Snowflake. Databricks. BigQuery. Redshift. Logiciel makes them work like one system.
You probably don't need another data warehouse. You need everything around the warehouse to stop being a mess. Logiciel is the data warehouse software layer that plugs into Snowflake, Databricks, BigQuery, or Redshift and turns it into an end-to-end system: ingestion, transformation, governance, observability, and cost control - all governed in one place.
Most US data teams shipping on Snowflake or Databricks are quietly drowning in:
You've already picked your warehouse. Now you're hitting the real problems:
The system around your warehouse - unified
Warehouse-agnostic - same pipelines, same governance, same observability across Snowflake, Databricks, BigQuery, Redshift. Warehouse-agnostic operations let you make warehouse decisions independently of your tooling investment, which protects optionality as your strategy evolves.
Cost telemetry - every query, model, and pipeline tagged to a team, an SLA, and a dollar amount. Cost telemetry tagged to teams, models, and SLAs turns FinOps from a periodic exercise into a continuous discipline that scales with the team.
dbt-native - your existing dbt project works as-is, with added lineage, testing, and observability. dbt-native integration means existing investment in dbt doesn't get displaced; it gets enhanced with the lineage and observability dbt was always missing.
AI/ML-ready - feature stores, vector search, and model serving on top of the same warehouse. AI/ML-ready features on top of the same warehouse means data science adoption doesn't require a parallel infrastructure decision.
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.
Native connectors and tuning for Snowflake, Databricks, BigQuery, Redshift.
Query, model, and warehouse-level cost attribution and recommendations.
Multi-tenant warehouse partitioning, queue management, autoscaling.
dbt, Python, Spark - orchestrated with shared lineage.
Incremental, snapshot, streaming - pick the right pattern automatically.
Feature engineering, vector indexes, and model serving on warehouse data.
No - and we don't want to be. Snowflake, Databricks, BigQuery, and Redshift are excellent warehouses, each strong on different workloads. Logiciel is the management layer around the warehouse you already have or are evaluating: ingestion, transformation orchestration, governance, observability, cost telemetry. We sit between source systems and your warehouse on the inbound, between your warehouse and BI/ML/reverse-ETL on the outbound, and across the warehouse itself for cost and reliability management. By staying out of the warehouse business, we stay neutral on the warehouse choice - which means we can honestly recommend the right warehouse for your workload, not the one that pays our highest partner tier.
We can run a workload-grounded TCO analysis - taking your top 50-100 actual queries and benchmarking them on Snowflake, Databricks, BigQuery, and Redshift with realistic compute sizing. Output is an honest TCO comparison (compute, storage, egress, operational lift) and a capability-fit assessment. About half of customers who consider switching end up staying - the perceived savings don't survive workload analysis once the migration cost and operational disruption are factored in. The other half migrate, and Logiciel runs the migration with parity testing so reports match cent-for-cent across the cutover. Many customers stay multi-warehouse permanently for different workload classes.
We complement them, never compete. Snowflake's compute, storage, security, and SQL engine are excellent - Logiciel doesn't try to replicate any of that. We provide the connective tissue Snowflake doesn't: source-system ingestion (Fivetran-class), pipeline orchestration (Airflow-class), observability (Monte Carlo-class), governance (Atlan-class), cost telemetry (Select-class), and feature stores (Tecton-class) - unified into one platform. For Snowflake-heavy customers, Logiciel typically replaces 4-6 point tools surrounding the warehouse, lowering total TCO 30-50% while improving operational clarity. Snowflake's own ecosystem (Snowpark, Cortex, Streamlit) is fully supported and we lean on it where appropriate.
Limited - we focus on cloud warehouses (Snowflake, Databricks, BigQuery, Redshift) but can integrate with on-prem Teradata, Vertica, Netezza, Oracle Exadata, and SQL Server during a migration period. Most on-prem warehouse customers come to us specifically because they're modernizing - running parallel for 6-18 months while Logiciel manages both the legacy on-prem warehouse and the cloud target, with parity testing across reports. After cutover, we retire the on-prem connection. We don't recommend Logiciel for customers planning to stay fully on-prem indefinitely; the value proposition is strongest in cloud and hybrid scenarios.
No - and we tune for whichever you've standardized on. Our customer base runs roughly 50% Snowflake, 25% Databricks, 15% BigQuery, 10% Redshift, with a small Iceberg-on-S3 segment growing fast. Each warehouse wins for different patterns: Snowflake for SQL-heavy enterprise analytics, Databricks for ML-adjacent and Spark workloads, BigQuery for ad-tech-style aggregation at scale, Redshift for AWS-deep stacks. We optimize Logiciel for whichever you run, including warehouse-specific features (Snowpark, Delta Live Tables, BQ ML, Redshift ML) where they matter. We will tell you honestly when your workload would run better on a different warehouse, but we don't push migration unless the savings are material.
Drop-in. We add lineage, testing, observability, and cost telemetry without changing your dbt project structure or how your team writes SQL. Logiciel orchestrates dbt runs natively (no Airflow shim required), surfaces dbt tests in a unified pipeline view, and extends dbt's column-level lineage into upstream ingestion and downstream BI. dbt Cloud customers can keep dbt Cloud for development workflows and use Logiciel for production orchestration; dbt Core customers get a managed dbt runtime included. About 80% of our customer base runs dbt - we treat it as a first-class citizen, not a workaround.
Both - pricing tiers and feature scope flex to the size of your data team. Mid-market customers (5-30 data engineers, 50-200 pipelines) typically pay $30-80K ARR for the full platform with self-serve onboarding. Enterprise customers (50+ data engineers, multi-BU, regulated) start around $150K ARR and scale up with dedicated TAM, advanced governance, custom SLAs, and US-citizen engineering pools. The platform is the same; what flexes is the support model and the governance depth. Customers often start mid-market and upgrade to enterprise as their footprint grows - we don't gate core capability behind enterprise tier the way some competitors do.
We'll review your current warehouse footprint, identify the top 3 cost and reliability risks, and show you how Logiciel layers on top - no replatforming required.
