Flowise vs BigML
Side-by-side comparison of features, pricing, ratings, and alternatives.
Flowise is an open-source visual builder for LLM applications and AI agents. Its drag-and-drop canvas composes chatflows and agentflows from components such as models, prompts, vector stores, tools, and memory, which can then be exposed through an API, an embeddable chat widget, or the built-in chat UI. Flowise orchestrates existing models rather than training them.
BigML is a cloud-based platform for building, training, and deploying machine learning models. It provides a simple and intuitive interface for data scientists and developers to create and deploy machine learning models at scale.
Self-hosting the open-source project is free. Flowise Cloud is tiered: Free $0/month (2 flows & assistants, 100 predictions/month, 5MB storage), Starter $35/month (unlimited flows, 10,000 predictions/month, 1GB storage), and Pro $65/month (50,000 predictions/month, 10GB storage, unlimited workspaces, +$15/user/month beyond 5 users).
- Easy to use
- No coding required
- Fast development and deployment
- Collaboration features
- Easy to use and intuitive interface
- Scalable and flexible architecture
- Collaborative features for team-based workflows
- Automated machine learning workflows
- Behaviour beyond the built-in nodes means writing your own integrations in the components package
- Dependent on visual interface
- Limited support for complex AI models
- Steep learning curve for advanced features
- Limited support for certain types of machine learning algorithms
- Can be expensive for large-scale deployments
- Limited customization options for the user interface
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The Verdict
AI-generated from listing dataFlowise is the low‑cost, open‑source visual AI‑agent builder ideal for developers who want self‑hosting and drag‑and‑drop flows, while BigML is a paid, cloud‑only platform focused on traditional machine‑learning model training and deployment.
Key differences
- •Flowise is open source and can be self‑hosted; BigML is a proprietary SaaS only.
- •Flowise targets LLM chatflows and agent pipelines; BigML focuses on classic ML algorithms and predictive analytics.
- •Pricing model: Flowise offers a free tier and cheap cloud plans; BigML requires a paid subscription with potentially higher cost at scale.
- •Flowise’s collaboration is via Discord/GitHub; BigML provides built‑in real‑time shared workspaces and live‑chat support.
- •Integration scope: Flowise connects LLMs, vector stores, and custom nodes; BigML integrates with AWS, Azure, GCP, Python, and R.
Pricing & value
Flowise has a free self‑hosted option and low‑cost cloud tiers; BigML only offers paid subscriptions.
Ease of use / learning curve
BigML’s UI is described as intuitive for classic ML; Flowise’s visual canvas is easy but advanced features require custom node coding.
Features & depth
Flowise supports LLM agents, memory, vector stores, and API/widget export; BigML lacks LLM‑specific capabilities.
Integrations & ecosystem
BigML lists native integrations with AWS, Azure, GCP, Python, and R; Flowise relies on community nodes and custom code for extensions.
Collaboration
BigML provides real‑time shared workspaces; Flowise collaboration is limited to Discord/GitHub discussions.
Scalability
BigML is built as a cloud SaaS with scalable infrastructure; Flowise scalability depends on user‑managed hosting.
Support
BigML offers email, live chat, and documentation; Flowise support is community‑based via Discord and GitHub.
Choose Flowise if…
Developers or AI enthusiasts needing free/self‑hosted LLM agent building with visual flow design.
Choose BigML if…
Data scientists or teams needing a managed, scalable ML platform with built‑in collaboration and enterprise support.
Common questions
Can I run Flowise on my own servers?
Yes, Flowise is open source and can be self‑hosted on AWS, Azure, GCP, Digital Ocean, etc.
Does BigML support large language models?
Not specified; BigML focuses on traditional machine‑learning algorithms.
Which tool is cheaper for a small team starting out?
Flowise offers a free tier and low‑cost cloud plans, making it cheaper than BigML’s paid subscription.