Numbers that speak for themselves.
- 180+
- Machine learning systems deployed across research, inference, and automated execution pipelines.
- 1,000+
- Engineering, research, and quantitative teams running Vantage AI in production environments.
- 4.8B
- Predictions served every 24 hours across the platform's model and automation layer.
- 99.99%
- Trailing 12-month availability across the production inference and automation infrastructure.
Three systems, one platform.
Vantage AI combines machine learning, high-throughput data processing, and disciplined automation into a single stack — engineered from the model layer down to the metal.
Models that adapt without drifting.
Purpose-built architectures trained on high-frequency, multi-modal data. Every model ships with reproducible training runs, versioned weights, and a full evaluation harness — so behavior in production matches behavior in research.
A pipeline built for scale, not demos.
Petabytes of structured and unstructured input, streamed and batched through the same infrastructure. Feature stores, lineage tracking, and schema contracts keep downstream systems honest as inputs evolve.
Systematic execution within defined limits.
Automated processes run 24/7 inside strict operational bounds — position sizing, throughput caps, and circuit breakers are policy, not opinion. Every action is logged, auditable, and reversible.
Built differently: how Vantage AI stacks up.
We've evaluated the leading model vendors, MLOps platforms, and automation stacks — and found most of them underwhelming, opaque, or brittle under real load. Here's what makes our approach different.
One-model, one-task systems
A single narrow model with no path to compose — one distribution shift and the whole pipeline degrades.
Black-box vendor APIs
Closed weights, opaque behavior, and no way to audit what the system actually did on your data.
Prototype-quality infrastructure
Notebooks glued to cron jobs. Impressive demos that never survive contact with production traffic.
Self-tuning that quietly drifts
Continuous retraining without evaluation gates. Behavior changes silently and nobody notices until it's costly.
Automation without limits
Systems that act at full throughput with no circuit breakers, no throttles, and no reversible state.
Offshore, unaccountable teams
Anonymous engineering, ghost founders, and outsourced ops with no way to reach a real human on-call.
Composable model stack
Specialized models orchestrated together — the same architectural discipline serious AI teams operate on.
Open, auditable behavior
Reproducible training, versioned weights, and per-decision provenance. Every output is explainable on demand.
Production-grade infrastructure
Built for petabyte-scale data, high-throughput inference, and the same SLOs enterprise systems are held to.
Evaluation gates on every release
Nothing ships until it beats the incumbent on live-shadow evaluation. No silent drift, no surprise regressions.
Automation with hard limits
Throughput caps, circuit breakers, and reversible actions are policy — enforced by the platform, not by convention.
New York based, in-house team
Research, engineering, and infrastructure under one roof. Real names, real people, real accountability.
The people behind the platform.
Martin C.
Leads platform engineering and model architecture. More than a decade building large-scale machine learning systems designed to run reliably in production.
David H.
Oversees model research and evaluation, turning experimental work into disciplined, reproducible programs that ship on a predictable cadence.
Eric L.
Runs the platform's compute, data, and inference infrastructure — focused on throughput, reliability, and operational safety at scale.
Elaine R.
Leads client relationships and external communications, and represents Vantage AI on the company's ongoing capital and partnerships initiatives.
What teams say about Vantage AI.
Feedback from engineering, research, and platform leaders running Vantage AI in production.
“Vantage AI cut our inference latency in half without a single line of application code changing. Deployment was the easiest infrastructure migration we've run.”
“The evaluation harness alone has changed how our team ships. We went from quarterly model releases to shipping something meaningful every week.”
“We compared four platforms during our RFP. Vantage AI was the only one where the reliability numbers in the pitch matched what we measured in production.”
“Onboarding took a week, not a quarter. The team walked us through the failure modes first, which is exactly what a serious infrastructure vendor should do.”
“Multi-model routing works out of the box. We're running eleven models in production with a single control plane and no bespoke glue code.”
“The observability layer is the best I've used. Every request is traceable end-to-end, and the cost attribution finally lets us bill workloads back to teams.”
Questions teams ask before deploying.
The short answers to what buyers, engineers, and platform leads ask us most often. For anything more specific, get in touch.
- What does Vantage AI actually deploy?
- A managed inference and orchestration platform for production machine learning workloads. You bring the models — Vantage AI handles routing, scaling, evaluation, observability, and cost attribution across a single control plane.
- Where does our data live?
- Under your control, in the environments you already trust. Vantage AI supports single-tenant deployments in your own cloud account, VPC peering, and on-premise installs for regulated workloads. Nothing is trained on your inputs.
- How long does onboarding take?
- Most teams are running production traffic within the first week. A dedicated solutions engineer works with you through integration, evaluation harness setup, and the first shadow deployment before you cut over.
- Which models are supported?
- Any open-weight model plus routing to the major hosted providers. Teams commonly run a mix — open-source models for high-volume workloads, frontier hosted models for the harder tail — through a single unified API.
- How is pricing structured?
- Usage-based for the platform layer with volume commitments for larger deployments. Enterprise plans include dedicated support, custom SLAs, and single-tenant infrastructure. Contact us for a tailored quote.
- Do you offer SLAs and compliance coverage?
- Yes. Enterprise agreements include uptime SLAs, incident response commitments, and support for SOC 2, HIPAA, and GDPR requirements. Deployment options exist for teams with additional regulatory constraints.