Who needs this
- ML engineers shipping models to staging and production clusters
- LLM teams running on-prem inference with governance requirements
- Tech leads bridging data science notebooks and platform standards
Industries
ML platforms and lean platform teams moving fast safely
AI & ML engineering, MLOps, data platform, cloud-native startup, and internal developer platform teams shipping on their own terms.
Industry · AI & ML engineering
AI & ML engineering
LLM, GPU, and Kubernetes workloads in one engineering control plane
Model builders need GPU visibility, sane deploy paths, and the same cluster context as platform—not a separate “AI portal” that drifts from production. FusioNative keeps LLM deploy wizards, inference KPIs, and pod-level workloads in one navigation model.
Design, deploy, and observe LLMs and training workloads on Kubernetes with GPU metrics beside the pods they run on.
Industry pressures (why change)
- GPU pools are opaque—teams oversubscribe or starve jobs without a fleet view
- LLM deploy steps span cluster, storage, and networking with no single checklist
- Workloads and inference metrics live in different tools, so incidents take longer
Why FusioNative fits
- On-prem LLM wizard validates GPU, storage, and service exposure step by step
- AI workload and live metrics views tie utilization to namespaces and nodes
- Same control plane as platform—no duplicate inventory of clusters
How teams adopt it
- Register GPU-backed clusters and confirm DCGM/Prometheus signals appear
- Deploy or attach models through the LLM workflow with quota checks
- Monitor inference and pod health from AI workload and metrics screens
- Hand off capacity plans to platform when GPU headroom tightens
In Cloud Admin
What AI & ML engineering teams see in the product
Product screens with notes on when each view matters for your sector.
On-prem LLM overview
Model KPIs and GPU signals on one overview—where ML leads start stand-ups.
- Inference beside fleet metrics
- Drill into deploy wizard
- Production context preserved
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Active workloads
Deployments, StatefulSets, and GPU pods in one inventory—tie models to the objects platform actually runs.
- Namespace-scoped views
- Restart and health visibility
- Less kubectl archaeology
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Live performance analytics
24h CPU, memory, and GPU trends—spot training or inference spikes before they exhaust the pool.
- Real-time charts
- Cross-cluster comparison
- Feeds capacity conversations
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Industry · MLOps
MLOps
Deploy and monitor models on Kubernetes with guardrails
MLOps sits between ML code and platform reality. FusioNative gives you a repeatable deploy path for models, GPU allocation you can defend in review, and monitoring that speaks both inference and infrastructure.
Take models from artifact to monitored production services with wizard-driven deploys and fleet-grade observability.
Who needs this
- MLOps engineers owning the path from registry to production
- Inference platform teams standardizing deploy templates
- Reliability partners pairing models with SLO dashboards
Industry pressures (why change)
- Each team hand-rolls Helm for models—drift breaks reproducibility
- GPU requests do not match real pool capacity
- Inference outages lack ties between model metrics and node health
Why FusioNative fits
- LLM deploy wizard covers cluster through service exposure
- GPU and workload screens align inference with node capacity
- GitOps and registry integrations complete the delivery loop
How teams adopt it
- Standardize on wizard or GitOps paths for model rollouts
- Validate GPU and storage quotas per environment before promote
- Monitor inference KPIs alongside pod restarts and events
- Document rollback using the same deployment inventory
In Cloud Admin
What MLOps teams see in the product
Product screens with notes on when each view matters for your sector.
Deploy · Cluster
First wizard step—anchor the model to the right cluster and policy boundary.
- Validated cluster pick
- Policy-aware progression
- Fewer wrong-environment deploys
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Deploy · Resources
GPU and CPU allocation step—align requests with real pool headroom.
- Quota visibility
- GPU pool alignment
- Blocks oversubscription mistakes
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Deployed models overview
Post-deploy KPIs—operations view after go-live.
- Inference metrics
- Model inventory
- Bridge-friendly summaries
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Industry · Data platform engineering
Data platform engineering
Capacity and multi-cluster stacks for data platforms
Data platforms run heavy batch and stream workloads on Kubernetes—often many clusters per region. FusioNative shows capacity, namespace consumption, and fleet health so data infra teams scale before pipelines queue.
Plan headroom and operate multi-cluster data stacks with the same control plane as the rest of engineering.
