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Event Streaming Platform - All the Power of Kafka, None of the Operational Tax

Durable. Replayable. Multi-tenant. Without a Confluent contract or a platform team.

Event streaming is foundational for modern apps - but standing up Kafka means standing up a platform team. Logiciel gives you Kafka-grade durability, sub-second latency, and exactly-once processing in a managed service that integrates natively with the rest of your data stack.

See Logiciel in Action

Your event streaming is one engineer's exit risk away from a crisis

If your event platform is held together by tribal knowledge:

  • One person can answer 'how do I add a new topic?' — and they're probably interviewing. Single-engineer key-person risk on the Kafka cluster is one of the highest-impact unmanaged risks in most US data infrastructures.

  • Your Kafka cluster is on a major version that's now in extended support. Major-version Kafka clusters in extended support are a regulatory and operational time bomb that's structurally avoidable.

  • Each new producer team writes their own retry, dedup, and DLQ logic from scratch. Producer-team-by-producer-team retry logic is duplication that the platform should absorb; the cumulative engineering cost is substantial.

If you're shopping event streaming platforms, you have real workloads

  • Multi-tenant streaming with per-team SLAs and quotas. Multi-tenant streaming with per-team SLAs requires platform support; ad-hoc multi-tenancy on shared Kafka rarely scales beyond a handful of teams.
  • Native integration with the data warehouse and lakehouse. Native warehouse and lakehouse integration eliminates the integration tax of stitching streaming and analytical infrastructure separately.
  • An operational model that doesn't require an SRE rotation. Operational models that don't require an SRE rotation are the structural advantage of managed event streaming versus self-hosted alternatives.

What you get with Logiciel

Event streaming with operational sanity.

  • Managed runtime — no Kafka clusters to upgrade, patch, or babysit. Managed runtime eliminates Kafka upgrade cycles, Zookeeper-to-KRaft migrations, and the operational debt that comes with self-hosted streaming infrastructure.
  • Multi-tenant — per-team topics, quotas, ACLs, and SLAs. Multi-tenancy with per-team topics, quotas, ACLs, and SLAs means teams ship without coordinating cluster operations.
  • Native data stack integration — stream-to-warehouse and stream-to-lake out of the box. Native data stack integration delivers stream-to-warehouse and stream-to-lake out of the box — no custom Kafka Connect maintenance.
  • Standards-friendly — Kafka API, Schema Registry, MirrorMaker compatible. Standards-friendly compatibility means existing Kafka tooling, schemas, and patterns transfer with minimal rework.

Where this fits - industries we serve in the US

FinTech & Financial Services

Trading data, risk models, regulatory reporting — sub-second SLAs and audit-ready governance.

PropTech & Real Estate

Listing data, transaction pipelines, geospatial analytics — multi-source consolidation.

Healthcare & Life Sciences

EHR integration, claims pipelines, clinical analytics — HIPAA-aware infrastructure.

B2B SaaS

Product analytics, customer 360, usage-based billing — embedded and operational data.

eCommerce & Marketplaces

Inventory, pricing, order, and customer pipelines — real-time and high-throughput.

Construction & Industrial Tech

IoT, project, and supply-chain data — operational analytics on hybrid stacks.

Engagement models that fit your stage

Dedicated Pod

Embedded data engineering pod aligned to your sprint cadence — typically 3–6 engineers + a US lead.

Staff Augmentation

Senior data engineers, architects, and SMEs slotted into your team to unblock specific work.

Project-Based Delivery

Fixed-scope, milestone-driven engagements with clear deliverables and outcomes.

From first call to first production pipeline

Discover

We map your stack, workloads, team, and constraints in a working session — not an RFP response.

Architect

Reference architecture grounded in your reality, with capacity, cost, and migration plans.

Build

Iterative implementation with weekly demos, code reviews, and your team in the loop.

Operate

Managed operations or knowledge transfer — your choice. Both with US-aligned coverage.

Optimize

Continuous tuning of cost, performance, and reliability against measurable SLAs.

Streaming capabilities

Kafka-Compatible API

Drop-in compatibility with existing producers/consumers.

Stream Processing

Stateful joins, windows, aggregations with exactly-once.

Schema Registry

Avro/Protobuf/JSON schema evolution with backward/forward compat.

Stream-to-Warehouse

Sub-minute freshness in Snowflake, Databricks, BigQuery.

