Run any AI workload.
On infrastructure you own.
Frontier AI infrastructure
Run it on the compute you already own, or let us run it fully managed. Either way, you pay a fraction of hosted-cloud prices.
Most AI infra just schedules your jobs and hands back logs. Tensile is the enterprise-grade control plane for training, inference, and agents that returns every run verified: graded, costed, and ready to promote. And it runs on a sandbox engine we built ourselves, not a wrapper over someone else's.
Stop solving infrastructure and platform-engineering problems. Ship agents and models as fast as you can build them.
Training, inference, agents, RL, and evals — one API, one UI.
Everything a hosted AI cloud gives you — the full workload surface, down to the specifics — running first-party on infrastructure you own. Each family is a first-class surface in the product.
Post-train on your own cluster
SFT, RL, DPO, or LoRA against a base model and dataset. The Tensile operator gang-schedules the job onto your Kubernetes/Volcano or Slurm cluster — live metrics, and a trained adapter back.
- SFT · full fine-tune · LoRA
- RL & DPO pipelines
- Multi-node gang-scheduling
- Parallel hyperparameter sweeps
from tensile import Workload # Gang-scheduled onto your K8s/Volcano or Slurm clusterjob = Workload.training( base="meta-llama/Llama-3.1-8B", method="lora", # sft · rl · dpo · lora dataset="s3://org/gold/v1", backend="volcano", # volcano · slurm gpus=8,).submit() job.stream() # live loss + metrics → /observabilityA fresh sandbox for every run — resumed in ~16 ms.
Tensile Sandbox is our flagship: state-of-the-art Firecracker microVM isolation, engineered and measured in-house. Hard kernel-level separation with an egress allowlist enforced before the VM resumes, one VM per run, multi-cluster, on CPU and GPU. Every sandbox is stateful: checkpoint its RAM and disk live, pause into a snapshot, resume warm in ~16 ms, or fork it 1:N — so an agent's working memory survives pauses, node loss, and branching. It's the substrate for agents, evals, RL rollouts, and load tests — a drop-in replacement for the hosted sandboxes (Daytona, Modal, E2B, Fargate, Vercel) that runs ~3× cheaper at scale. Training and heavy inference skip the sandbox and schedule straight onto your GPU clusters.
Engine numbers measured on bare-metal Intel; cost vs Daytona at 10,000 average concurrent sandboxes on on-demand GCP. See the full comparison →
We build the stack, not the glue.
Tensile isn't a thin layer wired over other people's services. The systems that decide performance and cost — the sandbox substrate, the post-training stack, the inference optimizer, the scheduler — are ours, built and tuned in-house. You get everything the market leaders offer, on infrastructure you own.
Our own microVM fleet
A kernel-isolated Firecracker fleet across your clusters, on CPU and GPU: sub-second boots, one VM per run — everything the hosted sandboxes give you, at a fraction of the cost, and you own it.
See the sandbox →SFT · RL · DPO · LoRA
First-party post-training pipelines with checkpointing and direct data integration. No external training service to rent, no framework to relearn.
Open runs →Fast, cheap serving
Our own serving and autoscaling layer, tuned for latency and throughput, so inference deployments stay fast and cheap under real load.
See deployments →Load-test, size, place
A scheduler that load-tests every workload, right-sizes capacity, and places each run on the best backend — cluster or sandbox — automatically.
Open the analyzer →Size the compute before you spend it.
Deployment isn't “grab a GPU”; it's a capacity question. Analyze load-tests your inference, training, and rollout workloads before they run, so you know what a job needs before you pay for it. The workload engine then provisions the right cluster capacity and autoscaling policy for your team, with no over- or under-provisioning.
The platform to eliminate platform engineering.
Today you get two options, and both are bad: hire and manage an infra team, or depend on vendors who charge insane markups. Tensile is the third. Subscribe the way you'd hire an AI infra engineer, without the months of bandwidth and resources it takes to build that team. Our workload engines design the best infrastructure for every job, then run it.
What's left is a small surface: a few things you work with, a few things you run, and the guarantees we keep underneath. You live in the first two columns — the third is our job.
- Agents
- Models
- Data
- Evals
- Train
- Infer
- Deploy
- Store
- Cost
- Observability
- Reliability & recovery
- Security
- Compliance
Every model, dataset, and eval — versioned, with lineage.
Nothing floats free. Tensile keeps a registry for the artifacts you produce and consume, so any run is reproducible and any result traces back to the exact inputs that made it — which dataset trained which model, which eval graded it, and at what cost.
Checkpoints & adapters
Every checkpoint and adapter, versioned with its metrics and ready to deploy or evaluate.
Browse models →Versioned & addressable
Training and eval datasets, versioned and addressable — the same s3://org/gold/v1 across every workload.
Browse data →Suites & results
Eval suites and their results, so a leaderboard stays reproducible and comparable over time.
Browse evals →Run at the frontier — without the frontier markup.
If you have the compute, we're your team. Tensile runs on heterogeneous hardware — any GPU, any generation, across clouds, neoclouds, Slurm, or on-prem — and makes it first-class for frontier work. Point us at what you already own and keep the frameworks your team knows; nothing is a walled garden, and there's no premium for renting someone else's cloud. Stop compromising on frontier work just to dodge the bill.
Plugs into PyTorch · vLLM · SGLang · TensorRT-LLM · TRL · DeepSpeed · Ray · verifiers · inspect-ai · lm-eval, or bring your own.
No hardware of your own? Managed Tensile gives you the same first-party stack, run for you at a fraction of hosted-cloud pricing.
One platform, every team's job.
AI work isn't one job. Each team gets the surface it needs, without adopting anyone else's tooling.
Plan capacity
Right-size cluster capacity, set autoscaling policies, and keep cost and reliability in check.
Open cost →Deploy
Ship agents and inference to production, sandboxed when it matters and scaled automatically.
See deployments →Train
Train and post-train models on your clusters, with checkpoints and direct data integration.
Open runs →Bring your infra. We'll bring the plane.
Point Tensile at the compute you already run: connect, submit a workload, get a verified result. No platform team required.
Your team stays AI-native, building and automating workloads. We manage everything below: cloud accounts, VPC and networking rules, containers and Docker, scheduling, and scale.
Connect your infra
Register a Kubernetes or Slurm cluster, or cloud creds — GCP, AWS, Azure, Nebius. Nothing leaves your perimeter.
Submit a workload
Declare training, inference, an agent, or an eval from the CLI, SDK, or UI. Tensile picks the backend that fits.
Get a verified outcome
Every run is graded against your bar, with cost, latency, and lineage — and promote or rollback behind gates.
pip install tensile && tensile connectIn early-access private beta today. Frontier agents like auto-research are coming soon.