Ingest. Store. Govern. Serve. Without seven contracts and four SaaS dashboards.
You don't need another cloud data platform — you need one that doesn't fight your existing stack. Logiciel runs on AWS, Azure, and GCP, integrates with Snowflake, Databricks, Redshift, and BigQuery, and gives your team a single layer for ingestion, transformation, governance, and serving — without ripping out what's working.
But here's what most US data teams are actually living with:
Most teams searching this don't need 'best in class' — they need 'works in our org':
A platform that respects your stack and your team.
Multi-cloud native — runs in your VPC, your account, your compliance perimeter. Multi-cloud native deployment means data and compliance perimeters stay aligned with your existing architecture, not the platform vendor's preferences.
Connector-rich — 200+ pre-built integrations with the tools you already pay for. Connector richness means time-to-first-value is days, not quarters — every integration that already works in your existing stack continues to work.
Engineer-friendly — Terraform-first, Git-native, Python/SQL-first, no proprietary DSLs to learn. Engineer-friendly tooling reduces the platform's adoption curve from 'multi-quarter training program' to 'first sprint productive.'
Predictable pricing — pipelines and storage, not per-query roulette. Predictable pricing means budget conversations happen quarterly, not after every unexpected spike.
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.
CDC, batch, and streaming from databases, SaaS apps, files, and APIs.
Native integration with S3, ADLS, GCS — bring your own storage tier.
dbt-compatible, Python-native, with built-in lineage and testing.
Auto-cataloged datasets, column-level lineage, RBAC, and audit.
API, BI, and reverse-ETL endpoints from one governed source.
Pipeline health, freshness, and cost — all in one console.
Neither — and that's a feature, not a bug. Snowflake and Databricks are warehouses (and Databricks is also a lakehouse compute engine). Logiciel is the platform layer that sits in front of and around them: ingestion, transformation orchestration, observability, governance, cost telemetry. We accelerate management on top of the warehouse you've already standardized on, so you don't re-platform every time your warehouse vendor strategy shifts. Customers running on Snowflake report 25-40% faster pipeline shipping and 20-35% compute cost reduction; the same outcomes apply on Databricks, Redshift, and BigQuery. Pick your warehouse for compute and storage; pick Logiciel for everything around it.
Per active pipeline plus storage volume — predictable, contractually capped, with unlimited users. We don't charge per query (which makes Snowflake bills feel arbitrary), per row (which punishes you for ingesting your own data, like Fivetran), or per seat (which discourages your team from collaborating). Typical mid-market US engagements start at $30-60K ARR for 50-100 active pipelines; enterprise tiers with advanced governance, custom SLAs, and dedicated TAM scale from there. We publish pricing tiers transparently and provide a workload-grounded TCO at evaluation, including comparisons to your incumbent stack.
SOC 2 Type II, HIPAA, GDPR, and CCPA covered out of the box. Customer-managed encryption keys (CMKs) via AWS KMS, Azure Key Vault, or GCP Cloud KMS. Optional in-VPC and air-gapped deployment for FedRAMP-aligned and regulated finance scenarios. SSO via SAML/OIDC, SCIM provisioning, and field-level RBAC. Audit logs are immutable, exportable, and integrate with your SIEM (Splunk, Datadog, Elastic). For US healthcare customers, we sign BAAs and configure HIPAA-compliant deployments by default. EU AI Act readiness (model cards, lineage, evaluation logs) is built into the AI infrastructure layer.
Our 30-day onboarding is built for exactly that scenario. Most teams adopting Logiciel are not greenfield — they're 2-5 years into a cloud migration with mounting tooling debt, but their data engineers may have come from on-prem backgrounds (Teradata, Hadoop, traditional BI). We pair every customer with a US-based implementation engineer plus hands-on enablement: pipeline patterns workshop, governance setup walkthrough, cost telemetry tour, and incident response runbook. After 30 days, customers should have 10-20 production pipelines, a working observability dashboard, and a team that can ship without Logiciel-engineer involvement on routine work.
AWS, Azure, and GCP — all three, equally well, with native services on each. Logiciel deploys into your account so your data never leaves your perimeter; the control plane can be managed SaaS, customer-managed in your VPC, or fully air-gapped on-prem for regulated workloads. Most US mid-market customers run on AWS; financial services and healthcare lean Azure; AI-native scale-ups often choose GCP for Vertex AI proximity. Multi-cloud customers (about 30% of our base) run a unified Logiciel control plane across clouds with native data planes per region — useful for residency, DR, and acquisition consolidation scenarios.
Most teams ship their first production pipeline within 2 weeks. Day 1-3: connect sources and warehouse. Day 4-7: first ingestion pipeline running with observability and lineage. Week 2: governance, access control, and cost telemetry configured. By the end of 30 days, customers typically have 10-20 pipelines under management and a quantified baseline for cost and reliability. Enterprise rollouts (multiple business units, regulated environments) take longer — 90 days to first BU live, 6-9 months for full rollout — but core capability is operational from week 2 regardless of org size.
Yes — sub-second latency for streaming workloads with native Kafka, Kinesis, and Pub/Sub integration, plus exactly-once semantics across stateful operations. Logiciel handles streaming and batch in the same orchestration model, so you don't run two parallel architectures. Real-time use cases we ship regularly: personalization features for B2C apps, fraud detection pipelines for FinTech, inventory and pricing updates for marketplaces, and feature stores for ML inference. For sub-100ms requirements (ad-tech, high-frequency), we recommend evaluating us against specialized streaming platforms — but for the 99% of US enterprise streaming use cases, sub-second is sufficient and operationally far simpler.
Spin up a sandbox in your own AWS, Azure, or GCP account. No credit card. No replatforming. See whether Logiciel actually fits before you commit a dollar.
