ParallelIQ

GPU fleet efficiency and reliability for AI teams

Catch waste before it costs you.
Catch risk before it escalates.

GPU waste inflates your cost per token, forces premature hardware orders, and hides the risk signals that turn into incidents. 20–40% of what you have is recoverable.

Paralleliq finds the waste and risk, recommends the fix, and keeps your team in control of every change — with a full audit trail and nothing executed without human approval.

Your model weights, inference data, and customer traffic never leave your environment. Only operational metrics flow to the platform — evaluated through a deterministic rules engine, not a black-box model. Optional AI-assist is the only thing that ever reaches beyond it.

Scan Your Fleet FreeSee Your $/Token
paralleliq.app / fleet
 live
Workload infer-prod-eu running at 81% utilization · KV cache healthy
2s ago
Incident · KV cache pressure on a100-pool-2
needs approval
Tokens/sec
0
GPU util
0%
KV hit rate
0%
+ recommend rebalance shard 3 → 5 (a100-pool-2)
- evict idle replica infer-canary-2 · saves $184/hr
~ scale tier from B → A for prompt-7b
audit chain · sig 0x9f2…ae1
fleet utilization+12.4%
cost / 1k tokens−7.1%
Watch how Paralleliq works
See it live

Inside the dashboard

Install piqc freeBook a demo

The blind spot every dashboard shares — except this one

These failure modes are invisible to infrastructure monitoring because catching them requires understanding the model, not just the machine underneath it.

Mis-tiered models are silently inflating your $/token.

Models get updated. Traffic patterns shift. Token compression reduces context length. The GPU you correctly sized six months ago may be wrong today — costing you more per token than necessary. Nothing in your monitoring stack will tell you until the bill arrives or throughput collapses.

Why Paralleliq catches this: We read the model, not just the machine. GPU tier recommendations are matched to the actual model and workload shape, so tier misplacement gets flagged as it happens — not discovered on the bill.

0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hitshardrps0xA13Fevictscaledrainwarnschedtierkv:hit

You're buying GPUs you don't need yet.

When utilization looks high, the instinct is to order more hardware. But high utilization is often a false signal — your GPUs are busy doing the wrong things. Wrong batch sizes, wrong instance types, wrong concurrency settings. The meter is running. The throughput isn't keeping up.

Why Paralleliq catches this: Utilization alone can't tell the difference between busy and productive. We look underneath it — batch size, instance type, concurrency — so you know your real capacity runway before you place a 12-week-lead-time order.

By the time your monitoring catches it, your customer already has.

A customer deploys a model slightly too large for their chosen GPU tier. Under load, KV cache pressure builds and the container crashes. Your platform gets the support ticket — even though the root cause was a configuration the customer created. Without model-aware intelligence, there is no way to catch this before it happens.

Why Paralleliq catches this: Because we understand the model running on each GPU, not just CPU/memory graphs, we can flag KV-cache pressure before it becomes an OOM crash — the failure mode generic infrastructure monitoring is structurally blind to.

IQ

How Paralleliq delivers it

Six capabilities that work together to lower your cost per token, extend your capacity runway, catch production risk before it escalates, and keep your team in control.

The optimization engine is rules-based and deterministic — not model-driven. No AI making infrastructure decisions. Every recommended action shows you the blast radius and requires human approval before it touches your cluster.

See what your fleet is actually using

Starts as a read-only one-time scan. Scales to a lightweight agent — one per node, reading from your existing Prometheus. Auto-discovers vLLM and Ray Serve workloads with no changes to your serving stack.

Know your real $/token

See exactly what each deployment costs per hour and per token — and where tier mismatches, dark capacity, or idle replicas are inflating that number. Not estimates — actuals from your fleet.

Stop incidents before customers see them

Safety signals every 15 seconds — KV cache pressure, OOM risk, queue depth. Performance checks every 30 minutes. Structural tier analysis every 6 hours. Catch the problem before it becomes a support ticket.

Your team approves every change

Every recommendation approved by a human. Every action logged permanently. Full chain of custody for every change to your fleet — no black-box automation touching production.

Data boundary you can explain

Model weights, inference data, and customer traffic never leave your environment. Operational metrics — utilization, throughput, cache pressure — flow to the platform to power recommendations. Nothing that touches your customers' data moves. An optional AI-assist feature can send finding summaries to Claude for remediation guidance — configurable, and off by default for environments that need zero third-party data flow.

