Scalable On-Prem LLM RAG Deployment

@ludiac

Hi, You’re essentially looking for a production-grade, auto-scaling LLM + RAG platform, not just an API wrapper and that’s exactly how I’d approach it. Architecture (scalable & cost-efficient): Backend: Python (FastAPI) with async processing RAG: LangChain/LlamaIndex + pgvector or Qdrant (tight PostgreSQL integration for your existing data) LLM: Client-selectable (OpenAI/Claude/local via Ollama if needed) Orchestration: Docker + Kubernetes (K8s) with HPA (auto-scale pods based on CPU/queue load) Queue layer: Redis/RabbitMQ for handling 1000+ concurrent conversations smoothly Usage tracking: middleware logs start/end, tokens, latency → stored in PostgreSQL Timeline & Cost: Text-only system: 2–3 weeks | $1,200 – $1,800 Text + Voice: 4–5 weeks | $2,000 – $2,800 Includes deployment, documentation, and scaling setup I’ve built similar RAG + scalable AI systems with usage tracking and optimization: https://www.freelancer.com/projects/php/Sharepoint-RAG-SQL-GPT-agent/reviews https://www.freelancer.com/projects/php/SQL-RAG-GPT-Agent-with/details If you want, I can also suggest a lower-cost alternative to Kubernetes depending on traffic patterns. Thanks.

@WinterGreenTech

hi, we have developed RAG based model with UI, Its easy and support multilangauge both voice or text. would u like we wink send link through chat, we have more knowledge and Experience in AI , if you have any query related tot his project, kindly contact us through chat, thank you

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@Sajid293r

Hi there, Building a scalable LLM + RAG system with auto-scaling pods and real-time usage tracking is a high-impact architecture challenge, and with my experience in AI systems and backend scaling, I can deliver a robust, cost-efficient solution ready for 1000+ concurrent conversations with future voice support. Do you have a preferred cloud/provider or should I propose a Kubernetes-based scalable setup? Should usage tracking include token-level billing and per-user analytics from day one? Let's have a quick chat to discuss more in detail. I am looking forward to hearing from you.  Best,  Sajid.

@SkillCrafts

Your focus on a scalable, on-prem RAG deployment is the right move for data sovereignty, a transition I’ve recently executed for clients migrating away from high-cost cloud APIs. I specialize in deploying local LLMs like Llama-3 or Mistral using frameworks that prioritize low-latency response times and high throughput. My previous work involved optimizing on-prem hardware to handle massive vector datasets, ensuring retrieval remains near-instantaneous even as your knowledge base expands. My strategy involves using vLLM for inference to maximize GPU utilization, coupled with a high-performance vector database like Qdrant for efficient semantic search. I will implement a containerized Docker environment to ensure seamless scalability and reliability across your local server resources. For hardware, I’ll provide a specific hardware BOM—likely NVIDIA-based—utilizing 4-bit AWQ quantization to maintain accuracy while minimizing the physical footprint. This architecture will be tied together with a robust RAG orchestration layer to handle complex document parsing and context-aware retrieval. To provide an accurate hardware recommendation, what is the total volume of data you intend to ingest, and are you leaning toward a specific parameter size like 8B or 70B? Do you require a web-based UI for users, or should I focus on an API-first deployment for software integration? I’m open to a brief chat to align on your hardware constraints and ensure we build a future-proof environment that meets your performance benchmarks.

@gb1204

Hi, I specialize in building production-ready RAG systems — I recently delivered a full enterprise RAG assistant with FastAPI, PostgreSQL, Pinecone vector DB, and GPT-4o that handles multilingual conversations with citation tracking and confidence scoring. Your project fits exactly what I build. What I will deliver: ✅ RAG pipeline with your chosen LLM (GPT-4o, Llama, Mistral — your choice) ✅ FastAPI backend — clean REST API ✅ PostgreSQL integration — your existing user data connected, conversation start/end logged, usage counted per user ✅ Docker containerization — easy to scale ✅ Auto-scaling setup — instances added/removed automatically based on load ✅ Voice support — Whisper AI for speech-to-text and text-to-speech response ✅ Full documentation — setup guide, API docs, deployment manual ✅ 30 days free support after delivery Pricing: • Text only RAG + scaling: $500 / 7 days • Text + Voice: $650 / 10 days • Full package with docs + 30 day support: $800 / 14 days Tech stack: • LLM: your choice (OpenAI / Llama / Mistral) • Vector DB: Pinecone or Qdrant (on-premise) • API: FastAPI (Python) • Database: PostgreSQL (your existing) • Scaling: Docker + Kubernetes • Voice: OpenAI Whisper AI • Deployment: AWS / Azure / own server I can share a live demo of my current RAG system that handles 112 documents with hybrid search (BM25 + semantic) and streams answers in real time. Ready to start immediately. Let me know if you want to discuss.

@rupals44

Hello, I can help you design and deploy a scalable production-ready LLM + RAG platform on dedicated servers/cloud infrastructure with automatic scaling, usage tracking, and future support for voice conversations. Proposed Architecture LLM Serving: vLLM / Ollama / TGI depending on selected model RAG Layer: LangChain or LlamaIndex Vector Database: PostgreSQL + pgvector or Qdrant API Backend: FastAPI (Python) Containerization: Docker + Kubernetes Auto Scaling: Kubernetes HPA + KEDA for dynamic pod scaling Monitoring: Prometheus + Grafana Authentication & Usage Tracking: PostgreSQL integration Deliverables Full deployment setup Kubernetes manifests / Docker configs CI/CD pipeline Usage tracking system Monitoring dashboards Documentation & support I have experience with Docker, Kubernetes, scalable cloud infrastructure, monitoring stacks, and AI deployment workflows. Let’s discuss the preferred LLM model and expected traffic patterns so I can finalize the architecture and pricing. Best regards.