One governed embeddings endpoint, any model or provider.
Route embedding requests to OpenAI, Cohere, or self-hosted models like BGE-M3 and E5 through a single OpenAI-compatible endpoint. Swap embedding models without code changes, track spend per team, and power your RAG pipelines reliably.
Embedding model migrations require full reindex and code changes.
Hard-coding embedding provider calls into application services means that switching models requires coordinated deploys, reindexing pipelines, and schema migrations. Teams avoid upgrades even when better models are available. Embedding spend is invisible across teams, making cost attribution impossible.
Model swaps with zero application changes and full cost visibility.
ManyLayers abstracts the embedding provider behind a stable alias. Update the routing config, trigger a background reindex, and the new model is live — no application deploys, no API surface changes. Per-team budgets give full cost attribution across batch indexing and real-time query workloads.
A flexible, governed embeddings layer for every workload.
Unified Embeddings Endpoint
A single OpenAI-compatible /v1/embeddings endpoint routes to any configured provider or self-hosted model. Applications call one URL regardless of whether the underlying model is text-embedding-3, BGE-M3, or E5-large.
Provider & Model Flexibility
Supported backends include OpenAI, Cohere, Vertex AI, and self-hosted models including BGE-M3, E5-large, and any HuggingFace-compatible checkpoint deployed on your infrastructure.
Model Swap Without Downtime
Change the embedding model behind an alias without touching application code or re-deploying services. Trigger a background reindex of affected knowledge bases and the swap completes transparently.
Powers RAG Pipelines
Embeddings generated through ManyLayers feed directly into the Workspace knowledge bases and vector indexes. The same governed endpoint serves both indexing jobs and live retrieval queries.
Spend Tracking per Team
Embedding jobs can be large — millions of vectors for document reindexing. Per-team budgets and rate limits apply to embedding traffic, giving cost visibility for batch and real-time workloads separately.
Semantic Cache Integration
Semantically similar embedding requests are deduplicated against the cache before hitting any provider. Reduces redundant computation on high-volume indexing workloads and repeat query patterns.
From embedding call to vector output.
BGE-M3
Self-hosted model example
Zero
Application changes to swap models
100%
Embedding spend tracked per team
“We upgraded from text-embedding-ada-002 to our own fine-tuned BGE-M3 model with no application changes whatsoever. The background reindex completed overnight and retrieval quality improved measurably the next morning.”
Staff Machine Learning Engineer — Enterprise Search Platform