16 agents · 7 providers · v0.4.0 GA · August 2026

GPU deployments,
decided in 60 seconds.

Sixteen specialized AI agents observe your cluster in parallel, reason independently, and reach a single consensus decision — before any human could read the first log line.

< 60 s autonomous decision 16 specialized agents 7 cloud providers RPO 0 ms · RTO < 5 s SHA-256 audit chain
The Problem

Three things break
every GPU fleet

You cannot catch them manually at scale. You cannot afford to miss them.

01 · Cost
Every bad deployment burns real GPU hours
An 8×H100 node on AWS costs $32.77 per hour. A misconfigured training job runs unnoticed for hours before a human escalates. That is not a monitoring gap. That is a budget leak.
8×H100 (AWS p4de.24xlarge) · $32.77/hr
4-hour bad run → $131 wasted before detection
02 · Visibility
Costs are invisible until the bill arrives
"$50k on compute this month" is not an insight. You need cost per model run, cost per million tokens, normalized across providers — before signing the next purchase order.
AWS H100: $18.40/1M tokens
CoreWeave H100: $11.20/1M tokens · 33% cheaper
03 · Scale
Sixteen failure modes, one on-call engineer
NCCL topology, lemon GPU nodes, config drift, RDMA health, compliance posture, security policy — checking them serially while a cluster idles is not a process. It is a risk.
Serial human review: 15–45 min
DeployIQ parallel verification: < 60 s
The System

Five steps.
No engineers on-call.

Every deployment follows the same autonomous loop, regardless of team size, cluster scale, or time of day.

01
Ingest

Signal received.
Loop opens.

A webhook from GitHub, ArgoCD, Jenkins, or Kubernetes reaches DeployIQ in under a second. Zero config required.

ingest
sourcegithub
receipt< 1 s
confignone
loopopened
02
Observe

16 agents fire
in parallel.

Every signal domain — GPU health, cost, canary, topology, compliance — is read simultaneously. Nothing waits for anything else.

observe
preflightNCCL · RDMA
cost$11.20/1M
canaryp99 +12%
nodes8/8 healthy
03
Reason

Each agent scores
with confidence.

Each agent runs a trained RL model and outputs Promote, Hold, or Rollback with a 0–1 confidence score. Not a hunch.

reason
preflightPromote 0.96
canaryHold 0.71
costPromote 0.88
policyPromote 0.99
04
Decide

One answer.
Under 60 seconds.

The DRP engine weighs all 16 scores into a weighted consensus. No black box — the reason for every decision is shown.

DRP
verdictHold 0.73
promote14 / 16
hold2 / 16
elapsed43 s
05
Act

Executed.
Sealed forever.

DeployIQ promotes, holds, or rolls back automatically. A SHA-256 audit chain seals the record — attributable and explainable, indefinitely.

act
actionHold
auditsha256:a1b2…
rollbackready
alertsSlack · PD
Agent Coverage

16 specialists.
Four domains. Every failure mode.

Each agent owns one signal. None overlap. Together they cover every failure mode in a production GPU fleet.

