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AstroPulse

AI infrastructure control plane

Ship faster.Keep control.

The infrastructure AI needs in production, without building it. It runs on the cloud you already have, under the rules you already set.

ap.deploy(model="Qwen3-32B", traffic="60 rps")
  1. PolicyApproval · audit · rollback
  2. OperationsHealth · cost · lifecycle
  3. ServingScheduling · routing · scaling
  4. WorkloadsServicesBatchAI workloads
  5. KubernetesYour clusters · your node pools
  6. Your cloudaws · us-east-1 · vpc-0a91c4
Your perimeteraws · us-east-1 · vpc-0a91c4
  • Your cloud
  • Your GPUs
  • Your network
  • Your policies
Request accessWhy we're different
Running in your VPC

https://qwen3.svc.internal/v1

HealthHealthy
Accelerators4 × H100
p99412 ms
Cost$14.20 / hr

The difference

One platform, or two.

Nobody runs only inference. You already have policy, approvals, audit and an on-call rotation. Every inference platform hands you a second one.

An inference platform in your VPCTwo planes to operate

Every control you already built, implemented twice.

AstroPulseOne plane

Inference lands beside everything else. One place to look.

How it works

From code to endpoint.

01

Declare intent

A model, a traffic shape, a latency target. Not a node pool.

from astropulse import Client

ap = Client()
ap.deploy(
    model="Qwen3-32B",
    traffic="60 rps",
)
Derived plan
2 × H100 · TP=2 · 3 replicas
Estimated
$14.20 / hr
02

Policy runs first

Evaluated before anything is provisioned. A refusal never reaches a GPU.

Model allowlistQwen3-32B
BudgetWithin limit
Public ingressDenied by policy
Human approvalRequired
Decision
Awaiting approval
03

Operate

In the same operational model as everything else you run.

Tokens / sec
8,732
p99 latency
412 ms
Utilisation
71%
Rollback
Armed

The boundary

Nothing leaves.

Two accounts. Instructions cross one way over a signed channel, status comes back as metadata, and nothing else has a route at all.

Desired stateSigned over mTLS
Health · cost · audit eventsMetadata only
Data · weights · promptsNo route exists

We hold no copy of your data, your weights or your traffic. There is nowhere in our account to put them.

Governance

Policy is execution.

Evaluated as part of running it, not reported afterwards.

Requestap.deploy(model="Qwen3-32B")
  1. PolicyModel allowlistNot reachedPassedDenied
  2. ApprovalHuman sign-offNot reachedPassedDenied
  3. BudgetSpend ceilingNot reachedPassedDenied
  4. CostAttributionNot reachedPassedDenied
  5. AuditEvery decisionNot reachedPassedDenied
  6. RollbackArmed on deployNot reachedPassedDenied
Provisioned

Autonomy

It proposes. You approve.

Nova proposes changes and sends them down the same path a human change takes. Autonomy is safe here because the plane already knows how to say no.

  1. 01Situation

    p99 latency climbing

  2. 02Proposal

    Scale 2 → 6 replicas

  3. 03Policy check

    Budget · blast radius · SLO

  4. 04Approval

    Platform on-call

    Waiting
  5. 05Executed

    Applied · rollback armed

Direction

Deeper, not wider.

Inference first, and then everything that happens to a workload after it ships. Not a catalogue of adjacent AI products.

  1. 01

    Deploy

    A model becomes a governed endpoint in your cluster.

    Model · plan · policy · endpoint
  2. 02

    Operate

    Health, scaling, rollout and rollback, in the model you already use.

    Health · scale · roll forward · roll back
  3. 03

    Govern

    Policy, approvals, quotas and audit — including for autonomous action.

    Policy · approval · quota · audit

One platform. Every workload.

Running production inference on a second control plane? Let's talk.

Request access

Access is limited, and by conversation.