Priced to your contracts, not generic estimates

Running on-prem hardware, reserved instances, or proprietary models? We configure the platform to your actual contracted costs, your model catalog, and your team's operational policies — so the waste we surface is specific to your fleet.

Talk to an Expert

Built for teams where GPU cost is a business problem

Lower your $/token. Serve more customers before your next hardware order.

Enterprise AI Teams

Do more AI with the same budget. Never get caught in a procurement emergency.

You run your own models on your own infrastructure. Recovering utilization waste stretches your AI budget further and tells you your hardware ordering deadline months in advance — before the capacity crunch, not during it.

On-Prem & Sovereign AI

Owned or leased, 100% of waste is your cost.

There's no variable billing to hide inefficiency — whether the hardware is owned outright or under a fixed lease, every underutilized GPU-hour is money you're already committed to. For government, financial services, healthcare, and HR data teams, on-prem also means compliance: air-gapped operation, human-approved changes, and a permanent audit log — the change-control record regulators require.

GPU Cloud & Neocloud Providers

Give your customers lower $/token — without cutting your margins.

You own the infrastructure and your customers pay for GPU time. Recovering utilization waste lowers their effective cost per token using hardware you're already running. Customers who see efficiency gains don't go shopping for alternatives.

Inference API & Deployment Platforms

$/token is your pricing page. Own it.

Whether you charge per token or host customer models, GPU cost is your cost of goods sold. Paralleliq surfaces tier misplacement, dark capacity, and throughput suppression at the model level — with dollar impact per finding — so you can compete on price without compressing margin.

AI Services Companies

GPU cost is your COGS. Treat it like one.

Your gross margin is determined by $/token. Recovering utilization waste is direct margin expansion — no pricing change, no new customers required. As you scale, GPU cost should grow slower than revenue. Optimization is what makes that possible.

Hardware Manufacturers

Customers who use hardware efficiently buy more of it.

When customers underutilize a cluster, they question the ROI on the purchase — which affects the next one. Offering Paralleliq alongside your hardware turns a capital sale into an ongoing value relationship. Utilization data also shortens the expansion sales cycle.

Works with your stack

Reads signal from

vLLM vLLM Production Stack Ray Serve Triton Inference Server Prometheus SGLang · coming soonTensorRT-LLM · coming soon

Runs on

Kubernetes dstack SkyPilot AIBrix

Remediation targets

LMCache · also detectsRun.ai / KAI Scheduler · coming soon

Case Studies

Training
40%
lower training cost

Cutting AI Training Costs by 40% — No Trade-Offs in Performance

How a growth-stage startup closed the AI execution gap with deeper observability and policy-driven optimization.

Inference
40%
fewer incidents

Faster AI Model Releases with 40% Fewer Incidents

A mid-market firm modernized model serving with KServe, Triton, and inference-grade observability.

MLOps
85%
faster drift detection

Cutting Drift Detection by 85%: Observability that Transforms MLOps

How a platform team replaced a tangle of probes with a single drift signal that operators trust.

Compliance
100%
audit coverage

Compliance-Aware AI Data Infrastructure for Healthcare

Air-gapped GPU operations with immutable audit trails — change-board reviews went from weeks to hours. Applies to any regulated environment, not just healthcare.

The AI Infrastructure Journal

Deep dives into architecture, performance tuning, and operational excellence.

The GPU Shortage That Isn't
AI Infrastructure

The GPU Shortage That Isn't

I asked a GPU cloud provider what their biggest pain point was. They said they're running out of GPUs. Here's why I think the real problem is somewhere else entirely.

The Two Business Models Running AI Inference — And Why They Have Completely Different GPU Problems
AI Infrastructure

The Two Business Models Running AI Inference — And Why They Have Completely Different GPU Problems

Fireworks, Together, and Groq sell tokens. Baseten and Modal sell deployments. The same GPU waste looks completely different from each seat — and fixing it requires a completely different pitch.

Selling GPUs Is No Longer Enough — Why GPU Clouds Are Becoming Optimization Platforms
AI Infrastructure

Selling GPUs Is No Longer Enough — Why GPU Clouds Are Becoming Optimization Platforms

CoreWeave, Lambda, Crusoe, and RunPod all sell the same H100s at roughly the same price. The GPU clouds that survive the coming commoditization wave will be the ones that help enterprise customers run workloads well — not just the ones that have the most hardware.

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