Production observability for AI workloads
Skyportal finds what changed, ships the fix as a PR, and proves it held — on your workload.
Built for the AI that actually breaks.
Safe to connect to production.
From regression to verified fix.
AI workloads break in production, and the evidence of what changed is scattered across every tool you own. Skyportal rebuilds it into one causal timeline — and ships the fix as a PR, proven on staging.
When production breaks today
The cause could be a deploy, a config value, a model version, or the GPU itself — and the evidence lives in different tools: monitoring, run traces, GitHub, cloud state, deploy history, and Slack.
The slow part isn’t the fix. It’s finding what changed.
p95 latency doubled overnight on one inference path. The team tabs between Grafana, MLflow, deploy history, kubectl, and GPU telemetry, rebuilding the timeline by hand. Three hours in — still guessing.
Ask SARA what changed — she rebuilds the timeline across code, runtime, models, and infra.
Every issue is diagnosed in production, every fix is proven on staging, and nothing reaches production until you promote it.
The GPU wasn’t the bottleneck — the CPU control plane was. A config PR, proven on staging: p95 5.1s → 1.8s.
These are three of dozens — missing instrumentation, KV-cache OOMs, model drift, retrieval degradation. See every failure and its fix in the Playbook →
Skyportal diagnoses the cause, ships a fix you approve, and proves it on your workload. And because it made that workload production-ready before you shipped, the answer to “what changed” is already there.
Before you ship, Skyportal checks the workload will actually run — the model fits the GPU, the runtime is packaged right, the instrumentation is in place — and pushes a verified-ready build to staging. So when something breaks later, there’s an answer.
A regression lands, and SARA pulls the before-and-after off one timeline — deploys, configs, model versions, runtime, GPU telemetry — and ranks the likely causes, most to least probable.
It proposes the top fix, checks the blast radius, and — on your approval — opens a pull request in your GitHub. Your team reviews and merges; your GitOps ships it to staging. A real code change in your workflow, not a suggestion in a chat window.
It re-runs your workload on staging to confirm the fix held. If it holds, it’s ready to promote to production; if it doesn’t, it reverts and works down to the next likely cause — until the workload passes.
Every verified fix goes into operational memory — so the next diagnosis is faster.
Read-only first. Nothing changes without your approval. Every change is a reviewable PR; every action is audited.
It watches before it touches anything.
Changes ship only as PRs you review.
Every action, logged end to end.
No SDK in your serving path. No re-platforming. Skyportal reads from the systems your stack already emits to — and ships changes back through your own GitHub workflow.
Reads state, logs, and run history — and ships changes back as PRs.
Kubernetes
state · deploys · pod logs
Slurm
jobs · queues · node state
MLflow
runs · metrics · lineage
Weights & Biases
experiments · metrics
GitHub
reads history · opens PRs
Argo / GitHub Actions
your existing GitOps ships it
Hardware and host metrics via the standard collectors.
NVIDIA DCGM
GPU health · memory · util
Prometheus
host & service metrics
OpenTelemetry
traces · logs · metrics
No per-framework integration or SDK — Skyportal operates at the run, config, and infra layer.
LLM observability watches your app. APM watches your infra. Neither connects the change that broke production — your code, your config, your model version — to the workload it broke. And neither ships a fix and proves it.
Ship code fast. Blind the moment it’s live — no operational memory, no runtime context.
Traces, evals, quality drift. Stops at the app layer — and doesn’t fix.
Sees clusters, nodes, deploys. Doesn’t touch your code, config, or model lineage — and can’t verify a fix on your workload.
Every verified fix is remembered. The next diagnosis starts where the last one ended.
Trusted by teams running AI in production
5 min Dev → staging
“I didn’t have to wait on an ML engineer.”
— Applied AI Scientist, Series B agent startup · 6-person AI team
10 min PR ready
“It found the missing MLflow metric and synced the PR from the dashboard.”
— ML Platform Engineer, healthcare AI platform · 13 production models
~$7K GPU waste avoided — in a single catch
“It caught the invisible failure before we burned more GPUs.”
— Forward Deployment Engineer, enterprise AI company · 64× H100 fleet
Faster recovery. Lower compute. Engineers back on the roadmap.
Priced by workload, not by seat. Every paid tier ships fixes as PRs in your repo, proven on staging — you choose how much Skyportal watches.
Save 20% on Pro & Teams
Free
$0
For individual builders
See what SARA finds.
1 workload · 30-day history
1 seat
Includes
No credit card required.
Pro
$99 /mo
For small AI teams
Diagnose, fix, and verify — when you ask.
3 workloads · 3-month history
1 seat
Adds
Teams
$599 /mo
For teams running AI in production
SARA finds it before you ask.
10 workloads · proactive on 1 · 1-year history · 5 seats
Adds
Enterprise
From $2K /mo
For platform & compliance teams
Your environment, your rules.
Custom workloads · all proactive · unlimited history · custom seats
Adds
Volume and multi-year pricing available.
What’s a workload? One monitored service or pipeline: an inference endpoint, a serving cluster, or a recurring training job.
Reactive vs proactive Reactive: SARA investigates when you ask. Proactive: SARA watches the workload and opens the diagnosis before you ask.
Seats Pro: add up to 3 seats at $100/seat/mo. Teams includes 5. Need more? Talk to us.
Bring one workload. Get your first verified fix.
