Ollama vs RAGFlow

Which one should you try?

Ollama

Freemium

Best for Developers who want a local model API without managing model files by hand

Visit Ollama See full review

RAGFlow

Freemium

Best for Teams requiring strong document fidelity and structure-aware parsing

Visit RAGFlow See full review

Field notes

All field notes →

Ollama in Docker: check the thread count before changing hardware
In our three-core container, Ollama started eight threads. Explicitly matching the thread setting to the available cores changed generation time substantially.
Ollama vs llama.cpp: a paired test with the same 3B model
Our controlled pair showed modest generation differences and different process-memory and Docker-image footprints. It did not establish a universal runtime winner.

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Ollama

Ollama downloads open models and runs them locally behind a command line and API, with optional cloud-hosted models. We ran it on a 3-core CPU server and measured startup, memory and speed.

RAGFlow

Open-source RAG engine with deep document understanding, hybrid search, and agentic workflow orchestration.

Attribute	Ollama	RAGFlow
Pricing	Freemium	Freemium
Price	$20/mo or $200/yr	$29/mo
Pricing checked	Nov 3, 2026	Jun 11, 2026
Free trial	Not stated	Not stated
Open source	Yes	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	macOS, Windows, Linux, Docker	Docker, Kubernetes, Linux, macOS, cloud (cloud.ragflow.io)
Released	2023-06	2024-04
Pros	Pulls and runs a model with one command and serves it over a local API. Quick cold start in our test: about 8 seconds from server start to first answer. OLLAMA_NO_CLOUD=1 turns off its cloud models and web search for local-only use. Generated text faster than LM Studio on the same model and cores, by up to 1.38x with short prompts.	Deep document understanding and structure recognition reduce noise and hallucinations Unified agentic platform—RAG, tools, and MCPs in one orchestration layer Fully open source, self-hostable, and enterprise-ready deployment options Rich visual UI with workflow builder, citation tracking, and chunking visualization Active community and rapid iteration; frequent feature and model updates
Cons	Ignores a container's CPU limit: it started 8 threads on 3 cores, and generation was 3.3 to 11.9 times slower until we set num_thread to 3. Caps context at the model's trained length (32,768 tokens for our model) even when you ask for more, so check what you actually get. The largest Docker image of the runtimes we tested: 9.37 GB, against 1.21 GB for llama.cpp.	Complex stack requiring Docker, Elasticsearch or Infinity, MySQL, MinIO, Redis—steep DevOps overhead Slower time-to-value for prototyping compared to managed SaaS alternatives Documentation and community libraries smaller than mature frameworks like LangChain

Made up your mind?

Ollama

Freemium

Best for Developers who want a local model API without managing model files by hand

Visit Ollama See full review

RAGFlow

Freemium

Best for Teams requiring strong document fidelity and structure-aware parsing

Visit RAGFlow See full review

Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

Ollama vs RAGFlow

Which one should you try?

Ollama

RAGFlow

Field notes

Ollama

RAGFlow

Made up your mind?

Ollama

RAGFlow

Related comparisons

Free trials & curated lists

Share this comparison