Sold by: abyo software
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S4 Embed is a Vector Search 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, reducing the in-RAM ANN graph by up to 32x. On a 30k-vector benchmark, binary + float rescore reached recall@10 of 0.976 to 1.000 depending on store and over-fetch (Milvus 0.976; OpenSearch, pgvector, Qdrant 0.99+); you choose the operating point to meet your recall target. Runs as an Amazon Linux 2023 AMI in your own VPC, billed per unit of usage (texts embedded, documents indexed, searches served).
Overview
S4 Embed is a Vector Search FinOps layer: it helps you find and run a low-cost vector-search configuration that meets your recall target. Quantization (binary for an up-to-32x smaller in-RAM ANN graph, int8 residual for about 4x smaller on-disk vectors) plus a two-stage search (coarse Hamming shortlist then exact rescore) reduce RAM while you tune the over-fetch/rescore operating point to hold recall. On a 30k-vector benchmark, binary Hamming + float rescore reached recall@10 of 0.987 at over-fetch 50 and 0.995-0.996 at over-fetch 100 on OpenSearch and pgvector, 1.000 on Qdrant, and 0.976 on Milvus (tune per workload) - all at the 32x RAM reduction. Recall scales with over-fetch as the corpus grows, so the operating point is chosen per workload. The pipeline is store-agnostic across OpenSearch, pgvector, Qdrant, and Milvus.
The FinOps tools are the product. s4embed prove estimates the recall/cost/latency frontier on your vectors using the in-process quantization/rescore model and returns a recommended config. s4embed compare measures live ANN recall across OpenSearch, pgvector, Qdrant, and Milvus for your corpus and queries. s4embed tune emits a deployable config meeting a declared recall + latency + RAM budget. A gateway shadow mode dual-writes and shadow-compares live reads so you can watch the compressed path reproduce your primary before cutting over; use compare plus shadow mode to validate before any cutover, and s4embed drift watches embedding drift and recall and recommends re-tuning.
It runs as a standard Amazon Linux 2023 AMI behind your own load balancer, in your own VPC - your data and your vector database never leave your account, and there is no lock-in. The gateway supports API-key auth when configured, request size and concurrency caps, a readiness probe that fails closed on billing or store problems, and Prometheus metrics. Billing is usage-metered through your AWS bill: you pay per text embedded, document indexed, and search served, reported hourly via the AWS Marketplace Metering Service.
Highlights
- Cut vector-search RAM by up to 32x with a measured recall curve: binary quantization + two-stage rescore reached recall@10 of 0.976 to 1.000 on a 30k-vector benchmark depending on store and over-fetch (OpenSearch 0.995, pgvector 0.996, Qdrant 1.000, Milvus 0.976). You tune the operating point to your recall target.
- Decide with data before you cut over: s4embed prove estimates the recall/cost frontier on your vectors, s4embed compare measures live ANN recall across OpenSearch, pgvector, Qdrant, and Milvus, and a shadow mode replays live traffic against the compressed path - plus drift-aware re-tuning.
- Store-agnostic, runs in your VPC, no lock-in: a standard Amazon Linux 2023 AMI behind your load balancer; usage-metered billing (texts embedded, documents indexed, searches served); your data and your vector database never leave your account.
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Delivery method
Delivery option
CloudFormation Quick Launch
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux 2023
Deployed on AWS
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Pricing
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Try this product free for 14 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
Embedded texts | Per text embedded via the /embed endpoint. | $0.0000003 |
Indexed docs | Per document indexed via the /index endpoint. | $0.000001 |
Searches | Per query served via the /search endpoint. | $0.0000003 |
Dimensions summary
You pay based on three independent usage meters, each tied to a different API endpoint. One meter counts texts you embed through the /embed endpoint. A second counts documents you index through the /index endpoint. A third counts queries served through the /search endpoint. These meters are not tiers or bundles. Each accrues separately as you use that endpoint, and AWS reports the totals hourly through the Marketplace Metering Service. You also pay for the EC2 instance that runs the gateway in your own VPC, billed separately on your regular AWS invoice.
Top-of-mind questions for buyers
One embedded text is a single text sent to the /embed endpoint for conversion into a vector. One indexed document is a document stored via the /index endpoint. One search is a single query served through the /search endpoint. Each is counted individually and reported hourly through the AWS Marketplace Metering Service.
It depends on your workload. Retrieval-heavy applications with many live queries accrue more on the /search meter. Bulk ingestion of a large corpus drives the /index and /embed meters. All three bill independently and add together on one invoice, alongside the separate EC2 instance charge.
The three meters count actual endpoint activity — texts embedded, documents indexed, and searches served. When no requests hit those endpoints, no usage accrues. The EC2 instance running the gateway in your VPC still bills per instance-hour on your regular AWS invoice while it runs.
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Usage is metered hourly and charges already incurred are generally non-refundable. If you believe you were billed in error, contact aws-support@abyo.net within 30 days and we will investigate and correct any verified billing error.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Adds a CloudFormation Quick Launch delivery option. Software identical to version 1.0.1.
Additional details
Usage instructions
Launch the AMI in a private subnet behind an Application Load Balancer; terminate TLS at the ALB and do not expose the gateway port directly to the internet. The gateway listens on TCP 8080 and serves GET /ready for the ALB health check. Configure the vector store kind (opensearch, pgvector, qdrant, or milvus) and endpoint, and an API key (S4EMBED_API_KEY), via instance user-data, tags, or SSM Parameter Store. Grant the instance role aws-marketplace:MeterUsage so metered billing works. A CloudFormation quick-start that provisions the OpenSearch and pgvector paths is included; Qdrant and Milvus are supported by pointing the gateway at your existing endpoint. For the full configuration reference, contact aws-support@abyo.net .
Support
Vendor support
Email support at aws-support@abyo.net .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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