About Amde

Amde is an embeddings-as-a-service platform that differentiates itself by being lightweight, scalable, and instantly accessible. While there are many ways to generate vector embeddings today, they often force you to choose between managing heavy local libraries, renting expensive GPU compute, or dealing with inconsistent model pipelines. Amde aims to solve all these.

In the world of AI infrastructure, we are not trying to claim that Amde is the only way to get a vector. But when we set out to create Amde, we felt that most solutions placed a heavy burden on the developer. You either had to be a machine learning engineer managing PyTorch dependencies, or a DevOps engineer managing GPU clusters. We wanted to create a utility that was invisible, reliable, and effortless.

Lightweight

The big picture of "lightweight" is that Amde is designed to strip away the complexity of local inference.

Importantly, Amde decouples the generation of embeddings from the environment you are developing in. The traditional way requires installing heavy libraries like transformers or sentence-transformers, which can bloat your project size and dependency tree. With Amde, the interface is a simple SDK. Whether you're using Python or Node.js, you aren't installing a neural network on your machine—you are installing a lightweight client that communicates with our optimized inference servers.

Part of this is removing the hardware barrier. Local embedding generation on large datasets is slow and resource-heavy without a GPU. Amde allows you to build advanced RAG (Retrieval-Augmented Generation) or semantic search applications on a standard laptop, shifting the compute load entirely to our cloud.

Standardized

Amde tries to provide a standardized pipeline for meaning.

Embeddings are the numerical representations of meaning—they allow computers to understand that "dog" is closer to "puppy" than "car." However, different models (like BERT, MiniLM, or OpenAI Ada) often require different input formatting and tokenization pipelines. Amde standardizes this by offering a unified API endpoint for various models. This lets you switch between a speed-optimized model like all-MiniLM-L6-v2 and a precision-optimized model without rewriting your entire ingestion pipeline.

This standardization opens up capabilities that were previously difficult to implement, such as semantic search, recommendation engines, and clustering, without needing deep expertise in the underlying model architectures.

Scalable

Amde aims to be scalable from day one.

"Scalability" is a loaded term, but in the context of embeddings, it refers to the ability to move from a prototype to a production dataset without refactoring. Storing and generating embeddings locally is fine for a hundred documents, but it becomes a bottleneck when processing millions.

In the future, I plan to provide detailed benchmarks on our inference speeds. For now, We will just say that our backend is engineered to ensure consistency and speed, regardless of whether you are on the Free Tier or a paid subscription. We handle the GPU provisioning so you don't have to.

Architecture

Amde also differentiates itself with its client-server architecture.

The workflow is designed to be "plug-and-play." The developer generates an API key, installs the SDK, and makes a request. The request hits our backend API, which verifies the plan and routes the text to cloud GPUs. A vector is returned to the developer, ready for insertion into a vector database. This architecture allows for a clean separation between your application logic and the heavy lifting of AI inference.

Here is a simple example of how this looks in sample.py. Note how the complexity of the model is abstracted away behind a single method call:

# Install: pip install amde
from amde import Amde

# Initialize client
client = Amde(api_key="api_key") 

# Generate embeddings
resp = client.embed(
    model="sentence-transformers/all-MiniLM-L6-v2",
    input_data="Your text data here")

# Use embeddings
print(resp.embedding)

Note

As of the initial launch, Amde supports select open-source models optimized for speed and accuracy. We are actively working on adding support for custom fine-tuned models and larger context windows in future updates.