AI Model Marketplace: How to Rapidly Build Consumer AI Applications Using Eachlabs🔥🤖

In recent years, consumer AI applications have grown massively. From AI voice assistants to mobile apps that generate music, design interiors, or create short-form content in seconds. The market is growing fast, with consumer AI projected to reach $1.3 trillion by 2032, according to Bloomberg Intelligence.

But for developers, building these kinds of apps isn’t always easy and straightforward. Combining a number of AI models like transcription, summarization, and speech synthesis usually means working with multiple APIs, authenticating, dealing with latency and keeping costs under control. That’s where the real issue lies. Developers need to move fast, but the complexity of stitching models together slows everything down.

Eachlabs aims to fix that. It provides an all-in-one platform with a plug-and-play AI Model Marketplace and a visual workflow builder that makes it easy to prototype and launch AI-powered apps, without worrying about backend integrations. Whether you're working on a solo mobile app or building a production-ready tool, Eachlabs gives you a faster way to test, iterate, and deploy with minimal setup.

This article will show how to build an AI-powered video summarizer using Eachlabs, while also comparing AI models in Eachlabs and sharing tips for optimizing workflows.

Understanding the AI Model Marketplace in Eachlabs

Eachlabs' AI Model Marketplace contains a high-quality collection of production-ready AI models from leading providers, presented in a way that's easy for developers to consume and make use of without setup hassles. Instead of working with different APIs, authentication tokens or SDKs, developers get to plug models into a workflow in just a few clicks.

Eachlabs organizes its models into key categories:

1. Text Models: They are used for tasks like summarization, content generation, and chat-based interactions. Eachlabs offers models such as GPT (OpenAI), and Minimax, which makes it easy to plug in powerful natural language processing tools.
2. Voice Models: They are ideal for text-to-speech applications, voice assistants, and audio responses. Examples include ElevenLabs and Minimax which provides high-quality voice synthesis with natural intonation, allowing developers to build voice-enabled AI experiences without manual audio processing.
3. Video Generation Models: These are used to create videos from text or reference inputs, streamlining content creation. Runway AI enables text-to-video and video editing with ease, while Hailuo offers realistic, stylized video generation ideal for creative and commercial use.

Eachlabs removes the typical integration headaches so that developers spend less time working with API setups and more time building and testing. This allows for a faster path from idea to deployment, with the added flexibility to mix and match models within a single visual workflow.

Comparing AI Models in Eachlabs

When building AI-powered applications, it's crucial to choose the right model. Eachlabs offers a range of integrated AI models. How do you decide which one best matches your needs? A simple framework can help: Cost, Latency, and Output Quality.

• Cost: Refers to how much it takes to run the model per request. For instance, using a high-end model like GPT-4 can be powerful but expensive for large-scale applications.
• Latency: Talks about the response time. If your app needs real-time output, say a voice assistant, models with lower latency become a priority.
• Output Quality: Captures the accuracy, fluency, or naturalness of the output. It can vary widely depending on the task, whether it is text generation, audio, or image synthesis.

The table below compares popular AI models in Eachlabs across various categories:

Text Generation

Voice Synthesis Models

Image Generation Models

Video Generation Models

Speech-to-Text (Transcription)

The choice of the right AI model hinges on cost, speed, and output quality trade-offs. Eachlabs simplifies the choice by providing access to various models in one simplified interface.

With the right model in hand, developers can now turn to Eachlabs’ Visual Workflow Builder to bring their ideas to life with no complex infrastructure required.

Building with the Workflow Builder

AI workflows are no longer just for machine learning engineers. With Eachlabs' drag-and-drop Workflow Builder, anyone, from solo developers to product teams, can design, iterate, and deploy AI-powered features without writing orchestration code. It’s a visual canvas for chaining together AI models, logic blocks, inputs, and outputs.

Think of it as a low-code backend for your AI ideas. You don’t need to manage API keys, handle payloads, or worry about format mismatches between models. Instead, you:

• Drag in pre-configured model blocks (like GPT or Whisper)
• Connect them with logic, memory, or conditionals
• Run workflows via an API endpoint or webhook

You can also mix and match models across various modalities such as text, audio, videos etc, and add branching paths based on values (like video length or confidence score). AI models such as ByteDance's models, DeepSeek AI's Janus, Kuaishou AI's Kling, etc, enable the design of complex AI workflows that respond intelligently to diverse inputs. Workflows can be triggered from a web app, uploaded files, or external events like a Notion update or Dropbox sync.

To illustrate how it works in reality, let's go through creating a basic but powerful AI Video Summarizer app. This app accepts a video file or URL, transcribes the content, summarizes it with GPT, and then converts the summary into audio using ElevenLabs.

Building an AI Video Summarizer

Before building your AI Video Summarizer, the first step is to log in to your Eachlabs account. If you don’t already have one, you can easily sign up at Eachlabs.

Once you log in, you’ll land on the Eachlabs dashboard, which serves as the central hub for exploring AI models, tracking workflow activity, and managing projects.

To begin building, locate the "My Workflows" tab in the left-hand sidebar. Clicking this takes you to the Workflow Builder. From here, click the "Build your own flow" button at the top right to spin up a new project. Give your workflow a name like "AI Video Summarizer" and optionally add a description to keep things organized.

This action opens a blank canvas, where AI model blocks can be added, configured, and connected to form a complete end-to-end workflow.

