StackAI vs. Langflow

To win against competitors, many enterprise companies are now turning to AI agents. With the ability to perform critical jobs, AI agents are making teams more efficient and filling the worker gap for enterprise companies. 

However, there are several different solutions for building AI agents. Many of them require technical knowledge, including a background in coding. But some no-code solutions have recently emerged. These give non-technical users the tools they need to build AI agents. 

Two current AI builder solutions include Langflow and Stack AI. Both offer the ability to create AI agents. But they are targeted to different audiences.

Langflow is a low-code AI platform for developers. The Langflow platform looks accessible due to its visual interface. But Langflow requires deep technical knowledge to build basic AI agents. The tool is suited for developers who want to save time. 

Stack AI, on the other hand, is a no-code AI platform that empowers non-technical enterprise teams to build AI agents. These AI agents perform business use cases, such as loan underwriting, IT support, and competitive analysis. Stack AI is ideal for users who don’t know how to code but want to leverage the power of AI agents. 

Is Langflow or Stack AI the right solution for your team? Read the following comparison blog to understand the differences. 

Side-by-Side Comparison: Stack AI vs. Langflow

User Experience

Langflow is designed for developers

A Langflow project for sending a prompt to an OpenAI model.

At first glance, Langflow looks no-code. The visual interface seems intuitive, with the list of nodes on the left-side menu that you can drag to and connect on the canvas. There are multiple options, from LLMs, logic gates, to data processing. This shows the platform’s versatility.

The issues begin when you inspect each node. Instead of being a plug-and-play, they need a lot of extra configuration before they’re ready to work: LLMs don’t run without external integrations, data sources need to be connected via API, and data transformation requires technical knowledge to implement.

Compared with fully coding a solution, Langflow it’s a great speed boost. But compared with a user just starting out building AI workflows, it’s too complex and difficult to adopt. This positions Langflow more as a developer platform, not fully suited for non-technical users. 

Stack AI is designed for non-technical users

A starter Stack AI project for chatting with an OpenAI model.

Stack AI looks no-code and is no-code. The visual interface follows a similar logic to that of Langflow. You can browse LLMs, data sources, and logic nodes on the left side of the screen. Drag, drop, and connect them on the canvas to build your workflow.

The major difference is that all nodes require little configuration. LLMs are natively integrated into the platform: you can use them immediately without API keys. 

Some data sources require connecting with external systems. Still, the integration process is similar to other SaaS platforms you use: click to connect, allow Stack AI as a connection in the data source provider, and your data is ready to use.

Despite being no-code, Stack AI is a valuable tool for developers. It speeds up the development process and involves more teams in the building process, letting IT teams focus on data administration and optimization. And, if you need to extend any part of the project with code, you can use Python nodes for advanced logic, and configure manual integrations for legacy systems.

AI integrations

Langflow requires API keys to integrate with AI models

Langflow requires you to paste the API keys onto the platform before activating functionality.

When using an LLM node in Langflow, you’ll notice you need an API key. This can increase operational complexity and bring unpredictable costs.

First, you have to create a developer account with every AI model provider you want to use to get an API key. Then, you set up billing and security features. Finally, you generate the key, paste it into Langflow, and it’s ready for action.

But, if you want to leverage OpenAI’s o3 models for their advanced reasoning, Claude’s Sonnet 3.5 for better writing abilities, and Gemini 2.0 Pro for its massive context window, you’re suddenly managing three external platforms. This also makes it harder to keep track of token costs, as there’s no unified dashboard.

Managing API keys requires care: if leaked, they can become a security risk. Whoever has them can bill AI work to your account or start actions in your internal systems if you’re using the OpenAI Assistants API, for example. For larger companies with multiple teams and developers, this could also lead to many different keys being generated, increasing management complexity on top of the security risks.

Lastly, each API has unique technical specifications and rate limits, which depend on how they’re developed and pricing rules. Familiarizing your developer teams with multiple API specs adds one more layer of complexity, leading to more time invested in preparation, testing, and troubleshooting.

Stack AI offers access to all leading AI models, no keys required

Stack AI gives access to all leading providers, with dropdowns available to choose your preferred AI model.

In Stack AI, all leading AI models are ready to use as soon as you create a new project. You don’t need to register with third parties. All you have to do is:

• choose the LLM, 

• drag it onto the canvas, 

• connect it to inputs and outputs, 

• and start using it. 

The platform handles all the integration complexity behind the scenes. This speeds up implementation, reduces operational complexity, and increases security for large teams of developers.

More than that, since Stack AI has thousands of users, it’s in a good position to negotiate advantageous contracts with leading AI providers. This means it has easier access to compute with OpenAI and Anthropic, so you don’t have to progress through tiers to get the best availability, along with data protection addendums (DPAs) that delete your data from their servers right after processing.

Stack AI is always adding new models as they’re released. This makes it easy to upgrade a project with the latest AI technology. You can do so by simply clicking the LLM’s settings icon in a Stack AI project and choosing the provider and model combo you want to upgrade to.

Data integrations

Langflow relies on API-level integrations, making it hard to connect new sources

You need to use the API Request node in Langflow to connect to most of your tech stack.

