For financial services
GPU compute and LLM platforms built for FSA-supervised and compliance-heavy organizations — engineered for the audit rights, data residency, and data-handling standards your institution answers for, not adapted to them afterwards.
Compliance documentation — including per-workload FISC control mappings — is available under NDA.
The compliance gap
Under FSA supervision, your institution answers for its vendors. Generic public-cloud terms were not written with that accountability in mind — and four gaps surface in every vendor assessment.
Supervisory guidelines expect you to verify — and audit — how an outsourced provider handles your data. With hyperscalers, audit rights typically require complex bilateral negotiation and are rarely granted on standard terms.
Prepared whitepapers describe how standard services relate to FISC controls in aggregate. Mapping those controls onto your actual workloads falls to your compliance and risk teams — commonly a multi-month internal exercise for a mid-tier bank.
Redundancy, performance optimization, and disaster recovery routinely move data across regions — so every region where data may land becomes its own APPI and supervisory due-diligence exercise.
Service credits and availability notices do not map to FSA incident-reporting duties — structured communication to the regulator and coordination with your CSIRT.
What Tara Cloud provides
On Tara Cloud-hosted infrastructure, the controls your examiners look for are built into contracts and architecture — not left to configuration.
GPU compute and open-weight LLM inference run on Tara Cloud-hosted infrastructure located in Japan. For regulated workloads there is no cross-border transfer — a contractual commitment.
Customer prompts and results are not persisted, and customer data is not used for model training — enforced by infrastructure design, not by policy alone.
On-demand audit rights written into the agreement, with access to control documentation and subcontractor supervision records.
Tara Cloud delivers control documentation aligned with the FISC safety guidelines for each workload — so your teams review the mapping instead of building it.
Incident communication is structured around FSA reporting workflows and CSIRT coordination — not generic service credits.
Every service arrives on a single JPY invoice — one vendor, one contract, no foreign-currency exposure.
Open-source models you fully control, plus the major global models — selected per workload through one API. Your architecture stays portable across both ecosystems.
Portability and exit documentation ships as part of the contract — the concentration-risk answer your supervisors increasingly ask for.
Data leaving Tara Cloud-hosted infrastructure to your environment costs nothing extra — predictable total cost, end to end.
Tara Cloud vs alternatives
Five requirements that surface in vendor assessments for supervised institutions — and how each class of provider answers them.
Tara Cloud
Contractual, on demand
Generic public cloud
Negotiated case-by-case, rarely on standard terms
Domestic GPU providers
Varies by provider and contract
Tara Cloud
Delivered per workload
Generic public cloud
Generic whitepaper only
Domestic GPU providers
Documentation scope varies by provider
Tara Cloud
Contractual, on Tara Cloud-hosted infrastructure
Generic public cloud
Multi-region movement by default
Domestic GPU providers
Japan-based; contractual scope varies by provider
Tara Cloud
All services, one vendor
Generic public cloud
Multiple vendors, commonly USD-denominated
Domestic GPU providers
JPY GPU billing; LLM services often a separate vendor
Tara Cloud
Open-weight and major global models, one API
Generic public cloud
Model services tied to each ecosystem
Domestic GPU providers
Developer and research focus; enterprise model services vary
Zero-persistence architecture
Inference runs on stateless infrastructure that scales to zero when idle. Observability is metrics-only — prompts and completions are never logged. Optional GPU confidential computing (TEE) keeps workloads protected even while in use.
Session state stays in ephemeral memory — discarded on teardown, never written to storage.
Idle capacity is released rather than kept running.
Health and performance signals — no payload logging.
Optional hardware-level protection for data in use.
Products for this segment
Dedicated deployments or shared open-weight endpoints — every product runs on the same Japan-resident platform.
A private, dedicated open-source LLM with a production API — completely isolated, billed per GPU-second with limits you set.
Your containerized workloads on managed Kubernetes — scheduled, scaled, and documented for enterprise operations.
Dedicated GPU servers in data centers across Japan — full control of the hardware layer for your most sensitive workloads.
Prefer to call an API? Shared open-weight endpoints are available on the same platform. See the LLM API Endpoint
Tell us about your workloads, regulatory constraints, and timeline. A senior member of our team — familiar with FSA-supervised environments — will respond within one business day.