// Partner
ProphetStor
Founded
2012
HQ
San Jose, CA / Taipei
Employees
50+
Total Funding
$10M
Status
Private
Website
VisitLeadership
Eric Chen (CEO)
Last Round
N/A
Key Investors
// Overview
ProphetStor develops software for managing AI compute infrastructure, including GPUs, Kubernetes and virtual machines. Federator.ai forecasts demand and recommends resource allocations; GPU Booster addresses GPU use for AI training and inference. Our Japanese pre-sales engineers evaluate supported hardware, GPU memory, throughput, latency and operating permissions with the organisation’s technical team. We measure the effect on that workload and provide Japanese support for the agreed configuration.
// Capabilities
- --Predictive AIOps
- --Multilayer Causal Analysis
- --Resource Optimization
- --Infrastructure Intelligence
- --GPU Utilization Optimization
- --Kubernetes Autoscaling
// Products
Federator.ai
AI powered Kubernetes and VM resource optimization with predictive autoscaling and cost management.
Federator.ai Cortex
Infrastructure monitoring and resource planning for GPU environments. Check the supported hardware, metrics and control integrations for the proposed deployment.
Federator.ai GPU Booster
GPU resource management for LLM training and inference. Evaluate memory use, throughput and behaviour under the customer’s workload.
Federator.ai Smart Liquid Cooling
Liquid-cooling control for supported GPU infrastructure. Verify hardware compatibility and measure cooling, energy and application performance.
// Use Cases
Click a use case to see matching deployments below
// Customers, partners and environments
// Routes to market
Buyer groups and sales partners for ProphetStor
ProphetStor’s potential buyers operate Kubernetes, virtual machines or GPU infrastructure; delivery partners may manage those systems for others. StrategyCore develops Japanese opportunities around the supported environment and tests application performance and operating cost before preparing a proposal.
Large Enterprise
initial focusEnterprises assessing resource contention or allocation across Kubernetes and AI infrastructure. Test application performance, stability and operating cost before proposing a change.
SaaS Companies
application-dependentSaaS platforms operating Kubernetes or GPU infrastructure. Measure response time, throughput and cost before and after allocation changes; savings depend on the workload.
SMEs / Mid market
specialist fitMid-market organisations operating Kubernetes or GPU workloads. Evaluate the resource-management problem, supported environment and total cost before deciding whether the product is a practical fit.
Government & Public Sector
initial focusResearch institutes, universities and public-sector AI teams operating GPU infrastructure. Evaluate supported jobs, resource allocation and power use under the actual operating conditions.
System Integrators
initial focusData-centre and cloud integration partners building AI infrastructure. Confirm supported hardware, control permissions and connections to existing management tools.
OEMs (Embedded)
specialist fitProphetStor is not typically embedded inside other vendor products. It is deployed as a management layer on top of customer infrastructure.
MSSPs
initial focusManaged cloud and AI infrastructure providers considering resource management for Kubernetes and GPU services. Check tenant performance requirements, permissions and cost reporting.
Distribution Partners
application-dependentJapanese infrastructure distributors carrying ProphetStor as part of their AI and cloud optimization portfolio.
// Case Studies
Case Studies
Case study content is aggregated from publicly available vendor sources. All trademarks and customer attributions belong to the original vendors.
// Competitive Landscape
How ProphetStor compares
ProphetStor forecasts demand and recommends or automates resource allocation for supported infrastructure. Compare forecast error, scaling behaviour, GPU job completion and cost against the existing operating baseline.
| Capability | ProphetStor | Densify | CAST AI | Datadog | Dynatrace | Kubecost |
|---|---|---|---|---|---|---|
| ML-driven predictive optimization | ● | ● | ● | ○ | ● | — |
| LLM inference resource optimisation (GPU Booster) | ● | — | — | — | — | — |
| GPU workload optimization | ● | ○ | ● | ○ | ○ | ○ |
| Kubernetes data platform optimization | ● | ● | ● | ● | ● | ● |
| Kubernetes autoscaling automation | ● | ● | ● | — | — | ○ |
| Multi-layer causal analysis | ● | ○ | — | ● | ● | — |
| Data center power and cooling intelligence | ● | — | — | — | — | — |
| Multi-cloud and hybrid support | ● | ● | ● | ● | ● | ● |
// Competitor Reference
Densify
Cloud cost optimizationMachine learning cloud resource right-sizing.
CAST AI
Kubernetes cost optimizationAutomated Kubernetes cost and performance management.
Datadog
ObservabilityMonitoring, logging, and observability platform.
Dynatrace
APM and AIOpsApplication performance monitoring with AI-assisted root cause analysis.
Kubecost
Kubernetes cost visibilityOpen source Kubernetes cost allocation and monitoring.
Spot by NetApp
Cloud cost optimizationContinuous cloud infrastructure optimization with automated scaling.
This table summarises product positioning from public vendor information. Confirm the edition, supported features and deployment requirements for your evaluation. Trademarks belong to their respective owners.
// Benchmarks
ProphetStor by the numbers
GPU Efficiency
2x improvement
GPU utilization doubled with 48% faster job completion
Federator.ai GPU BoosterCloud Cost Savings
35-90%
Vendor-reported field range; compare savings with the current workload and purchasing model.
Federator.aiCooling Energy
30% savings
Smart liquid cooling with 45% GPU performance boost
ProphetStor// Hosting and data access
Review the telemetry used for resource planning.
Define which workload metrics ProphetStor collects, where they are processed and how scaling changes are approved and recorded.
Hosting, data access and operating responsibilities for the proposed deployment.
See the sovereignty approachIdentify collected metrics, service connections and retention periods.
Compare resource use and application performance against the current baseline.
Agree access, update, support and rollback procedures.
// Latest Videos
// Recent News
Bring your infrastructure software to Japan.
Our Tokyo team develops enterprise prospects and Japanese channel relationships for infrastructure vendors. We coordinate demonstrations using the prospect’s workloads, lead local commercial discussions and agree licensing, deployment support and escalation to your engineers.