When Flowise announced it was shutting down, the team offered a sentence worth holding before you decide where to go next: coding agents had made "the typical rigid workflow low-code approach quickly hit the limit on complexity." Flowise was not a marginal voice — it was an enterprise-acquired project of approximately 55,000 GitHub stars, the closest architectural analogue to a visual agent canvas at commercial scale, acquired by Workday in August 2025.
Cortex is a visual AI agent builder sold once as source code you deploy under your own brand. Wire an agent's reasoning on a six-category node canvas, watch the node-execution trace stream in real time as you test, and ship as an embeddable widget or public link. No per-seat fee. No per-message platform rent. This page compares it honestly against Dify, names Langflow as the counter-argument you deserve to hear, and maps the four ways to acquire a platform of this shape.
The ceiling Flowise named is real. If you are an engineer who already works with a coding agent, the visual canvas is not your tool. LangGraph, CrewAI, and Microsoft Agent Framework — which unified AutoGen and Semantic Kernel into a 1.0 release in April 2026 — are built for people who live in a terminal. They are free, more flexible for complex multi-agent topologies, and do not require a UI. A canvas is a constraint for that buyer, not a feature.
The buyer who still has a use for the visual surface is someone else. The operator who needs to hand a non-technical client a working agent product — not a GitHub repository. The agency whose delivery artifact is a deployed, white-labelled service the client can update through a browser, not a code editor. The founder who intends to charge for an agent-powered service and needs the billing infrastructure, the multi-tenant isolation, and the admin panel to do it — without paying per-conversation and per-seat rent to whoever controls the hosted platform this year.
For that buyer, Flowise demonstrated by archiving that a platform you rent or rely on can be deprecated out from under you. Apache 2.0 survives the archival — Flowise's own shutdown notice says the code "will still remain on Github and the Apache 2.0 licensed code is yours to keep," with community forks explicitly encouraged. What ends is upstream maintenance: no new features, no security patches, no core-team support. Unmaintained is not unusable. It is, however, a real risk for any security-sensitive deployment that depends on someone else filing CVEs. Owning the source code is the hedge against that failure mode — not a claim, a transfer of control.
Decision guide
| If the requirement is… | The honest answer |
|---|---|
| Running agents internally for your own team — single-tenant, not operated for clients | Dify community self-host: free, more mature, no restriction on this use case |
| Maximum licence freedom with no platform chassis to build | Langflow (plain MIT, IBM-backed) — more permissive than Cortex on permission alone |
| Automation workflows more powerful than HTTP calls and webhooks | n8n — broader automation depth; Sustainable Use License restrictions apply to commercial hosting (€20–€667/mo annual, n8n.io/pricing, 2026-08-07) |
| Complex multi-agent topologies built in Python or TypeScript | LangGraph, CrewAI, or Microsoft Agent Framework — free, flexible, built for engineers; the visual canvas is a constraint at this level |
| A visual agent-builder platform to run for multiple paying clients — white-labelled, under your own brand, without a per-workspace subscription to a third-party platform | Cortex — https://saascode.ai/products/cortex |
Capability table
| Cortex | Dify | |
|---|---|---|
| Licence for multi-tenant commercial use | No restriction — included in the one-time purchase | Requires written authorization from the Dify team or a commercial licence; community edition forbids it |
| Node canvas | Six categories: Brain · Knowledge · Tool · Decision · Guardrail · Output — confirmed in source (node-types.ts) | Flow-based editor with LLM, Knowledge, and Tool blocks |
| Node-execution trace | Streamed in real time to the live-preview panel during testing (SSE events: node_start / node_done / node_error) | Workflow run history logged post-run |
| Guardrails as canvas nodes | First-class graph objects: topic filter, keyword allow/deny list, PII masking (email/phone/card patterns), response validation, max-turns escalation — confirmed in source (guardrail-executor.ts) | Moderation settings in config panel; not modelled as visible nodes in the graph |
| Confidence-based routing | Explicit Decision node with configurable confidence_threshold slider — branch when confidence falls below the threshold you set | Conditional branching available; confidence-as-explicit-primitive differs by implementation |
| Human handoff node | Output node in handoff mode — emits context summary to dashboard with terminalStatus: "escalated" | Human handoff available as an escalation path |
| Knowledge base / RAG | Document upload, chunking, pgvector semantic search, conversation-memory window — confirmed in source | Mature RAG pipeline with more indexing strategies, retrieval modes, and production hardening |
| Deploy surfaces | Embeddable vanilla JS widget (Shadow DOM, no iframe, <30 KB gzipped, allowed-domain enforcement) · public shareable link | Embeddable widget · standalone app URL · API endpoint |
| Model selection | OpenRouter routing; model configured per Brain node from a curated list | Broader model selection across providers; no model gated to a higher plan tier |
| Connector / plugin ecosystem | HTTP call · parameterized DB query · email send · webhook — no code sandbox (deliberate single-service constraint) | 50+ connector plugins across a mature ecosystem |
| Commercial SaaS chassis | Multi-tenant orgs · RBAC · plan-gated billing · admin panel · usage metering · conversation analytics · agent versioning — ships with the codebase | Provided by the Dify Cloud service on a per-workspace-per-month basis; community self-host provides the canvas, not the commercial layer |
| What Dify does that Cortex does not | — | Substantially larger plugin and connector ecosystem; more mature RAG pipeline with more retrieval strategies; broader model selection; years of production hardening across edge cases; free for single-tenant internal self-hosted use |
Cortex vs. Dify
What each is at the owner level
Dify is a business someone built and runs: a visual-builder platform where organizations create agents, manage RAG knowledge bases, and embed or publish finished apps — available as a managed cloud service or as a self-hosted deployment. The buyer of Dify is a team or company that wants to use that platform. The buyer of Cortex is the person who wants to operate a platform of that shape for their own clients, under their own name, on their own infrastructure.