Replay & Time Travel

Replay any window without disrupting current consumers.

Multi-Tenant Governance

Per-team quotas, ACLs, and audit.

Questions buyers ask before they book

Yes — for US teams that want managed event streaming without per-seat Confluent pricing or per-message bills that punish growth. Confluent is excellent at what it does, but the operational and cost model assumes you have a dedicated platform team and a healthy budget for a strategic platform investment. Logiciel delivers Kafka-grade durability, exactly-once processing, Schema Registry, and stream processing in a managed service typically 40-60% lower TCO than equivalent Confluent Cloud workloads. For mid-market customers without dedicated Kafka SREs, Logiciel is usually the right choice; for established Confluent customers, migration timing typically aligns with contract renewal cycles.
Per active stream plus storage volume — predictable at scale, contractually capped, with unlimited consumers. We don't charge per message (which makes Confluent Cloud bills feel arbitrary at high volume) or per consumer (which discourages teams from using streams). Mid-market customers (5-20 streams, moderate volume) typically pay $20-60K ARR. Enterprise tiers (50+ streams, high-volume, advanced governance, dedicated TAM, US-citizen support) start at $150K ARR. Storage pricing is tiered (hot vs cold) so historical retention doesn't punish your bill. Pricing is transparent with workload-grounded TCO comparison against Confluent Cloud at evaluation time.
Yes — native CDC connectors from Postgres (logical replication), MySQL (binlog), MongoDB (change streams), SQL Server (CDC), Oracle (LogMiner or Goldengate), and other databases. CDC events flow into Kafka-compatible streams with ordered, exactly-once delivery and full schema evolution support. This eliminates the typical 'two systems for CDC' pattern (Debezium for capture, separate platform for processing) — Logiciel handles both with shared observability and SLA management. CDC throughput scales with database load; we've supported customers running CDC at hundreds of thousands of events per second across multiple source databases. Schema evolution is policy-driven (auto-evolve, alert, block).

MirrorMaker 2 compatible cross-region replication with configurable topology — active-active for global low-latency, active-passive for DR, hub-and-spoke for centralized analytics. Replication latency is typically sub-second within a cloud provider, low single-digit seconds across cloud providers. Conflict resolution for active-active uses configurable strategies (last-writer-wins, application-driven). Disaster recovery configurations are tested in customer environments quarterly with documented RTO and RPO. For US customers running global products (e-commerce, FinTech, marketplaces), geo-replication is typically a critical requirement and we have reference architectures for major patterns. Cross-cloud replication is supported with appropriate egress cost transparency.

Yes — Kafka API compatible, so existing producers (Java, Python, Go, .NET clients) and consumers connect with minimal config changes (typically just bootstrap server URLs). Schema Registry compatibility means existing Avro/Protobuf/JSON schemas work as-is. MirrorMaker 2 compatibility supports cross-cluster replication. The drop-in compatibility means migration from self-managed Kafka or Confluent typically requires no application code changes — your engineering team's existing skills and code transfer directly. We provide migration tooling for topic export/import, consumer offset translation, and parallel cluster running during cutover. Most customers complete migration in 8-16 weeks with zero application downtime.
Native — including stateful operations (joins, aggregations, windows). No manual idempotency keys, no dedup tables, no transaction-replay nightmares. Exactly-once is the platform default; you have to opt out, not opt in. Stream processors integrate with transactional sinks (Snowflake, Databricks warehouse writes, transactional Postgres writes, downstream Kafka topics) so the exactly-once contract extends end-to-end. For US FinTech, billing-critical, and inventory-critical customers, we've passed independent third-party audits on the guarantee. The exactly-once contract is part of the platform SLA, not aspirational marketing copy. We document the contract precisely so audit and risk teams can validate it.

Built-in registry with backward, forward, and full compatibility modes — Avro, Protobuf, and JSON Schema all supported. Compatibility checks run in CI for schema changes, blocking deploys that would break consumers. Subject naming and grouping support multi-tenant patterns. Schema changes are versioned, auditable, and traceable to the producer team and the change rationale. For regulated customers, schema lineage and change history support audit evidence requirements. Compatibility with Confluent Schema Registry means existing schemas migrate without code changes. Custom serializers/deserializers are supported through standard interfaces — no proprietary plugin model.