Who needs this
- Data platform SREs and infrastructure engineers
- Owners of Spark/Flink/airflow-on-K8s style footprints
- FinOps partners allocating cost to data products
Industry pressures (why change)
- Batch spikes exhaust nodes but metrics are divorced from scheduler view
- Multiple analytics clusters duplicate tooling and blind spots
- Finance asks for chargeback data platform cannot produce quickly
Why FusioNative fits
- Capacity planning ties trends to limits and forecasts
- Namespace and pod analytics show top consumers
- Tenant metering supports chargeback to data products
How teams adopt it
- Inventory analytics clusters in the fleet view
- Review capacity forecasts before major pipeline releases
- Throttle noisy namespaces via quotas visible in tenant tools
- Export utilization narratives for FinOps monthly
In Cloud Admin
What Data platform engineering teams see in the product
Product screens with notes on when each view matters for your sector.
Capacity planning
Forecasts and headroom—plan nodes before Black Friday-scale batch jobs.
- Trend vs limit
- Scenario thinking
- Shared language with finance
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Namespace resources
CPU and memory by namespace—identify the job blowing the pool.
- Sorted consumers
- Quota alignment
- Faster triage
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Cluster dashboard
Cluster-level gauges for CPU, memory, pods, and nodes—ops snapshot for data sites.
- Single-cluster focus
- Node and pod counts
- Bridge-ready numbers
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Industry · Cloud-native startups
Cloud-native startups
Speed, cost visibility, and simple multi-cluster ops
Startups need one operator-friendly plane—not six dashboards held together with scripts. FusioNative gives founders and platform hires fleet visibility, autoscaling, and registry/GitOps basics before headcount scales.
Operate a growing K8s footprint with clear cost signals and provisioning that does not require a 10-person platform team.
Who needs this
- Founding engineers wearing platform and product hats
- First DevOps hire standardizing staging and production
- CTOs who need honest utilization for runway planning
Industry pressures (why change)
- Clusters multiply faster than runbooks
- GPU experiments blow the budget without per-team meters
- Hiring is slower than customer growth—tools must stay legible
Why FusioNative fits
- Single product for clusters, workloads, metrics, and deploys
- Autoscaling and capacity views catch overspend early
- Provisioning wizards reduce bespoke terraform glue
How teams adopt it
- Stand up staging and prod with the same navigation patterns
- Enable autoscaling policies before traffic spikes
- Review capacity weekly with leadership-friendly dashboards
- Add tenant boundaries when B2B customers need isolation
In Cloud Admin
What Cloud-native startups teams see in the product
Product screens with notes on when each view matters for your sector.
Enterprise overview
One screen for leadership and engineers—health, GPU, and workloads without a custom Grafana wall.
- Startup-friendly overview
- GPU and cost signals
- Shareable in investor updates
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Auto scaling
HPA, VPA, and KEDA in one place—grow with traffic without manual node hacks.
- Policy inventory
- Event context
- Scale before outages
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Cluster provisioning
Spin environments with guided provisioning—less time before first customer deploy.
- Repeatable clusters
- Version checks
- Fewer snowflake envs
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Industry · Internal developer platforms
Internal developer platforms
Tenants and self-service for engineering customers
IDP teams sell golden paths. FusioNative backs the portal story with real tenancy—projects, quotas, billing visibility, and audit—while platform keeps fleet and policy control.
Give product teams workspaces and meters; keep clusters and policy in platform hands.
Who needs this
- Internal developer platform product managers and engineers
- Portal teams integrating Backstage or custom UIs with real quotas
- Engineering directors measuring adoption and chargeback
Industry pressures (why change)
- Developers share admin credentials because self-service is missing
- Quota fights happen in Slack without objective meters
- Portal mockups do not match what operations can enforce
Why FusioNative fits
- Tenant portal with projects, quotas, billing, and audit
- Fleet and GitOps layers stay with platform operators
- Same product screenshots IDP teams can show in onboarding
How teams adopt it
- Define tenant orgs per business unit or product line
- Expose portal for projects and usage; keep cluster admin platform-only
- Connect GitOps templates to tenant namespaces
- Review audit and billing monthly with engineering leaders
In Cloud Admin
What Internal developer platforms teams see in the product
Product screens with notes on when each view matters for your sector.
Tenant overview
Per-tenant KPIs developers recognize—usage, quota, and workspace entry.
- Self-service entry
- Billing visibility
- GPU quota clarity
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Projects & workspaces
How teams organize services inside a tenant—matches IDP mental models.
- Project boundaries
- Workspace management
- Less ticket-driven access
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Resource quotas
Hard limits platform can enforce—objective end to quota arguments.
- Enforced limits
- Visible consumption
- Chargeback ready
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