That distinction is not abstract. The licence makes it concrete. Dify's licence carries two conditions on top of Apache 2.0. First: you may not remove or modify the Dify logo or copyright information in the frontend or console. Second: you may not use the Dify source code to operate a multi-tenant environment — defined as separate workspace data and configurations per customer — without written authorization from the Dify team. A buyer who wants to run Dify for multiple paying clients needs a Dify commercial licence. Verified at primary source: github.com/langgenius/dify/blob/main/LICENSE.
When Dify is the right call
Dify has been in production substantially longer than Cortex v1. Its plugin ecosystem and connector library are deeper, its RAG pipeline more tested across edge cases, its model selection broader. Its documentation is more extensive and its community larger.
For a team running agents internally — a single-tenant deployment for their own organization's use — Dify is free to self-host under the community licence, more capable at launch, and carries no restriction on that use case. A buyer who wants to run agent-powered tools for their own team, not for external paying clients, should use Dify's free self-hosted community edition. That case does not belong to Cortex.
When owning Cortex is the right call
The moment you are running separate workspaces for separate paying clients — each with isolated data, their own agent library, their own knowledge bases — you are operating a multi-tenant environment under Dify's licence definition, and you need Dify's commercial licence. Cortex is sold once, as source, with no such restriction.
The second difference is the metering model. Dify Cloud charges per workspace per month. Scaling from two clients to twenty means twenty workspace subscriptions. Cortex's end-user plan limits are values you configure, profit from, and control inside your own deployment. They are not an obligation owed to a third-party platform.
The money
Dify Cloud: free Sandbox tier (200 message credits total, not per month); Professional at $59 per workspace per month; Team at $159 per workspace per month; Enterprise on annual custom terms only. Source: comparedge.com/tools/dify-ai/pricing and checkthat.ai/brands/dify/pricing, as of 2026. Dify self-hosted: free for the community edition for single-tenant internal use; licence-restricted for multi-tenant commercial deployments as above.
Cortex: $179 — one payment. Ongoing costs are yours to project: hosting, Postgres, vector storage, and LLM tokens metered at the model provider. No workspace subscription. No per-user charge. No platform fee that scales with your client count.
Langflow — the more permissive alternative
Any honest comparison in this space requires naming Langflow, because the licence argument that justifies a source-code purchase is narrower than it might appear.
Langflow is plain MIT — verified at primary source: github.com/langflow-ai/langflow/blob/main/LICENSE. No multi-tenant restriction. No fair-code clause. No enterprise carve-out. You may modify it, redistribute it, sell it, and operate it as a hosted service for clients, with no obligation beyond retaining the copyright notice and licence text. IBM backs its development. The DataStax hosted service retired in April 2026, but the OSS project is actively maintained. On licence freedom alone, Langflow is more permissive than Cortex. If the licence comparison is your whole purchase driver and you are comfortable building your own deployment, Langflow is the honest recommendation — not Cortex.
What Langflow gives you is a canvas engine. What it does not give you is the commercial SaaS chassis: multi-tenant organizations with isolated data per tenant, RBAC with schema-level role enforcement, plan-gated billing, admin panel, usage metering, conversation analytics, and agent versioning. Cortex ships the canvas plus that infrastructure as a single codebase — the purchase price buys that work completed rather than deferred.
