Sub-second queries on streaming data. Live dashboards. Embedded analytics. Operational insights.
When 'real-time' is a product feature - not just an internal dashboard - your analytics platform has to work differently. Logiciel's real-time analytics platform delivers sub-second query latency on streaming data, with the ergonomics of a modern data warehouse.
Common patterns:
Teams here typically need:
Sub-second queries on freshly streamed data. Sub-second queries on streaming data require purpose-built infrastructure; warehouses don't serve this workload well, regardless of marketing claims.
Embedded analytics - multi-tenant, isolated, performant. Embedded analytics with multi-tenant isolation requires platform support for per-tenant query, storage, and quotas - not just connectivity.
Operational latency budgets defensible to a product manager. Operational latency budgets defensible to a PM are a product requirement, not a nice-to-have; the platform shapes feasibility.
Real-time analytics that fits your stack.
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.
Kafka, Kinesis, Pub/Sub native.
Tenant-aware query, storage, and quotas.
Hot and cold tiers for cost optimization.
p99 query latency under 1 second at scale.
Drop-in dashboards and charts for your product.
Per-tenant query SLAs measured and reported.
Same query primitives (sub-second OLAP on streaming data, multi-tenant isolation), but managed runtime, tighter integration with broader data stack, and better TCO at scale. Druid, Pinot, and ClickHouse are excellent open-source engines for this workload class - but running them well requires specialized operational expertise (cluster sizing, segment management, ingestion tuning) that most US data teams don't have. Logiciel provides equivalent query capability with managed operations, integrated observability, and unified data infrastructure (so the real-time analytics layer isn't a separate operational story from your warehouse). For ClickHouse-heavy customers, we provide TCO comparisons including operational overhead.
Different latency target. Snowflake and Databricks are excellent at batch-OLAP - sub-second-to-seconds for analytical queries on warehouse data. Logiciel serves sub-second user-facing queries on streaming data with high concurrency. For batch analytics, your warehouse wins. For embedded real-time analytics in a product, Snowflake/Databricks aren't built for that workload pattern - concurrency scaling, query-level cost predictability, and tenant isolation are all different problems. Most US customers run both: Snowflake for analytical workloads, Logiciel for user-facing real-time. The two systems complement rather than compete; don't try to force a warehouse to do real-time OLAP at scale.
Tiered storage (hot in-memory for recent data, cold object storage for historical) and concurrency-aware compute keep costs predictable. Hot tier serves sub-second queries; cold tier serves historical queries with slightly higher latency (typically 1-5s p99). Tiering policies are configurable per dataset - you choose how much hot retention is worth the cost. For multi-tenant SaaS, per-tenant cost attribution lets you see which tenants drive disproportionate compute (often a few enterprise customers); chargeback workflows are supported. Customers report 30-50% cost reduction versus self-managed Druid or ClickHouse when operational overhead is included; pricing is transparent with TCO comparisons.
Per tenant plus per query volume - predictable for embedded analytics where you know your customer count and rough query patterns. Mid-market customers (10-50 embedded tenants) typically pay $40-100K ARR. Enterprise tiers (500+ tenants, high-volume queries, dedicated TAM, US-citizen support) start at $250K ARR. For customers building embedded analytics into a SaaS product, the per-tenant pricing aligns with your revenue model - your bill scales with your customers' usage. Pricing is transparent with workload-grounded TCO comparison against ClickHouse Cloud, Tinybird, or self-managed Druid at evaluation. For high-volume use cases, savings are typically 30-60%.
Both - but the multi-tenant story is built specifically for embedded analytics products. Internal dashboards work fine but the differentiated value is at embedded scale: per-tenant query isolation, tenant-aware quotas, data residency enforcement, white-label dashboards, and SLAs measurable per tenant. For US B2B SaaS companies shipping analytics as a product feature, the multi-tenant primitives matter - because internal-dashboard tools collapse at embedded scale. For internal dashboards (executive analytics, ops dashboards), the same platform works but you're paying for capability you don't need; we'll recommend a simpler tier in those cases.
Yes - most customers run both, and the architecture is intentional. Warehouse handles the analytical workload (BI dashboards, ad-hoc analysis, ML training data, executive reports); Logiciel handles the user-facing real-time workload (embedded dashboards, live operational features, sub-second product analytics). Data flows naturally: streaming events land in Logiciel for real-time, batch ETL into the warehouse for analytical. The two systems share governance, lineage, and observability through Logiciel's broader platform - you don't run a separate observability tool for the real-time analytics layer. For US B2B SaaS customers, this dual-architecture pattern is increasingly standard.
Native streaming and batch - Kafka, Kinesis, Pub/Sub, plus warehouse and lake source connectors for batch loads. Sub-minute end-to-end freshness from event production to query availability. Streaming ingestion handles backpressure automatically; batch ingestion is bounded to budget caps. For high-volume customers (millions of events per second), we've supported real-time analytics workloads ingesting hundreds of thousands of events per second with sub-second query latency. Schema evolution is automatic with policy-driven routing (auto-evolve, alert, block). Most customers connect their existing event streams to Logiciel and add ingestion-specific transformations for analytics-ready event shapes.
Bring your hardest user-facing query. We'll benchmark it on Logiciel and show you the latency, cost, and scaling profile - vs your current setup.