To ease model selection, Eachlabs provides a Finder AI feature. Instead of manually browsing through dozens of models, users can simply type a description of what they want to build, for instance, typing a prompt “AI Video Summarizer”, the Finder AI functionality will recommend the best models to get started. This is especially useful for those unfamiliar with the entire model library or who want to skip the trial-and-error phase.

Moving on, the workflow starts with the Input block, where users upload a video file or provide a URL. This block is fully customizable.

Click on the input block to modify the following parameters:

• Variable Name: Set to something like video_file
• Input Type: Choose either File or Text (for URLs). You can even select Video directly.
• Access Multiple Files: Optional, based on use case
• Required: Enable this so as to ensure no empty inputs

This creates a clear entry point for users to provide content for processing.

Once the input block is created, it gives room for the video to be uploaded, then a Whisper model block is added to convert audio into text. To do this:

1. Click the Input block’s connector, then hit the plus (+) sign to add a new node.
2. Search for and add the “Whisper Speech-to-Text” model. The connection should look like this:

Click on the Whisper model block to edit the following parameters:

• Audio: Click on this parameter and select the output name stored while creating your inputs (it is always related to the variable name in the input block)
• Language: Optional, but setting "en" ensures better accuracy for English videos. This parameter is found once you click on “Advanced Controls”
• Timestamp: ON to retain the location of sentences in the audio
• Batch Size: Keep as default unless processing long videos

Your adjusted parameters in the Whisper model block should look like this:

What this block does is that it processes the input and generates a full transcript from the video.

Next, connect a ChatGPT block to the output of Whisper:

• Click on the Whisper block, then add a new node
• Select a ChatGPT model (e.g.GPT-4)

Edit the following parameters:

• Model Name: gpt-4o
• System Prompt: Make use of this prompt: “You are a helpful assistant”.
• User Prompt: Write the prompt: "Summarize the following transcript in a concise and engaging way:". Also include the output from the Whisper model block.
• Max Tokens: This depends on the amount of texts to be generated. “512” can be used for this demo.

Also, click on “Advanced Controls” to adjust the parameter:

• Message Array:

[
{
"role": "system",
"content": "You are an assistant that summarizes transcripts accurately and clearly."
},
{
"role": "user",
"content": "Summarize this transcript: {{step1.output}}"
}
]

This instructs GPT to produce a summary from the transcribed content.

Moving on, from the GPT block, add an ElevenLabs Text-to-Speech model.

Configure the block as follows:

• Text: Replace with your actual GPT block name
• Voice: Choose depending on tone preference
• Stability / Similarity Boost: Optional for tone control

Go to the “Advanced Controls” and edit the parameter:

• Speaker Boost: ON for more natural delivery

What this ElevenLabs model does is that it takes the summary and converts it into an engaging voice clip, thereby completing the process of the Video Summarizer.

Eachlabs' builder magic lies in the visual interface. You can chain models together without glue code. You can test each node individually or run the whole pipeline with one click. This is especially useful for mobile or iOS developers who are not AI experts but would like to add AI features to their apps easily.

Developers can easily integrate the AI-powered video summarization workflow into their own mobile or web applications using SDKs and API endpoints provided by Eachlabs.

Eachlabs provides ready-made SDKs for multiple platforms, including Golang, Node.js, and HTTP.

You can also trigger your AI Video Summarizer workflow using the Eachlabs client SDK

With the AI Video Summarizer workflow set up now, the next thing to attend to is ensuring it runs cost-efficiently and efficiently. Optimizing your workflow isn't just about performance, but about ensuring scalability, affordability, and flexibility as you build and iterate.

Let’s look at some key best practices to get the most out of Eachlabs workflows.

Workflow Optimization Best Practices

Below contains a few actionable strategies to optimize workflows whilst using the AI Video Summarizer as a reference:

1. Choose Token Efficient Models: For tasks like summarization, consider using lighter models such as GPT-3.5 instead of GPT-4. This brings about faster responses and significantly lower inference costs with little compromise on quality, especially for straightforward summaries.
2. Limit Input Sizes: Large video files can lead to lengthy transcription times and higher costs. To manage this:
3. Place limits on the duration of videos.
4. Break long videos into shorter pieces before transcription and summarization.
5. Use Conditional Logic: You can add conditions in your workflow to initiate summarization only if a transcription exceeds a certain word count. This avoids making unnecessary calls to the language model, saving resources for when they're really needed.
6. Make Use of the Built-in Debugger: As you build the AI Video Summarizer, Eachlabs' debugger stays invaluable. It lets you test out each block, from Whisper to GPT to ElevenLabs, and maintain inputs and outputs organized before you go live. This keeps slow or costly blocks from progressing too far.

These best practices do not only keep the AI Video Summarizer efficient but also give you a general template for optimizing any AI-powered workflow you build on Eachlabs.

Final Thoughts

Eachlabs is changing how developers prototype and deploy AI apps, uniting speed, simplicity, and flexibility in one low-code platform. Whether you're building a voice assistant, video summarizer, or multimodal chatbot, the drag-and-drop workflow builder and integrated model marketplace make it easy to go from idea to reality without wrestling with weighty APIs or infrastructure.

Ready to build your own AI tool? Visit Eachlabs, explore Eachlabs docs for more information.

The best way to learn is by building, and Eachlabs gives you everything you need to start today.

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