Flowise
Visual builder for LLM apps and agents - 100+ integrations, embeddable chat widgets, no prediction caps.
One-click deploy, from $13/mo on a Miget plan.
Flowise is the visual layer of the LLM stack: drag models, vector stores, tools, and memory onto a canvas, wire them into chatflows or multi-agent flows, and ship the result as an API or an embeddable chat widget. For teams where not everyone writes LangChain by hand, it is the fastest route from idea to working assistant.
This template runs Flowise v3 on a managed Postgres (flows, users, credentials) with a volume for uploaded files. v3 auth means accounts are created in the UI on first visit; four secrets in env keep sessions and stored provider credentials stable across redeploys.
It slots into the catalogue’s LLM lane end to end: models via the litellm gateway, vectors in qdrant or chromadb, traces to langfuse - every connection by service name, nothing leaving the project network.
Upstream project: Flowise
#what you get
- Drag-and-drop chatflows and agentflows over 100+ integrations
- Ship flows as REST APIs or embeddable chat widgets
- Managed Postgres for flows/users/credentials - clean ops
- Credential encryption with a fixed, recreation-safe key
- Optional queue mode (Redis) when throughput demands it
- Apache-2.0 core - the community image is the product
#topology
| Service | Role | Public |
|---|---|---|
| flowise | builder UI + runtime APIs (:5000) | yes (accounts) |
| db | Postgres - managed service on Miget, container locally | no |
#miget sizing
// this stack needs
2 GiB RAM · 10 GB disk · 2 services
1 GiB runs the builder and typical flow traffic; the LLM heavy lifting happens at your providers. Scale up (or enable queue mode) when widget traffic grows.
Hobby - recommended fit
$13/mo
1 vCPU · 2 GiB · 50 GiB disk
Headroom for your own apps: 2 GiB at $19/mo
Professional - production
$22/mo
1 vCPU · 2 GiB · 10 GiB disk
Dedicated resources, production SLOs - plan details
One Miget plan is a fixed pool of compute - the whole stack (managed databases included) deploys inside it, and anything left over runs your other apps. No per-service or per-seat math.
#vs. the managed service
What the hosted equivalents charge, against the flat Miget plan this stack fits on. Prices as of June 2026, sources linked.
| Service | Plan | Monthly | What you get |
|---|---|---|---|
| Flowise on Miget ★ | 2 GiB plan | $13 | this whole stack, flat - no usage meters, and room left for your own apps |
| Flowise Cloud | Starter | ~$35 | 10,000 predictions/mo cap, 1 GB storage |
| Dify Cloud | Professional | ~$59 | per workspace: 5,000 message credits/mo, 3 members, 50 apps |
Self-hosted Flowise has no prediction meter - flows run on your provider keys at raw token prices.
#vs. other PaaS
Estimated monthly cost of running this exact stack (2 GiB RAM, 10 GB disk, 2 containers) elsewhere, from published June 2026 rates.
| Platform | Est. monthly | Notes |
|---|---|---|
| Miget ★ | $13 flat | compose stacks first-class: one deploy, dedicated vCPU, managed Postgres/Valkey, volumes and TLS all included in the plan |
| Heroku | ~$100 | no volumes; nothing between 1 GB ($50) and 2.5 GB ($250) dynos - 2 GB containers cost far more than shown |
| DO App Platform | ~$29 | no persistent volumes - stateful containers need managed DBs/Spaces (base $5 Spaces included here) |
| Render | ~$28 | per-service instances (0.5 GB $7, 2 GB $25) - every container is its own paid service |
| Railway | ~$22 | usage-based ($10/GB RAM-mo); vCPU billed separately at $20/vCPU-mo on top |
| Fly.io | ~$13 | cheapest sticker price - but burstable shared CPUs (1/16 core; dedicated vCPUs cost ~2-3×), no compose deploys (one app per container, manual wiring), managed DBs billed extra |
Estimates assume RAM fully allocated at published on-demand rates - and sticker price isn't the whole comparison: the cheaper rows buy burstable shared CPUs, per-service wiring instead of a compose deploy, and managed databases billed separately. Heroku and DO App Platform have no persistent volumes at all - stateful stacks like this one need workarounds there.
#deploy it
On Miget
- Create a Compose Stack in app.miget.com pointing at the templates repository
- Set the stack path to
flowise -
Set the required variables:
JWT_AUTH_TOKEN_SECRET / JWT_REFRESH_TOKEN_SECRET / EXPRESS_SESSION_SECRET, session signing (openssl rand -hex 32 each)FLOWISE_SECRETKEY_OVERWRITE, fixed credential-encryption key (openssl rand -hex 24)APP_URL, set to the app’s https domain after first deploy
- Deploy. Miget layers
compose.miget.yaml(RAM, privacy, volumes, managed services) automatically
Locally first?
Every template is portable, vanilla Docker Compose - the Miget overrides are ignored locally:
git clone https://github.com/deployable-sh/stacks
cd miget-compose-templates/flowise
docker compose up -dSame files, same behavior. The template README covers connection strings and scaling notes.
#faq
How does this compare to Flowise Cloud and Dify Cloud?
Flowise Cloud Starter is $35/month capped at 10,000 predictions; Dify Cloud Professional is $59/month for 5,000 message credits. Self-hosted Flowise is $13/month with no prediction meter - the flows run against your own provider keys.
Where did the FLOWISE_USERNAME/PASSWORD login go?
Flowise v3 replaced the shared-credential envs with real accounts: visit the app once after deploy and create the admin user in the setup screen. The env secrets in this template sign those sessions; they are not a login.
Flowise or n8n for automation?
n8n (also in this catalogue, in queue mode) is general workflow automation with LLM nodes; Flowise is purpose-built for conversational AI - chat memory, RAG patterns, agent tools, and an embeddable widget. Many teams run both for different jobs.
How do I embed a bot on my site?
Each flow exposes an API endpoint and an embed snippet (script tag or React component) pointing at your domain. Keep the flow’s API key server-side for private flows; public chat widgets use the public endpoint with rate limits.
Ship Flowise today
One compose stack, 2 GiB of RAM, from $13/month flat, and it runs on your laptop with the same files.