AI & GPU

S4 Embed

Vector-search FinOps gateway

Up to 32× smaller ANN RAM AWS service it replaces: Vector-search RAM / instance cost

A FinOps layer that makes your existing vector database cheaper while keeping recall on target. It quantizes embeddings (binary + int8) and runs a two-stage search (1-bit Hamming coarse stage + exact rescore) in front of OpenSearch, pgvector, Qdrant, or Milvus, shrinking the in-RAM ANN graph by up to 32×. Runs as an Amazon Linux 2023 AMI in your own VPC, billed per unit of usage (texts embedded, documents indexed, searches served).

S4 Embed helps you find and run a low-cost vector-search configuration that meets your recall target. On a 30k-vector benchmark, binary Hamming + float rescore reached recall@10 of 0.976–1.000 depending on store and over-fetch — you choose the operating point. The FinOps tools are the product: s4embed prove (estimate the recall/cost/latency frontier), compare (measure live recall across stores), tune (emit a deployable config that meets a recall + latency + RAM budget), a gateway shadow mode (dual-write and shadow-compare before cutover), and drift (watch embedding drift). Store-agnostic across OpenSearch, pgvector, Qdrant, and Milvus.

The problem

Vector search keeps its ANN graph in RAM, so as your corpus grows the memory footprint — and the cost — of your vector database grows with it, and that spend is hard to predict. You don't want to trade away recall to save money, which leaves you stuck choosing between cost and quality. And there is rarely a good way to find out which store and which configuration across OpenSearch, pgvector, Qdrant, or Milvus is most cost-efficient before you commit it to production.

How it works

1
Quantize the embeddings

S4 Embed quantizes your embeddings to binary (an up-to-32x smaller in-RAM ANN graph) and an int8 residual (about 4x smaller on-disk vectors), shrinking the RAM your vector database has to hold.
2
Two-stage search to hold recall

A 1-bit Hamming coarse stage builds a short shortlist, then an exact rescore reorders it. By tuning the over-fetch and rescore operating point you hold your recall target while keeping RAM down.
3
Validate with data before cutover

s4embed prove, compare, and tune measure the recall/cost/latency frontier on your own vectors and emit a deployable config. The gateway shadow mode dual-writes and shadow-compares live reads so you can watch the compressed path reproduce your primary before you cut over.

Highlights

Binary quantization shrinks the in-RAM ANN graph up to 32×; recall@10 of 0.976–1.000 on a 30k-vector benchmark (per store / over-fetch) — pick the operating point for your recall target.

Store-agnostic (OpenSearch / pgvector / Qdrant / Milvus); shadow mode validates the compressed path against your primary before any cutover.

FinOps CLI — prove / compare / tune / drift — measures cost, recall, and latency and emits a deployable config.

What's included

Amazon Linux 2023 AMI (x86_64) — a vector-search FinOps gateway that runs behind your own load balancer, inside your own VPC
Binary + int8 quantization with a two-stage search pipeline (1-bit Hamming coarse stage plus exact rescore), cutting the in-RAM ANN graph by up to 32x
A store-agnostic pipeline across OpenSearch, pgvector, Qdrant, and Milvus, with no lock-in
A FinOps CLI — s4embed prove (estimate the recall/cost/latency frontier), compare (measure live ANN recall across the stores), tune (emit a config meeting a recall + latency + RAM budget), and drift (watch embedding drift and recall and recommend re-tuning)
A gateway shadow mode that dual-writes and shadow-compares live reads, so you can confirm the compressed path reproduces your primary before any cutover
A CloudFormation quick-start that provisions the OpenSearch and pgvector paths (Qdrant and Milvus connect by pointing the gateway at your existing endpoint)
Operational features — API-key auth (when configured), request-size and concurrency caps, a readiness probe that fails closed on billing or store problems, Prometheus metrics, and usage-metered billing (per text embedded, document indexed, and search served)

Use cases

Large-scale search and RAG workloads where vector-database RAM cost grows with the corpus

Teams that want to lower vector-search cost while holding their recall target

Measuring which store and config across OpenSearch, pgvector, Qdrant, and Milvus is most cost-efficient before cutting over to production

Running vector search on usage-metered billing while keeping your data and vector database inside your own account

FAQ

Doesn't compression hurt recall?

You choose the operating point. On a 30k-vector benchmark, binary Hamming + float rescore reached recall@10 of 0.976 to 1.000 (OpenSearch 0.995, pgvector 0.996, Qdrant 1.000, Milvus 0.976) — all at the 32x RAM reduction. Recall rises with over-fetch, so you tune the operating point to your recall target. Recall scales with over-fetch, and the best point is chosen per workload.

How much RAM does it save?

Binary quantization makes the in-RAM ANN graph up to 32x smaller, and the int8 residual makes on-disk vectors about 4x smaller. The exact reduction depends on your corpus, store, and chosen operating point, so the reliable way to size it is to estimate from your own data with s4embed prove and tune.

Which vector stores are supported?

The pipeline is store-agnostic across OpenSearch, pgvector, Qdrant, and Milvus. The OpenSearch and pgvector paths can be provisioned with the bundled CloudFormation quick-start, and Qdrant and Milvus work by pointing the gateway at your existing endpoint. s4embed compare lets you measure live ANN recall across the stores.

Can I validate before cutting over to production?

Yes. s4embed prove and tune estimate a config on your own vectors, and compare measures live ANN recall across the stores. The gateway shadow mode dual-writes and shadow-compares live reads so you can confirm the compressed path reproduces your primary before cutover, and s4embed drift then watches embedding drift and recall and recommends re-tuning.

Where does my data live, and how is it billed?

S4 Embed runs as a standard Amazon Linux 2023 AMI behind your own load balancer, inside your own VPC. Your data and your vector database never leave your account, and there is no lock-in. Billing is usage-metered through your AWS bill — per text embedded, document indexed, and search served — reported hourly via the AWS Marketplace Metering Service.