Fluree AI is a hosted knowledge-graph platform that connects an organization's scattered data — PDFs, spreadsheets, Salesforce, Snowflake, and more — into one governed graph that AI agents and chat tools can query safely. You sign up free, connect a data source, and start asking questions in plain English within minutes.
Short answer: Fluree AI is a free-to-start, serverless knowledge-graph platform that turns siloed business data into a single AI-queryable graph with built-in permissions. It supports 300+ connectors, runs GraphRAG-style queries against Claude, OpenAI, Gemini, or Ollama, and answers your first question in under 30 seconds. Paid tiers add team seats, more connectors, SSO, and single-tenant AWS deployment.
In my testing I went through the free signup at flur.ee/solo, uploaded a sample dataset, and ran the GenAI chat workflow the way a new user would — no sales call, no demo request. Here's what setup actually looks like, what the platform charges once you outgrow the free tier, and where I'd push back on the pitch.
What you'll need
You don't need a database background to start — Fluree AI is built to skip the modeling step most graph databases force on you. You will need at least one real data source to connect: a CSV, an Excel file, a PDF, or one of the 300-plus connectors the platform ships with, including Salesforce, HubSpot, Snowflake, Postgres, BigQuery, Databricks, and Stripe. Sign-up is email-and-password or a Google or GitHub account, no credit card required for the free tier. If you plan to query the graph from an AI assistant rather than the built-in chat, have an API key ready for whichever model you'll connect — Fluree AI's chat is native to the Model Context Protocol, so Claude and Cursor can query it as a first-class tool without custom integration work.
Step-by-step: setting up Fluree AI
1. Create a free account
Go to flur.ee/solo and sign up with email or a Google/GitHub login. No credit card is required, and the vendor's own launch materials claim your first query can run in under 30 seconds once a dataset is loaded.
2. Upload or connect a data source
From the dataset's Files tab, either upload files directly (a model file first, then instance data like CSVs) or connect one of the 300+ live connectors. Fluree AI does automatic schema inference and entity resolution here, which is the step that would otherwise take a data engineer days on a traditional graph database.
3. Review the auto-generated model
Open the Models tab to see the semantic structure Fluree AI inferred — classes, properties, and relationships. Click through a few nodes to confirm the entities line up with what you expected before you start querying against them.
4. Ask your first question in Chat
Click the "+" on the Chat panel to start a session. Fluree AI auto-summarizes the dataset for context, then you ask questions in plain English. This is where GraphRAG does the work — the model doesn't hallucinate an answer, it runs a structured graph query and grounds the response in your actual data.
5. Check the Explain panel before you trust an answer
Every chat answer has a "Show Explain Panel" option that reveals the underlying query and the raw records it pulled from. I'd treat this as mandatory, not optional, the first several times you use a new dataset — it's the fastest way to catch a bad schema inference before it quietly skews every answer downstream.
6. Set access policies if more than one person will use it
Under Policies, create policy groups that restrict which properties and records a user or agent can see, then apply them with "Run as policy group" in chat. Permissions are enforced at the data layer itself, so the same policy holds whether a person or an AI agent is asking.
Example prompts you can copy
Fluree AI's chat is built for natural-language questions against your connected graph, not generic writing prompts. A few patterns that got noticeably better answers when I tested it:
- "Which customers had a support ticket and a contract renewal in the same 30-day window, and what was the outcome?"
- "Summarize every vendor relationship touching our EU entity, and flag any missing compliance documents."
- "Show me the chain of approvals for this transaction, in order, with who approved each step."
- "List every dataset connected to this graph that hasn't been updated in 90 days."
Each one works because it asks for a relationship or a chain, not a single fact — that's the kind of question a graph answers better than a flat search index or a plain vector-search chatbot.
Common mistakes to avoid
The mistake I'd flag first: uploading data before reviewing the auto-inferred model. Skipping that check means you're trusting schema inference blind, and a wrong relationship early on propagates into every answer built on top of it — go slow on the first dataset. Second, connecting a high-value source like Salesforce or Snowflake without setting a policy group first; the free tier doesn't stop you from exposing everything to everyone by default. Third, ignoring the Explain panel because the chat answer "sounds right" — in my testing, a plausible-sounding answer and a data-grounded one aren't always the same thing, and the whole point of GraphRAG is that you can verify the difference in one click. Fourth, treating the free tier's connector limits as unlimited — you'll hit a ceiling fast once you're pulling from more than a couple of live systems, so check your actual data footprint before you build a workflow around it. Fifth, assuming this replaces your existing BI stack; it's a data layer for AI to reason over, not a dashboarding replacement, even though it can build dashboards.
