What workloads does the VLVC platform support?

VLVC supports end-to-end production ML workloads including online and batch inference, distributed training orchestration, feature management, model registry, and continuous evaluation — across LLMs, classical ML, computer vision, and time-series models on the same runtime.

How is VLVC's inference platform deployed?

Customers can deploy on VLVC-managed infrastructure across 14 global regions, in their own VPC via a dedicated deployment, or in air-gapped on-premises environments. The same control plane and compliance posture apply across all three deployment modes.

Which compliance frameworks does VLVC hold?

The VLVC deployment stack is independently audited for FedRAMP Moderate, HIPAA, and SOC 2 Type II in a single deployment, with ISO 27001 certification and quarterly third-party penetration testing as standard.

How long does it take to move a first model into production on VLVC?

VLVC customers ship their first model to production in an average of 8.7 weeks, compared to an industry median of roughly 14 months for teams building equivalent pipelines in-house.

Does VLVC replace existing MLOps tooling?

VLVC is designed to consolidate inference serving, orchestration, observability, drift detection, and compliance reporting into one platform. Existing notebooks, feature stores, and registries can be integrated through documented adapters during migration.

// Platform Overview — v8.2.0

VLVC Platform — one runtime, four interlocking subsystems, audited end to end.

We replace brittle in-house ML pipelines with a unified, self-healing production stack — shipping the average team's first model to production in 8.7 weeks instead of the industry median of 14 months.

Schedule a Technical Scoping Call →

// 01 — Subsystems

Four interlocking subsystems. One runtime. One audit boundary.

The VLVC platform is not a suite of separate products wired together. Inference, Training Orchestration, Observability, and Compliance Posture share a single control plane, a single model registry, and a single audit log — so an evaluating engineer can reason about the entire production surface in one mental model.

Inference Runtime

The hot-path execution layer. Autoscaling, request routing, KV-cache management, and shadow/canary deployment across the 14-region private footprint and the on-prem reserved H100 cluster.

p99 latency42 ms
cold-start< 800 ms
GPU footprint4,800 × H100

Training Orchestration

Schedules, resumes, and checkpoints distributed training jobs across heterogeneous hardware with deterministic replay and full lineage capture.

Observability

First-class telemetry: data drift windows, prediction-skew detection, and incident timelines tied back to model version, feature set, and deployer identity.

Compliance Posture

FedRAMP Moderate, HIPAA, SOC 2 Type II, and ISO 27001 attested in a single deployment stack — not stitched together per workload. Audit cadence is documented and current.

// 02 — Runtime Topology

A documented, inspectable runtime — not a black box.

Every component is named, every boundary is auditable, and every cross-process call lands in the same immutable event stream. Map it against your existing architecture diagrams and you will see the integration points immediately.

VLVC runtime architecture schematic showing control plane, data plane, model registry, feature store, and policy engine connections — Figure 2.1 — VLVC Runtime Topology, control plane (left) and data plane (right), joined by the model registry and the audit log.

Five named boundaries

A.
Control Plane
Scheduling, routing, policy, and the immutable audit log. Deployed active-active across three regions.
B.
Data Plane
Inference execution, autoscaling, and KV-cache management. Runs in customer VPCs or VLVC-managed regions.
C.
Model Registry
Versioned artifacts with cryptographic provenance, signed promotion gates, and full upstream lineage.
D.
Feature Store
Online and offline parity, point-in-time correct joins, schema-versioned at the column level.
E.
Policy Engine
Declarative guardrails evaluated at request time: rate limits, PII redaction, model allow-lists, and per-tenant budgets.

// 03 — Capabilities

Granular capabilities, written the way a senior engineer would spec them.

Every capability below is verifiable against the public reference architecture and the audit reports linked in the compliance section. No marketing-speak rephrasing of a feature list.

Inference — autoscaling & routing

Autoscaling policy: Queue-depth + p99 latency dual signal, 15s observation window, scale-to-zero after 5m idle
Routing strategies: Sticky, consistent-hash, region-pinned, and cost-optimized — switchable per route without redeploy
Shadow & canary: Mirrored traffic + percentage ramp with automatic rollback on guardrail violation
KV-cache: Prefix-shared across replicas; eviction logged and replayable for incident forensics

Training — orchestration

Scheduler: Topology-aware placement across H100, A100, and Trainium with gang-scheduled pods
Checkpointing: Async, sharded, with deterministic resume from any prior step
Lineage capture: Code commit → dataset hash → hyperparameter → checkpoint → promoted model, signed end to end
Replay: Bit-exact reproduction of a training run from the captured lineage within 0.4% metric variance

Observability — drift & skew

Drift windows: Kolmogorov–Smirnov, PSI, and learned-embedding drift on configurable rolling windows (default 1h/24h/7d)
Prediction skew: Live vs. shadow vs. baseline; alert thresholds tied to per-model SLOs, not a global default
Incident timeline: Auto-correlated against deploy events, feature-set changes, and upstream data-pipeline incidents
Self-heal: Policy-driven automatic rollback to last green model when guardrail SLO is breached for > 90s

Registry — provenance & promotion

Artifact signing: Sigstore-backed signatures on every promoted artifact; verification enforced at admission
Promotion gates: Declarative YAML, evaluated against offline + shadow + canary metrics before prod rollout
Lineage graph: Queryable as a property graph; exportable to Neo4j-compatible format for downstream audit tooling
Retention: Configurable per tenant; default 24 months hot + 7 years cold with cryptographic proof of immutability

// 04 — Operating Principle

Ship, observe, self-heal.

The platform is built around three verbs — ship, observe, self-heal — and one conviction: observability is a first-class production concern, not an afterthought bolted on after the model is already in production. Every subsystem treats the others as its source of truth: the Inference Runtime reads the registry, the registry reads the lineage graph, the lineage graph reads the audit log, and the audit log reads everything. Brittle in-house pipelines fail because one of those edges is implicit; we make every one explicit.

— Dr. Lena Voronova, Co-founder & CTO, VLVC, Inc.

// 05 — Compliance Posture

Audited, current, unified across the deployment stack.

Compliance is a single deployment property, not a per-workload patchwork. The same stack that runs your inference is the one the auditors reviewed.

Framework	Tier covered	Audit cadence	Last attestation	Auditor
FedRAMP Moderate	Full platform — control plane, data plane, registry, audit log	Annual + continuous monitoring	2024-11-14	Schellman & Co.
HIPAA	Inference + feature store + audit log, BAA available	Annual	2024-09-02	Coalfire Federal
SOC 2 Type II	All five trust service criteria, customer-facing deployments	Annual, 12-month observation window	2024-12-20	Deloitte & Touche
ISO 27001:2022	ISMS covering Boston, London, Singapore, Munich offices	Surveillance audits every 12 months, recertification every 36	2024-07-08	BSI Group