AI / LLM · PRO TIER

Ollamapro

Ollama is the de-facto standard for running local large language models on your own hardware. A single binary + REST API that pulls models from a public registry (Llama 3.3, Mistral, Qwen, DeepSeek, Phi-4 and dozens more), handles quantization, GPU offload, and exposes a simple /api/generate and /api/chat interface that's API-compatible with the OpenAI SDK.

Install via WHMCS → Visit ollama.com ↗

🤖 AI / LLM Min 4096 MB RAM Port 11434 (http) Tier pro

// What it is

A closer look.

It's the boring, reliable engine that every other self-hosted AI tool ends up integrating against — Open WebUI, AnythingLLM, LibreChat, Flowise, LangChain, LiteLLM, n8n.

// Use cases

What it's for.

Concrete scenarios where teams pick Ollama over the SaaS alternative.

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Private chat assistants

internal company chat that never sends prompts to OpenAI

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GDPR-compliant LLM access

EU customers in healthcare, legal, finance who can't push prompts to US clouds

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Cost control at scale

predictable per-month VPS bill vs metered API spend

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Air-gapped inference

on-prem or restricted-network environments

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AI app development backbone

local dev loop for engineers building on top of LLMs

// Who it's for

Built for these teams.

If your team profile matches one of these, Ollama is a strong fit out of the box.

Profile A

AI developers & ML engineers

fast local dev loop, no API rate limits, no $ per token while building

Profile B

Privacy-bound enterprises

legal, healthcare, finance, gov teams forbidden from US-hosted LLM APIs

Profile C

Hosting providers

resellers offering "private AI VPS" to their customers as a higher-margin SKU

Profile D

Researchers & academics

evaluating open models without paying OpenAI / Anthropic per experiment

Profile E

Indie SaaS founders

predictable per-month VPS cost beats unpredictable per-token bills as traffic grows

// Differentiators

Why teams pick Ollama.

When evaluating self-hosted options for this category, here are the dimensions on which Ollama consistently lands above the alternatives.

✓OpenAI-compatible API — most existing client code works with a URL change
✓Massive model catalog — with one-command pulls (ollama pull llama3.3)
✓Apache 2.0 license — commercial use unencumbered
✓CPU-capable — for small models (TinyLlama, Phi-3) — runs on a $7/mo VPS for testing
✓GPU optional — but supported (CUDA, ROCm, Apple Metal) when you scale to 7B+ models
✓Single binary — operational simplicity, no Python venv hell

// Integrations

Connects to.

The stack you'll plug Ollama into — services, protocols, and adjacent apps in the BluixApps catalog.

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Chat UIs

Open WebUI, AnythingLLM, LibreChat, Khoj all detect Ollama as first-class backend

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Workflow builders

n8n + Flowise + Langflow + Typebot have native Ollama nodes

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LLM SDKs

LangChain, LlamaIndex, Semantic Kernel, Haystack all support Ollama natively

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OpenAI-proxy gateways

LiteLLM proxies Ollama as if it were OpenAI for legacy clients

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IDE assistants

Continue.dev, Aider, Cline, Cody let devs hit local Ollama for code completion

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Model formats

pulls Hugging Face GGUF directly; Modelfile lets you fork & customize

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Embeddings endpoint

/api/embeddings works with Chroma, Qdrant, pgvector RAG stacks

// Adoption & deployment

Notable users & community

110k+ GitHub stars, top of awesome-selfhosted AI category
Integrated by Continue.dev, Cline, Aider, LangChain, LlamaIndex, LiteLLM, OpenWebUI as a first-class backend
Active Discord, weekly model drops, strong macOS and Linux maintainer community
Backed by ollama.ai company — sustainable dev model with permissive Apache 2.0 license
Cited in countless "self-host your AI stack" guides on r/selfhosted, r/LocalLLaMA

What we ship

Docker compose stack: Ollama server + GPU passthrough config (off by default)
Pre-allocated model storage volume at /var/lib/ollama for persistence across upgrades
Pinned ollama/ollama:0.5.4 image, tracked weekly against upstream
HTTP-only by default on 127.0.0.1:11434; SSL + auth via Nginx Proxy Manager when paired with Open WebUI
Sizing guidance shipped in customer docs: 8 GB RAM minimum for 3B models, 16 GB recommended for 7B, GPU required for 13B+
Backup hook captures /var/lib/ollama before each update (models can be 4-20 GB — opt-in)