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A scheduler packs the cluster. It doesn't defend the invoice.

If your AI bill is mostly inference tokens across Bedrock, OpenAI, and Anthropic, scheduling isn't the problem. Defending the next model swap is. Run:ai builds one of the strongest GPU schedulers in the market, and since April 2025 its core has been open source: NVIDIA released KAI Scheduler under Apache 2.0 and it is now a CNCF Sandbox project. If you need gang scheduling and fractional GPUs, take it. It is free and it is good. Scheduling and cost accounting are different jobs. We start with the LLM lane most teams live in: see the last 30 days, gate the next change, prove it held. When you also own the cards, we sit beside the scheduler.

See the last 30 days

At a glance

Where the products differ, and where they coexist.

Run:AI / NVIDIA AIE

TensorCost

GPU scheduler: fractional GPU (MIG/MPS), gang scheduling, preemption, queue fairness. Strong fit for NVIDIA training fleets.

Not a scheduler. Cost-accounts around yours, including Run:AI's. Ingests job metadata; surfaces per-job cost attribution.

Hardware scope: primarily NVIDIA (DGX, H100/H200/B200). AMD, Inferentia, TPU are not the product priority.

Vendor-neutral architecture. AMD MI300, AWS Inferentia, Google TPU hooks in the agent SDK. No production references yet; early customers are co-designing.

Managed inference (Bedrock, OpenAI, Anthropic, Vertex, Azure OpenAI): not covered.

Provider connections live. Full spend attribution across all five providers, reconciled daily against the cloud invoice.

Cost attribution bolted on. Scheduling throughput and fairness is the core product.

Cost is the product. Every AI usage signal and hardware utilization reading feeds a savings record.

Primarily on-prem / private cloud NVIDIA. Multi-cloud limited.

AWS cloud GPU live in production. GCP, Azure and on-prem are under validation, not yet GA.

No agent-level spend attribution or LLM inference cost tracking.

Per-agent, per-workflow, per-user attribution. Runaway-job catch flags a workflow running at five times its own 14-day baseline, or 200 calls in a rolling hour.

No savings verification ledger or reconciliation API.

Append-only tamper-evident record, customer-verifiable. Every saved dollar ties back to an invoice line.

Owned by NVIDIA since 2024.

Independent. Not backed by any hardware or cloud vendor.

Where we differ

Four structural differences that matter when you're making a vendor decision.

Independence: we don't sell GPUs

NVIDIA sells GPUs. TensorCost doesn't. Our incentive is to reduce your AI spend across any vendor: NVIDIA, AMD, AWS, Google or SaaS inference. A product owned by the world's largest GPU vendor will prioritize making that hardware easier to buy.

Cost first, scheduling second

Run:AI was designed to pack training jobs efficiently across NVIDIA clusters. TensorCost was designed to answer "what is my AI spend, by team and model, and what should I change?" Different question, different product.

LLM and inference workloads

If your AI bill is 70% inference tokens across Bedrock, OpenAI, and Anthropic, and 30% GPU capex, TensorCost is the better fit. If it's 95% batch training on NVIDIA, Run:AI is. Many customers are somewhere in between.

Multi-cloud and direct providers

Provider connections shipped (Bedrock + Azure OpenAI + Vertex live; OpenAI + Anthropic direct validated per-customer). AWS cloud GPU live; GCP, Azure, and on-premises under validation. Run:AI's deployment story is primarily on-premises NVIDIA.

Common questions

Can we run TensorCost on top of Run:AI?

Yes. TensorCost doesn't touch the scheduler. Run:AI continues to schedule jobs. TensorCost's agent collects hardware telemetry independently, and Run:AI job metadata can be wired in to add cost attribution on top.

Does TensorCost replace the GPU scheduler?

No. We don't schedule jobs, manage queues, or handle preemption. We sit beside the scheduler (Run:AI, Slurm, Ray) and cost-account what it runs.

Do you handle GPU partition and sharing configurations?

Partition visibility: yes. We collect partition-level telemetry and surface per-slice utilization and consolidation savings. Partition orchestration: no, that's the scheduler's job. Shared-memory configurations are tracked where they surface; coverage depends on your hardware setup.

Is multi-vendor GPU support (AMD, Inferentia, TPU) production-ready?

Not yet. The agent SDK has the architectural hooks. We have no production customers on those paths today — early customers are co-designing coverage with us. Expected production references by end of 2026.