The fastest embeddings for search at scale
Rapidly process millions of data points using any embedding model.
With Baseten Embeddings Inference, we immediately saw 3x speed improvements. Doctors rely on speed when treating patients, and that improvement has been critical to our product experience. 160 millisecond latency is crazy.
With Baseten Embeddings Inference, we immediately saw 3x speed improvements. Doctors rely on speed when treating patients, and that improvement has been critical to our product experience. 160 millisecond latency is crazy.
Jagath Jai Kumar,
Full Stack Engineer
With Baseten Embeddings Inference, we immediately saw 3x speed improvements. Doctors rely on speed when treating patients, and that improvement has been critical to our product experience. 160 millisecond latency is crazy.
With Baseten Embeddings Inference, we immediately saw 3x speed improvements. Doctors rely on speed when treating patients, and that improvement has been critical to our product experience. 160 millisecond latency is crazy.
Infrastructure built for performance and flexibility
Accelerate initial queries
With optimized cold starts and elastic autoscaling, you can rapidly process entire databases, serve bursts of requests, or scale down to zero to save on costs.
Use any embedding model
Ship custom Docker images, package any AI model using our open-source Python library, Truss, or use Baseten Chains for ultra-low-latency compound AI.
Customize your inference
At Baseten, you have full control over how you balance performance, cost, and accuracy. Our engineers are obsessed with meeting or exceeding your success criteria.
Any model, any application, custom inference
Semantic search
Get ultra-low-latency, high-quality search with any model series, including BAAI General Embedding (BGE), Stella, and SFR-Embedding models.
Recommender systems
Enable real-time RecSys experiences even during peak demand, with fluid autoscaling for any dataset size or traffic level.
Custom models
Deploy any open-source, closed-source, fine-tuned, or custom embedding model tailored to your use case and performance targets, including Nomic, NV-Embed, and Voyage model series.
Models
BGE Embedding ICL
BGE Embedding ICL is an excellent all-around model for text embedding.
Mixedbread Embed Large V1
A state-of-the-art text embedding model built on Bert with under 1 billion parameters.
Nomic Embed Code
SOTA text embedding model built for code.
Powering embeddings and search at massive scale
Production-grade reliability
Reliably serve customers anywhere in the world, any time, backed by our five 9's uptime and global deployment options.
Ship low-latency pipelines
Pass embeddings to any model or processing step, each equipped with custom hardware and autoscaling using Baseten Chains.
Auto-scale to peak load
Deliver fast response times under any load with rapid cold starts and elastic autoscaling.
Embeddings on Baseten
Build with EmbeddingsBaseten cut our P95 latency by 80% across the dozens of fine-tuned embedding models that power core features in Superhuman's AI-native email app. Superhuman is all about saving time. With Baseten, we're delivering a faster product for our customers while reducing engineering time spent on infrastructure.
Baseten cut our P95 latency by 80% across the dozens of fine-tuned embedding models that power core features in Superhuman's AI-native email app. Superhuman is all about saving time. With Baseten, we're delivering a faster product for our customers while reducing engineering time spent on infrastructure.
Loïc Houssier,
CTO
Baseten cut our P95 latency by 80% across the dozens of fine-tuned embedding models that power core features in Superhuman's AI-native email app. Superhuman is all about saving time. With Baseten, we're delivering a faster product for our customers while reducing engineering time spent on infrastructure.
Baseten cut our P95 latency by 80% across the dozens of fine-tuned embedding models that power core features in Superhuman's AI-native email app. Superhuman is all about saving time. With Baseten, we're delivering a faster product for our customers while reducing engineering time spent on infrastructure.