GPU Infrastructure
GPU Preflight
preflight
NCCL all-reduce bandwidth, RDMA QP link health, NVLink topology correctness. Fails fast before wasting a single training step.
Lemon Node Detector
lnd
Identifies underperforming GPU nodes — statistical outliers in throughput, ECC errors, thermal throttling — and quarantines them.
Topology Validator
topology
Validates the GPU interconnect topology matches the job's communication pattern. Wrong topology = 40–70% throughput loss.
Training Watchdog
twd
Monitors training loss curves, gradient norms, and step throughput. Catches divergence before you waste a full epoch.
Cost & FinOps
FinOps Attribution
finops
Attributes GPU cost by deployment, model, and team. Breaks down hourly spend by provider, instance type, and utilization efficiency.
NEW
Cost Normalisation
cost-norm
Normalizes GPU cost to USD per 1M tokens. Ranks providers by cost for your specific GPU model. Makes provider choice data-driven.
Reliability
Canary Verification
cva
Compares canary vs. baseline on error rate, p99 latency, and throughput with statistical significance gating.
Canary AI
cva-ai
ML-augmented canary analysis — detects subtle regressions that rule-based checks miss using model output quality.
Config Drift
cda
Detects configuration differences between deployed and desired state. Catches environment drift before it causes failures.
Migration Verification
mva
Validates database migrations against production data before apply. Checks rollback feasibility and schema invariants.
Operations
Log Intelligence
lia
NLP-based log analysis. Clusters error patterns and surfaces the root-cause line from millions of events in seconds.
Policy Compliance
pca
Evaluates deployment against security and compliance policy. Blocks non-compliant workloads before they reach production.
Pipeline Gate
pipeline
Enforces CI/CD quality gates: test pass rate, artifact provenance, SBOM completeness. Requires a signed artifact chain.
Cloud Parity
cloud-parity
Verifies multi-cloud environment parity so a workload on AWS behaves identically on CoreWeave or GCP.
Topology Reasoning
tra
Graph-based reasoning over cluster topology. Identifies communication bottlenecks and single points of failure.
Rollback Orchestrator
roa
Executes rollback sequences across all infrastructure layers with defined RPO targets when consensus votes Rollback.
GPU Economics

What it saves you,
in dollars per GPU cycle.

DeployIQ catches bad deployments before they waste hours of compute. And shows you exactly which provider gives the most compute per dollar.

The bad-deployment calculation
8×H100 SXM5 · AWS p4de.24xlarge
On-demand rate: $32.77 / hour
Without DeployIQ
Time to human detection: ~4 hours
Wasted compute: $131.08
With DeployIQ
Detection time: < 60 seconds
Compute spent: $0.55
Savings per incident: $130.53 · 238× ratio
At 100 training runs/month with a 12% bad-deployment rate, DeployIQ catches 12 incidents. Savings: ~$1,566/month per cluster. A 10-cluster fleet: $188,000 recovered per year.
Provider cost — USD per 1 million tokens
Hourly rates hide true inference cost. DeployIQ normalizes everything to USD/1M tokens so provider comparisons are like-for-like.
Provider$/hr$/1M tokens
CoreWeave$2.49$11.20
Lambda Labs$2.49$11.80
Nebius$2.89$13.20
GCP$4.19$16.50
AWS$4.10$18.40
Azure$4.50$20.10
Moving 20% of inference from AWS to CoreWeave saves 33% on that slice. At $50k/month: $3,300/month · $39,600/year with zero model changes.
By the Numbers

What you actually get

Autonomous Decision
< 60s
From webhook received to decision executed. Every deployment, every time.
Saved Per Incident
$131
Per caught bad 8×H100 deployment versus a 4-hour human-detected failure.
Specialized Agents
16
Each owns one signal domain. Parallel execution. Single consensus output.
Providers Normalized
7
AWS, GCP, Azure, CoreWeave, Lambda, Nebius, JarvisLabs — one USD/1M-tokens number.
Design partner slots
1 of 3 design partner slots · limited cohort

Become a Design Partner.
Shape autonomous GPU deployment.

We're selecting three GPU cloud infrastructure teams for a 90-day design partnership. You get production-ready autonomous deployment intelligence. We get real deployment telemetry and co-design input that makes the platform irreplaceable for exactly your workloads.

Early API Access

Full production API access from day one. 14-agent deployment verification on your real GPU cluster — not a sandbox. SLA-backed uptime, dedicated Slack channel with the engineering team.

Co-branded Case Study

A jointly authored technical case study documenting your deployment reliability improvements, GPU cost savings, and MTTR reduction — published under both logos and distributed to your prospect pipeline.

Roadmap Co-design

Monthly roadmap sessions with the founding team. Your GPU infrastructure requirements are built into the product architecture — not bolted on later. Design partners ship features 6 months ahead of GA.

Request Design Partner Access NDA-protected · No commitment · 90-day evaluation · Founding team responds within 24 h
Ready to see it?

The full benchmark report

93/100 across 50 parameters — agentic intelligence, GPU topology, event reliability, security posture, and audit depth. Independent analysis.

Benchmark Report TRL Assessment Feature Roadmap