Tools that make this easier
Fluree AI sits in the enterprise data-and-agent-governance category, alongside a few other platforms I've tested this year. If your team is comparing this against a lighter-weight, IT-first governance approach, Lunen.ai takes a similar "policy before automation" stance but starts from plain-language agent-building rather than a knowledge graph. For AI assistants that already do agentic work inside chat, how to use ChatGPT agents covers the simpler, self-serve alternative most teams try first. If you're specifically connecting Claude to a graph like this via MCP, my Unabyss for Claude guide covers a different way to give Claude persistent context across tools. Smaller teams unsure whether an enterprise data platform is overkill should read best AI tool for small business before committing budget here. Engineering teams building the connectors and queries themselves will get more mileage from my ranked best AI tool for code picks for the coding-assistant side of the stack. And if you want to see how Fluree AI stacks up against everything else I've tested, how we test AI tools explains the process; free AI tools is a good starting point if you're not ready to connect a paid data source yet.
Pricing
Fluree AI publishes a genuine free tier — no credit card, sign up and query the same day. Paid tiers exist for teams that need more connectors, collaboration, or enterprise controls, but Fluree does not publish exact dollar figures for those tiers; you get pricing through a sales conversation once you outgrow free usage, per Fluree’s July 23, 2026 launch announcement.
| Tier | Price | Connectors | Best for |
|---|---|---|---|
| Free (Solo) | $0, no credit card | Limited, self-serve | Individuals testing a dataset or a single use case |
| Team | Contact sales | Higher limits + collaboration | Teams sharing one graph across roles |
| Enterprise | Contact sales | 300+, SSO, single-tenant AWS | Regulated orgs needing audit trails and isolation |
Enterprise deployments can run entirely inside a customer's own AWS account as a self-contained, versioned stack — a meaningful option if data residency or isolation is a hard requirement, not a nice-to-have.
My take
Fluree earned a Gartner Cool Vendor recognition in data management back in 2024, and the AI-era pivot makes sense of that early bet: GraphRAG genuinely does reduce hallucination risk versus asking a model to reason over unstructured text alone, because the answer traces back to a query you can inspect (PR Newswire, Jan. 30, 2024). What impressed me most in testing wasn't a single feature — it was that the free tier is a real product, not a locked demo, so you can judge the schema-inference quality yourself before any sales conversation happens. What I'd push back on: the lack of published team and enterprise pricing means budgeting is a guessing game until you're already invested in a dataset, and the platform assumes you have data worth connecting in the first place. If you're a solo builder or analyst with a specific dataset and a real question, the free tier is worth an afternoon. If you're evaluating this for a whole org, go in with a specific workflow — not a vague "we want AI on our data" — the same rule that applies to any enterprise AI platform.
Frequently Asked Questions
Is Fluree AI free?
Yes, for individual use. The Solo tier requires no credit card and lets you connect a dataset and start querying immediately. Team and Enterprise tiers require a sales conversation for pricing.
How long does it take to set up Fluree AI?
The vendor claims a first query in under 30 seconds once a dataset is uploaded. In my testing, getting a small CSV dataset modeled, connected, and queryable took closer to 15–20 minutes, most of it spent reviewing the auto-inferred schema before trusting it.
What is the easiest way to get good answers out of it?
Ask questions that involve a relationship or a chain of events, not a single fact — that's what a knowledge graph is built to answer well. Check the Explain panel on your first several queries so you know the schema inference is accurate before you rely on it.
Do I need to know graph databases or query languages to use it?
No. The chat interface takes natural-language questions and translates them into structured graph queries behind the scenes; you can view the generated query in the Explain panel but never have to write one yourself.
How is this different from a normal AI chatbot connected to my files?
A chatbot doing retrieval over documents is matching text similarity. Fluree AI builds an actual graph of entities and relationships first, so it can answer multi-hop questions — "who approved what, in what order" — that a flat document search can't reliably trace.