Ways to acquire an agent-builder platform
The buyer comparing Cortex against Dify is choosing among four paths to the same outcome: running a visual AI agent-builder platform for clients.
| Path | Upfront cost | Ongoing cost | Commercial multi-tenant right | SaaS chassis |
|---|---|---|---|---|
| Rent a hosted builder (Dify Cloud, Botpress, Voiceflow) | $0 | Dify: $59–$159/workspace/mo · others vary; per-seat and per-message models common | Defined by the vendor's terms; subject to price and policy changes at renewal | Ships with the subscription |
| Self-host under a restricted open-source licence (Dify community, n8n Sustainable Use) | $0 | Hosting + LLM tokens | Multi-tenant or commercial resale restricted — separate licence required | Partial or absent; commercial hosting blocked without additional licence |
| Self-host MIT open source (Langflow) | $0 | Hosting + LLM tokens + engineering time to build the chassis | No restriction — modify, resell, and host as a service freely under MIT | Must be built |
| Buy Cortex | $179 | Hosting + LLM tokens | No multi-tenant restriction — operating it for your own paying clients is included in the purchase | Ships with the codebase |
The engineering cost of the Langflow path is real: multi-tenant isolation, RBAC, billing, metering, admin panel, and versioning are each an implementation project.
n8n is worth a precise note because it attracts the automation-adjacent buyer. n8n operates under a Sustainable Use License — fair-code, not OSI open source. The operative clause: use is permitted for internal business purposes, but hosting n8n as a paid service, white-labelling it to clients, or embedding it in a commercial product requires a separate commercial or embed licence from n8n. n8n Cloud: €20 to €667 per month on annual billing (Starter to Business), as of 2026-08-07 (n8n.io/pricing, fetched at primary source). For a buyer whose primary need is workflow automation rather than AI agent design, n8n is the more capable and better-suited product; Cortex's tool nodes — HTTP, database query, email, webhook — are intentionally narrower to preserve the single-service architecture.
Common questions
Answered honestly, including where the answer is not Cortex.
Is Dify really free to self-host?
For single-tenant internal use, yes. Dify's community edition is free to self-host under Apache 2.0. The restriction activates when you operate it as a multi-tenant environment — separate workspace data and configurations for separate paying customers. That use requires written authorization from the Dify team or a commercial licence. The restriction lives in the GitHub LICENSE file at primary source, not in a dashboard terms-of-service (github.com/langgenius/dify/blob/main/LICENSE, verified 2026-08-08).
Can you build on top of Langflow and sell what you build?
Yes. Langflow is plain MIT. You may modify it, sell it, and operate it as a hosted service for clients with no obligation beyond retaining the copyright notice. The practical question is what you are selling. Langflow gives you the canvas engine. The billing layer, multi-tenant isolation, RBAC, admin panel, and usage metering are not in it — those must be built. If the product you intend to launch is the canvas plus a complete sellable service around it, you are building the chassis as well as using the canvas.
What happens if Dify changes its commercial licence terms after you deploy?
You can continue running the community edition you already have — you own that deployment. The multi-tenant commercial right, however, requires current authorization from the Dify team. A terms change or price change in their commercial licence affects any renewal or renegotiation. Buying the source code transfers that decision to you permanently: the right to run it for clients is included in the purchase, not on a vendor's renewal terms.
What is the difference between a node-execution trace and a workflow run log?
A node-execution trace streams in real time while you test the agent — you see which node fired, what it received, and what it returned, in the sequence it actually ran, before the test conversation ends. A run log records what happened after the fact. The trace is the design feedback loop: it shows you where the agent went wrong in the same moment you can adjust the graph. Cortex's live-preview panel streams this via SSE events (node_start / node_done / node_error) during every test interaction, confirmed in the sse-runner service.
Can an agency run separate agent workspaces for each client without paying a per-workspace fee?
On Dify Cloud, each client workspace is a separate subscription: $59 to $159 per month depending on tier, as of 2026 (comparedge.com/tools/dify-ai/pricing). Scaling to twenty clients means twenty workspace subscriptions. On Cortex, you run one deployment and create organizations for each client inside it — the limits on what each organization can do are the plan limits you configured for your own product. No per-client fee goes to a third-party platform.
What does "no code sandbox" mean and why was it a deliberate choice?
Cortex's tool nodes cover HTTP calls, parameterized database queries, email sends, and webhook fires — there is no node that executes arbitrary JavaScript or Python. The constraint is architectural and intentional: removing a separate execution worker means the whole platform runs as a single Next.js service on standard serverless infrastructure, with no worker process to secure, scale, or operate separately. The practical implication: if your agents need to run computation, transform data programmatically, or call an SDK directly, wrap that logic in your own API endpoint and call it via the HTTP tool node — or Cortex is not the right fit for that agent.