FastEmbed

Overview

FastEmbed is a lightweight, fast library for embedding generation built and maintained by Qdrant. It uses ONNX Runtime instead of PyTorch, making it ideal for CPU-only environments and serverless deployments.

Key Features

Lightweight Architecture

• Minimal external dependencies
• No GPU required
• Doesn't download GBs of PyTorch dependencies
• Uses ONNX Runtime for efficient inference
• Perfect for serverless runtimes (AWS Lambda, etc.)

Performance

• 12x inference speedup on CPUs via ONNX optimization
• Faster than PyTorch-based implementations
• Quantized models for CPU (and Mac Metal)
• Optimized for edge computing
• Best compute efficiency

Accuracy

• Better than OpenAI Ada-002
• Default model: Flag Embedding (MTEB leaderboard leader)
• State-of-the-art results on benchmarks
• Multiple model options available

Supported Embeddings

1. Text Embeddings: Traditional text-to-vector embeddings
2. Image Embeddings: Visual similarity search
3. Sparse Embeddings: SPLADE-based sparse vectors
4. Reranking: Cross-encoder models for reranking

Multi-Language Support

Available in:

• Python: pip install fastembed
• Rust: Available as crate on crates.io
• Go: Native Go implementation
• JavaScript: Node.js support

Use Cases

Serverless Deployments

• AWS Lambda functions
• Google Cloud Functions
• Azure Functions
• Edge runtime compatibility

Edge Computing

• On-device inference
• IoT applications
• Mobile deployments
• Q1 2026 target: 1M device deployments

Resource-Constrained Environments

• CPU-only servers
• Development laptops
• CI/CD pipelines
• Cost-optimized cloud instances

Integration

Qdrant Integration

Native integration with Qdrant vector database:

from fastembed import TextEmbedding
from qdrant_client import QdrantClient

embedding = TextEmbedding()
client = QdrantClient(":memory:")
vectors = list(embedding.embed(["Hello world"]))

Framework Support

• Haystack integration
• LangChain compatibility
• Direct API usage

Model Selection

Default Model

• Flag Embedding (BAAI)
• MTEB leaderboard leader
• Good balance of speed and accuracy

Available Models

• Various sizes (small, base, large)
• Multilingual options
• Specialized domain models
• Custom model support

Technical Advantages

ONNX Runtime Benefits

• Cross-platform compatibility
• Hardware acceleration (CPU, GPU, Mac Metal)
• Smaller memory footprint
• Faster inference
• Production-ready

Quantization Support

• INT8 quantization for CPU
• Metal quantization for Mac
• Minimal accuracy loss
• Significant speed improvements

Deployment Scenarios

Cloud-Native

• Kubernetes deployments
• Container-friendly
• Horizontal scaling
• Resource-efficient

Hybrid and Edge

• On-premise deployments
• Edge data centers
• Fog computing nodes
• IoT gateways

Performance Benchmarks

CPU Inference

• 12x faster than PyTorch equivalent
• Sub-millisecond latency for small texts
• Efficient batch processing
• Linear scaling with text length

Memory Efficiency

• Low RAM requirements
• Small model files
• Efficient caching
• Minimal overhead

Production Readiness

Battle-Tested

• Used in Qdrant Cloud
• Powers production workloads
• Regular updates and maintenance
• Active community support

Enterprise Features

• Stable API
• Comprehensive documentation
• Type hints and IDE support
• Unit and integration tests

Recent Developments (2025-2026)

• November 2025: ONNX optimization analysis
• Q1 2026: Target deployment on 1M devices
• Ongoing model additions
• Performance improvements

Comparison to Alternatives

vs. PyTorch-based Libraries

• 12x faster on CPU
• Smaller dependencies
• Lower memory usage
• No GPU required

vs. API-based Solutions

• No API latency
• No usage costs
• Data privacy (local inference)
• Offline capability

Getting Started

# Install
pip install fastembed

# Basic usage
from fastembed import TextEmbedding

model = TextEmbedding()
embeddings = list(model.embed(["Hello world"]))

Pricing

Completely free and open-source. No API costs, no usage limits.