Machine Learning Infrastructure Platform Built for Production, Not Notebooks.

No — we focus on data infrastructure for ML (features, embeddings, training data, inference data) and integrate with model lifecycle tools (Vertex AI, SageMaker, MLflow, Weights & Biases, Comet, Anyscale). The line between ML data infrastructure and MLOps is blurry; we deliberately stay on the data side because that's where most production AI failures originate. Most customers run Logiciel for the data side and one of the major MLOps platforms for model lifecycle (training, registry, deployment). For customers without an MLOps platform, we have integrations and reference architectures to set one up. We're explicitly not trying to be SageMaker.

We have Feast-compatible APIs, so existing Feast users can adopt Logiciel without code rewrites. Many of Tecton's capabilities — point-in-time correctness, online/offline serving, feature versioning — are first-class in Logiciel. The differentiator: Logiciel's feature store integrates with the broader data infrastructure (catalog, lineage, observability, governance), so features are governed assets, not islands. For US customers running Tecton today, we provide TCO comparisons that typically show 30-50% savings; for Feast users, we offer managed operations on top of the open-source primitives they're already using. We don't recommend replacing a working Tecton install if features are stable.

First-class assets — versioned, observable, indexed in your vector DB of choice (Pinecone, Weaviate, pgvector, Qdrant, Milvus, Chroma, OpenSearch). Embedding generation pipelines are managed assets with full lineage from source documents through chunking, embedding model invocation, and vector indexing. Embedding model upgrades trigger versioned retraining workflows; old embeddings sit alongside new until evaluation passes; only then do you cut over consumers. This avoids the silent failure mode where an embedding upgrade improves average quality but breaks specific edge cases. Embedding cost telemetry is integrated so you can see actual $/embedding across providers (OpenAI, Cohere, Voyage, self-hosted).

Production RAG pipelines: chunking, embedding, retrieval, evaluation — all observable assets with versioning and lineage. Common patterns: document ingestion (PDF, HTML, structured data) → chunking strategies (semantic, fixed-size, hierarchical) → embedding generation (multiple providers supported) → vector indexing → retrieval with reranking → generation with optional eval. Each stage is a managed asset with SLAs and observability. Eval pipelines (Ragas, TruLens, DeepEval, custom) integrate as observable assets with results trackable over time. For regulated AI customers (EU AI Act, emerging US AI rules), the lineage and eval evidence supports auditability requirements that pure RAG demos can't satisfy.

Both — same feature definitions for training and serving, eliminating training-serving skew that's a leading cause of production model failure. Online serving via REST/gRPC API with sub-100ms latency for typical features; offline serving via warehouse SQL or Python notebook for training. Feature definitions are point-in-time correct, so training data assembly respects historical reality (no 'leakage from the future'). For US customers with mixed online and offline ML use cases, the unified feature definition eliminates a class of bugs that's notoriously hard to debug in pure offline-first or online-first architectures. We support both batch and streaming feature pipelines.

Feature drift, prediction drift, and ground-truth drift are all observed and alerted with statistical tests appropriate to each (KS test for continuous distributions, chi-square for categorical, JS divergence for general distribution shifts). Drift detection is per-feature and per-model with configurable severity thresholds. Alerts route through lineage-aware channels — when an upstream data source drifts, the upstream owner and downstream model owners are notified simultaneously. For customers with regulatory model risk requirements (financial services SR 11-7, EU AI Act high-risk systems), drift evidence supports model validation and post-market monitoring requirements. Drift monitoring is included in the platform, not a separate SKU.

Per active feature/embedding pipeline plus storage volume — predictable at scale, with unlimited model serving consumers. Mid-market customers (50-200 features, moderate online serving volume) typically pay $40-90K ARR. Enterprise tiers (1,000+ features, high-volume online serving, advanced governance, dedicated TAM, US-citizen support) start at $200K ARR. Compute is your cloud bill, passed through at cost; we don't markup compute. Online serving traffic is metered separately at low per-request rates. Pricing is transparent with workload-grounded TCO comparison against Tecton or self-managed Feast at evaluation. For Tecton replacement scenarios, savings are typically 30-50%.