Ollama runs open models locally with an OpenAI-compatible API—classify or redact scraped text before cloud spend, then store outputs with Apify datasets.
Ollama runs open models locally behind an OpenAI-compatible API, letting you classify or redact scraped text before spending on cloud calls. These guides cover setting up Ollama and pointing data pipelines at it.
Local inference keeps sensitive data on your own hardware and cuts per-token cost at volume. Apify datasets feed Ollama for batch processing, and outputs store right back. Below you will find setup and integration tutorials.
Ollama and vLLM both let you run open-weight LLMs on your own hardware without sending prompts to OpenAI or Anthropic. But they are built for different points on the scale curve — Ollama for developers and small teams, vLLM for production multi-user inference workloads where throughput matters.
ChatGPT Team costs $25 per user per month. At 10 users that is $3,000 per year — before you factor in rate limits, data residency concerns, and the fact that every prompt you send trains future OpenAI models unless you opt out. Self-hosting an LLM stack eliminates per-seat costs, keeps prompts on hardware you control, and lets you run models tuned for your domain.
TL;DR
docker-compose.yml: Ollama + LiteLLM + Open WebUI + Authentik + PostgreSQL + Redis + CaddyEvery prompt your team sends to ChatGPT Team or Copilot for Microsoft 365 leaves your network and is processed on OpenAI's or Microsoft's servers. For most teams that's an acceptable trade-off. For teams handling legal documents, source code, financial data, or customer PII, it is not. This guide deploys a private ChatGPT equivalent — Llama 3 70B running on your own GPU hardware, with a ChatGPT-quality web interface, your company's SSO, and per-team usage budgets — entirely inside your infrastructure.
TL;DR
docker-compose.yml: Dify (API + worker + web + sandbox) + Qdrant + Ollama + PostgreSQL + Redis + CaddyRetrieval-Augmented Generation (RAG) lets you ask questions against a private document corpus and get answers grounded in the actual content. The dominant hosted pattern — OpenAI Assistants + Pinecone — works, but every document chunk and every query travels to OpenAI's servers. For legal contracts, internal knowledge bases, or any non-public data, that is a compliance liability.
This guide deploys a fully self-hosted RAG stack: Dify as the orchestration and document management layer, Qdrant as the vector database, and Ollama serving both the embedding model and the chat LLM — all on a single Liquid Web VPS.
Cloud API costs scale linearly with usage. Self-hosting Ollama + n8n + Coolify on a $20/month VPS removes per-token fees for local Ollama inference — you still pay for the VPS, bandwidth, and your time; throughput is limited by CPU/RAM. When workflows call Apify, email, or other SaaS APIs, data transits those providers — not only your server.
This guide covers the complete setup from bare VPS to production-ready AI pipeline, with official documentation links for every step.
TL;DR:
| Component | Role | Official docs |
|---|---|---|
| Coolify | Infrastructure manager (replaces Heroku/Vercel for self-hosting) | coolify.io/docs |
| Ollama | Local LLM inference (Llama 3.1, Mistral, DeepSeek) | ollama.com |
| n8n | Workflow orchestration (visual automation) | docs.n8n.io |
| Qdrant | Vector database for AI memory/RAG (Retrieval-Augmented Generation) | qdrant.tech/documentation |
| PostgreSQL | Structured data storage | postgresql.org |
Prerequisites:
The n8n + Dify + Ollama stack is a common 2026 self-hosted pattern for teams that want open-source control over automation, LLM apps, and where models run: n8n runs event-driven workflows across SaaS and APIs; Dify ships LLM apps, agents, and knowledge bases; Ollama exposes a local OpenAI-compatible inference API on your hardware. None fully replaces the others, but teams often blur which layer owns which job.
This article maps each tool’s role, where capabilities overlap, how they compose into one architecture versus compete for the same budget and headcount, and a practical deployment roadmap you can adapt from a laptop toward production.
For how agent runtimes, MCP, and infra headlines affect wiring choices like these, see Top 10 AI and tech stories this week (March 17–24, 2026).
Self-hosting OpenClaw with a cloud API backend is easy. But cloud APIs have costs that scale with usage, and they receive every message you send. If your team uses OpenClaw heavily, or if data privacy is a concern, local GPU inference solves both problems: your data stays on your hardware, and you pay a flat server rate instead of per-token fees.
Retrieval Augmented Generation (RAG) lets your chatbot answer from your own documents instead of relying solely on the model's training data. This guide shows you how to build a fully local RAG pipeline: documents → embeddings → ChromaDB vector store → retrieval → Ollama LLM → answers. No cloud APIs. No per-token billing. Everything runs on your machine or a Liquid Web GPU VPS for heavier workloads.
Ollama runs open-source LLMs locally. Open WebUI provides a ChatGPT-like interface that connects to Ollama (or OpenAI/Anthropic). Together they give you a fully private chat experience — no data leaves your server. This guide covers installation, model choices by hardware, and production setup on CPU or GPU VPS.
OpenClaw is a multi-platform AI assistant — web UI, API, Telegram, Discord, Slack, and more — all fronting a single LLM backend. Connect it to Ollama and your local model becomes usable everywhere: from your phone via Telegram, from code via API, from the web dashboard. No data leaves your machine. This guide covers setup, config, use cases, and connecting multiple backends (Ollama + Anthropic + OpenAI) on a Liquid Web VPS.
Ollama is an open-source tool (170k+ GitHub stars) for running large language models locally. It provides an OpenAI-compatible API so tools like Open WebUI, LiteLLM, and AnythingLLM connect without code changes. Run it on a Liquid Web GPU server for production inference with no cold-start latency.
Run ollama pull llama3 to download the model, then POST text chunks to http://localhost:11434/api/generate with a structured extraction prompt. Use Python requests or the openai client library pointing to the Ollama base URL. Process Apify dataset exports locally for privacy-sensitive projects that cannot use cloud AI APIs.
Llama 3.1 8B and Mistral 7B offer the best speed-accuracy balance for extraction tasks on consumer hardware. Phi-3 Mini is fastest on CPU. For complex reasoning over long documents, Llama 3 70B or Qwen 72B give accuracy closer to GPT-4 but require 40-48GB VRAM. Always benchmark on 100 samples from your actual data.
An Apple Silicon Mac (M2/M3 with 16GB+ RAM) handles 7B models well for batch overnight processing. On Linux, a consumer GPU with 8GB+ VRAM runs 7B models in FP16. CPU-only inference is viable for overnight batch jobs but too slow for interactive use. Prioritize VRAM for GPU inference; RAM for CPU or Apple Silicon inference.
For 7B parameter models: 8 GB VRAM (consumer GPU or cloud L4). For 70B parameter models: 48 GB VRAM (Liquid Web L40S). For frontier models (Llama 3.1 405B): multiple A100s or H100s. Liquid Web offers dedicated L40S (48 GB) and H100 NVL (94 GB) GPU servers. Prices subject to change.
