores chat messages as embedded documents for ultra‑fast history reads.
• Redis – Caches frequent requests, keeping the app snappy.

Desktop App

• Tauri – Builds a native macOS (and Windows/Linux) client.
• Deep linking – Opens the desktop app directly from the browser.

Core Challenges & Solutions

Unified API Layer

• Integrated OpenAI, Anthropic, Google Gemini behind a single wrapper.
• Mapped each provider’s response format to a common schema, so the front‑end stays agnostic.

Model Switching

• Users can start a conversation with GPT‑4o and switch to Claude mid‑chat while preserving context.
• Implemented a context‑preserving mechanism that re‑injects the shared history into the new model.

Context‑Window Management

• Different models have vastly different token limits.
• Built a sliding‑window token counter that automatically trims the oldest messages when the limit is reached.

Real‑time Streaming

• Used Server‑Sent Events (SSE) to display tokens as they are generated, giving a ChatGPT‑like experience.

Retrieval‑Augmented Generation (RAG)

• Knowledge Base built with Cloudflare Vectorize.
• Documents are embedded via OpenAI embeddings, stored as vectors, and searched at query time.
• The most relevant snippets are injected into the prompt.

File Uploads

• Leveraged presigned URLs for Cloudflare R2 so users upload files directly to storage, bypassing the backend and reducing load.

Rate Limiting & Billing

• Custom throttler in NestJS tied to Stripe subscription tiers.
• Guarantees fair usage per plan.

Security

• All user‑provided API keys are encrypted with AES‑256‑GCM on the server; only the user can decrypt them.

Offline‑First Experience

• Chats are persisted locally in IndexedDB.
• A delta‑sync routine pushes changes to MongoDB when the network is available.

“Personal Memory” Feature

• Prefix a message with a special token (e.g., !mem) and the content is stored forever as a personal knowledge snippet.
• Acts like a persistent brain for the AI, enabling long‑term context across sessions.

Lessons Learned

1. User Experience Trumps Technology – A polished UI and smooth state handling make a huge difference.
2. Simplicity in Choice – Too many models overwhelm users; categorising them by “best use case” helps.
3. Unified Interfaces Pay Off – Abstracting provider differences early saves countless headaches later.
4. Performance is Multi‑Faceted – Fast builds, streaming responses, caching, and local storage all contribute to perceived speed.

Where to Find More Details

• Next.js Docs – Streaming, API routes, and Turbopack.
• Vercel AI SDK – Handling multi‑provider streams.
• NestJS Documentation – Custom throttlers and middleware.
• Cloudflare Vectorize – Vector search setup and best practices.

Building an Instant‑Feel AI Chat SaaS

“Makes the app feel instant. There is no waiting for the page to load every time you click a chat.”

I made a few mistakes along the way. One big one was trying to store everything in the main database. At first I didn’t use Redis for caching, and the app got slow as more people joined. I soon realized that building an AI‑chat SaaS (multi‑LLM chat app) requires a smart caching strategy.

Common Pitfalls

• Ignoring Token Costs – If you don’t track usage, your API bill will explode.
• Poor Error Handling – AI APIs fail often. You need good retry logic.
• Slow UI – If the text doesn’t stream smoothly, users will leave.
• Bad Security – Never store plain‑text API keys in your database.

Security First

Security is the most important part. I built an “API Key Vault.”

• The server never sees the actual keys in plain text.
• Keys are encrypted on the client side before being sent.

This builds trust with your users. If you’re looking for open‑source examples of secure patterns, check out community‑vetted libraries on GitHub.

The “Organization” Feature

I had to build a system where teams could share a knowledge base but keep their chats private. This required complex permission logic in NestJS. It took me two weeks just to get the database schema right, but it was worth it to make the product feel professional.

Getting Started

If you want to start building an AI‑chat SaaS (multi‑LLM chat app), don’t try to do everything at once.

1. Pick your core stack – I recommend Next.js and a Node.js backend.
2. Set up streaming – Get the basic chat working with the Vercel AI SDK.
3. Add user auth – I used Firebase Auth because it scales easily.
4. Implement Stripe – Set up your pricing tiers early so you can test the payment flow.
5. Focus on UX – Ensure the app works well on both mobile and desktop.

I spent months refining the “feel” of the chat before adding advanced RAG features.

Additional Lessons Learned

• Caching: Use Redis (or similar) to keep frequently accessed data in memory.
• Token Management: Continuously monitor usage and implement cost‑control mechanisms.
• Prompt Engineering: Invest time in crafting robust prompts; it dramatically improves output quality.
• Data Security: Encrypt API keys client‑side, use environment‑protected secrets, and follow least‑privilege principles.

Why ChatFaster Helps

ChatFaster streamlines development by providing:

• Pre‑built infrastructure and unified APIs that handle model integration complexities.
• A focus on building unique features and refining user experience rather than managing low‑level backend architecture.

Model Selection Guidance

Choosing the right model depends on balancing accuracy, latency, and budget for your SaaS platform.

Final Thoughts

Building AI chat SaaS requires a robust backend, secure API connections to various language models, and a user‑friendly interface. It also involves subscription management and strict data‑privacy practices to deliver a scalable, reliable service.

I’m proud of how ChatFaster turned out—it taught me a lot about scaling AI systems. I’ve used these same skills to help multi‑market brands like Al‑Futtaim build headless commerce sites.

If you’re a founder or developer looking for help with React, Next.js, or AI‑chat SaaS, feel free to reach out. I’m open to interesting projects, consulting, or senior‑level contract work.

Check out the final product at chatfaster and let’s connect!