AWS. Azure. GCP. One control plane. One bill view. One sane day.
Most US enterprises don't run on one cloud - they run on whichever cloud their last acquisition brought, plus the one their AI team picked, plus the one finance pre-paid for. Logiciel gives you one cloud infrastructure management platform across AWS, Azure, and GCP - without forcing consolidation.
If you're paying the multi-cloud tax without the multi-cloud benefits:
Teams arriving here typically:
Run data workloads across at least 2 of AWS, Azure, GCP. Single-cloud thinking falls apart when acquisitions, residency, or AI workload preferences pull workloads to different clouds.
Need a unified FinOps view to defend cloud spend to the CFO. CFO defense for cloud spend requires unified attribution; per-cloud reports leave gaps that finance teams legitimately reject.
Need a single governance and compliance plane - without forcing migration. Single governance plane across clouds is the only architecture that doesn't force migration before solving the immediate compliance need.
Multi-cloud without the multi-cloud overhead.
Trading data, risk models, regulatory reporting - sub-second SLAs and audit-ready governance.
Listing data, transaction pipelines, geospatial analytics - multi-source consolidation.
EHR integration, claims pipelines, clinical analytics - HIPAA-aware infrastructure.
Product analytics, customer 360, usage-based billing - embedded and operational data.
Inventory, pricing, order, and customer pipelines - real-time and high-throughput.
IoT, project, and supply-chain data - operational analytics on hybrid stacks.
| Dedicated Pod | Staff Augmentation | Project-Based Delivery |
|---|---|---|
| Embedded data engineering pod aligned to your sprint cadence - typically 3–6 engineers + a US lead. | Senior data engineers, architects, and SMEs slotted into your team to unblock specific work. | Fixed-scope, milestone-driven engagements with clear deliverables and outcomes. |
We map your stack, workloads, team, and constraints in a working session - not an RFP response.
Reference architecture grounded in your reality, with capacity, cost, and migration plans.
Iterative implementation with weekly demos, code reviews, and your team in the loop.
Managed operations or knowledge transfer - your choice. Both with US-aligned coverage.
Continuous tuning of cost, performance, and reliability against measurable SLAs.
Unified spend, attribution, forecasting, anomaly detection.
Right workload, right cloud - driven by cost, latency, compliance.
Auto-collected SOC 2, HIPAA, ISO evidence across clouds.
Cross-cloud policy authoring with native enforcement.
Cross-cloud RI/SP optimization.
Cross-cloud DR strategy and failover orchestration.
No - we sit on top, giving you one management plane while keeping native cloud services intact. AWS, Azure, and GCP all do compute, storage, networking, and security excellently; replacing any of them is foolish and we don't try. What we replace is the cross-cloud chaos: separate consoles, separate billing, separate IAM, separate compliance evidence, separate ops teams. Logiciel provides unified visibility, policy enforcement, FinOps, and operations across whichever clouds you're on. Native services (S3, Glue, Synapse, BigQuery, IAM, GuardDuty) remain as the underlying primitives; we add the connective tissue and governance layer that the cloud providers don't ship across one another's boundaries.
EKS, AKS, GKE, and on-prem Kubernetes are all supported with unified visibility and policy enforcement. We provide one console for cluster management, workload placement, cost attribution, and policy compliance across all your K8s environments. For data workloads on K8s (Spark on EKS, ML training on GKE, ingestion pipelines on AKS), we add data-aware monitoring on top of standard K8s observability. Common patterns: data engineering teams running Spark on EKS with Logiciel orchestration; ML teams running GPU workloads on managed GKE; multi-tenant K8s for cost-sharing across business units. We don't replace native K8s tooling (kubectl, Helm, Argo) — we federate across clusters.
We surface egress transparently in the FinOps view and recommend workload placement and data architecture patterns to minimize unnecessary egress. Cross-cloud egress is one of the most-overlooked cost drivers; many multi-cloud architectures accumulate egress charges that exceed compute spend. Logiciel maps your workload's data flows, identifies egress hotspots, and suggests remediations: data plane localization, regional caching, federated query with push-down, or workload colocation. For customers with structural egress costs (e.g., serving global customers from a single region), we model the trade-offs explicitly so the architecture decision is informed, not accidental.
Yes - for both AWS GovCloud and Azure Government, with active references in US Federal-adjacent customers. FedRAMP Moderate and High deployments are supported; we maintain a US-citizen-only engineering pool for customers with citizenship requirements. CMMC-aligned deployments for defense industrial base customers are available. We don't currently hold an autonomous FedRAMP authorization (the timeline and cost are non-trivial), but we operate under customer FedRAMP boundaries with documented inheritance of controls. For DoD-impact level 5 workloads, we work with prime contractors who hold the authorization. References available under NDA at the appropriate clearance level.
Yes - many of our customers run hybrid configurations, particularly regulated industries (financial services with on-prem mainframes, healthcare with on-prem EHR systems, government-adjacent with classified networks). Hybrid customers typically run our control plane in cloud while data planes execute in-region or in-DC, so sensitive data never leaves your perimeter. Common patterns: cloud-burst for analytical workloads with on-prem operational data, gradual cloud migration with parallel running, multi-region with on-prem secondary, and disaster recovery across cloud and on-prem. We have references in financial services and healthcare with active hybrid deployments at Fortune 500 scale.
Yes - most customers start with one cloud (typically their primary) and add multi-cloud capability as needs grow. Logiciel is just as useful single-cloud as multi-cloud - the FinOps, governance, and operational benefits don't require multi-cloud to deliver value. Customers often add a second cloud after an acquisition, a regulatory requirement (regional residency), an AI workload that fits better on a different cloud (Azure for OpenAI, GCP for Vertex), or simply a strategic decision to avoid vendor lock-in. We don't push multi-cloud; we make it operationally feasible if and when you decide it's right. Single-cloud customers represent about 60% of our base.
Yes - 2-week multi-cloud assessment with a costed remediation report, fixed-fee at a starting price that's typically <1% of annual cloud spend. Output includes: current-state inventory across AWS/Azure/GCP, top 5-10 cost optimizations with quantified savings (typically 15-30% of cloud spend), top 3-5 governance and security risks, multi-cloud strategy recommendation, and a phased remediation roadmap. About 60% of assessment customers proceed to implementation; the other 40% take the report in-house, which is fine - the assessment stands alone. The assessment is creditable against implementation if you proceed within 90 days.
2-week assessment. We map your current spend across AWS, Azure, GCP, identify the top 5 cost optimizations, and surface the top 3 governance risks - defensible to the CFO.