Continuing the trend of being a low-code tool, Langflow relies on API connections to integrate with your data sources. This requires technical knowledge to do. It also means you’ll have to generate keys in your platforms—and keep them safe, similar to the LLM keys we referenced above. Beyond that, you’ll have to expose the API endpoints, make sure they’re secure, and then set up the calls to pull the data into Langflow.

Stack AI directly connects with the most popular storage services in easy-to-use flows

Stack AI follows the user flow of traditional SaaS apps: pick the platform you want to connect and a popup appears. Log into the external platform with your credentials and confirm the connection to Stack AI there. Once you close the popup, test the connection. That’s it: you can drop the corresponding data source node into any project and start using it right away.

You can connect to the most popular enterprise data sources, including Microsoft Sharepoint, Amazon S3, Google Drive, Dropbox, HubSpot, and many more.

Retrieval-augmented generation (RAG)

Langflow requires familiarity with the concept of RAG

A Langflow template for a RAG project.

RAG is one of the most popular features in every AI building platform. While most make it seem easy to set up, Langflow exposes the complexity behind this framework. It will require you to:

1. Upload your documents and set chunking settings, determining the size of each text segment

2. Choose and set up an embeddings model, which will convert the chunks into vector representations, storing them in a vector database for efficient similarity search

3. Include the vector search when sending a prompt to an LLM, so it can surface the relevant data to ground the answer in your data

You have to connect and configure all the required nodes—up to 12—and run tests to ensure it’s working properly.

Stack AI handles RAG natively, making it easier to set up

A Stack AI template for a RAG project using uploaded documents.

Stack AI handles RAG natively under the hood. Whenever you connect a data source or upload a document, it indexes the data automatically, having it ready to use in less than a minute in a dedicated node. 

Once you connect it to an LLM node and pass its variable, the platform will run a vector search for semantic similarity, passing the results to generate the response. The result is a contextually relevant answer based on your data.

You only need up to 4 nodes to set up this functionality in Stack AI, instead of Langflow’s 12.

Sharing interfaces

Langflow only has a test interface

Langflow’s Playground, meant for testing projects only.

When building your project, you can click the Playground button to reveal a chat interface. This will let you test your work as you move forward. But this isn’t a shareable interface: you’ll have to build a frontend yourself, host it, and share it with your team. This adds another layer of work on top of building the workflow.

Stack AI has live shareable interfaces

Stack AI also has tools to test and debug your project as you move forward, allowing you to enter data into inputs on the project canvas and see outputs for that request. 

More than that, you can export your project as an interface: you’ll get a live link you can share with your team. You don’t need to build the interface yourself. As you iterate, you can see analytics and usage history in the platform, helping you make adjustments and upgrades over time.

Privacy and security

Langflow doesn’t have SOC 2 or HIPAA compliance

The only HIPAA-compliant in Langflow is the hyper-converged database, a data storage solution you can use in your projects. Beyond that, the platform doesn’t offer any compliance natively. Additionally, it doesn’t have easy-to-implement guardrails or PII protection, requiring more hours to secure your projects.

Stack AI is SOC 2 and HIPAA compliant, offering guardrails and PII protection

The entire Stack AI platform is SOC 2 and HIPAA-compliant. But that’s not the end of privacy and security in the platform:

• Data Protection Addendums (DPAs) in place with OpenAI and Anthropic: your data is deleted from their servers once your request is processed. 

• The Azure AI node is a containerized deployment of OpenAI models with HIPAA compliance. 

• Role-based access control (RBAC) to knowledge bases within the platform, making sure that users can only see data within their privilege level.

• Guardrails are available to block potentially harmful prompts and responses, as well as PII protection to remove that data before sending it for processing.

Pricing

Langflow is free, but the APIs are not

It’s free to build and run your projects on Langflow. However, as we’ve explained above, extra costs are not included, such as LLM APIs.

Stack AI is paid, but includes AI model tokens

Stack AI has a free plan and two paid plans. The AI model tokens are included in all plans, so you can bring large documents without extra costs.

Support and growth

Langflow is a developer tool only

Langflow is a developer tool backed by Datastax. However, it doesn’t offer native support, opting for a Discord community where users can collaborate. If anything breaks in your project, you’ll have to use the documentation, your troubleshooting skills, and the community’s goodwill.

Stack AI helps you execute your strategy as a partner

Once you subscribe to the Enterprise plan, you’ll have access to more than all the features on the Stack AI platform. You’ll also engage with the success team, who will identify use cases you can automate, measuring the ROI in the process. This positions Stack AI as a generative AI strategy partner, not just a tool to build your workflows.

Langflow vs Stack AI: which one is the best?

Langflow is a low-code tool for developers looking to build generative AI applications with flexibility and customization. While it provides an intuitive visual interface, it still requires a solid understanding of AI frameworks, RAG systems, and API integrations. 

This makes it a great fit for technically skilled users but a challenging option for those without a coding background. Additionally, Langflow’s reliance on external APIs for AI models and data integrations introduces additional setup complexity and potential cost unpredictability. 

Stack AI is designed to make AI development accessible to everyone, regardless of technical expertise. With a true no-code interface, seamless AI model integration, and enterprise-grade security features, Stack AI simplifies the process of building, deploying, and scaling AI-powered workflows. 

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