Dataiku vs Langfuse

Discover Dataiku Agent Management: every agent on the record

Dataiku announced the launch of Dataiku Agent Management, a new product feature for managing AI agents, along with the announcement of the AI Success Frontrunner award winners.

12,000 Attend Ai4 2026 to Discuss AI's Invisibility

The article reports on the Ai4 2026 conference in Las Vegas, which saw record attendance of over 12,000, up from 8,000 the previous year. Dataiku's keynote highlighted that 96% of enterprise leaders believe employees are using unsanctioned generative AI tools, and 80% of CIOs see their jobs at risk without measurable AI ROI. Pat Gelsinger argued that AI economics must improve dramatically, while Geoffrey Hinton, Fei-Fei Li, and Andrew Ng jointly endorsed AI regulation. The conference emphasized the need for AI governance, budget ownership, and cross-functional staffing. Speakers from Cisco, Nvidia, Uber, PayPal, and other companies discussed the shift from assistants to autonomous agents and the importance of accountable AI deployment.

• Ai4 2026 attendance exceeded 12,000, up from 8,000 the previous year.
• Dataiku's survey found 96% of enterprise leaders believe employees use unsanctioned generative AI tools.
• 80% of CIOs say their job is at risk without measurable AI ROI.

Governed RAG: Data, Context & Lineage for Enterprise AI

The article describes risks introduced by Retrieval-Augmented Generation (RAG) when enterprise data is exposed to vector search pipelines and proposes a three-part Governed RAG architecture: (1) ingestion with cryptographic embedding lineage and metadata, (2) query-time contextual Attribute-Based Access Control (ABAC) embedded into vector search queries, and (3) outbound payload sanitization (PII/PHI masking, indirect injection removal, and context length minimization). It argues that enterprises must enforce retrieval-time access controls, maintain graph-based data lineage, and implement real-time index freshness/eviction to prevent privilege escalation, prompt-injection attacks, stale-context hallucinations, and to meet compliance requirements.

• Retrieval-Augmented Generation (RAG) pairs LLMs with vector databases and knowledge graphs to ground agents in proprietary corporate knowledge.
• Vector stores typically do not preserve fine-grained document-level ACLs or cryptographic data lineage by default, creating over-permissioned retrieval risks.
• The proposed Governed RAG pipeline has three security boundaries: ingestion with cryptographic embedding lineage, query-time contextual ABAC inside the vector search, and outbound payload sanitization.

See which projects drive your usage

A new chart shows your organization's units over time, grouped by project or type, with a CSV export. On Langfuse Cloud it is on the Billing page; self-hosted deployments get a new Usage page.

Langfuse CLI 1.0 and new evaluator features

Langfuse CLI 1.0 released, along with new evaluator template gallery and reusable evaluators for production evaluations.

Deploying Langfuse Open-Source LLM Observability

This technical guide explains how to deploy Langfuse, an open-source observability platform for LLM applications, using Docker Compose. The deployment uses PostgreSQL for metadata, ClickHouse for trace and metrics analytics, Redis for cache/queueing, and S3-compatible object storage for media/exports, with Traefik and Let's Encrypt providing TLS. The article includes required prerequisites (Linux server 4 vCPU / 16GB RAM, Docker + Docker Compose, domain A record), step-by-step environment and docker-compose configuration, first-run setup (create organization/project and API keys), and a test-trace example using the Langfuse SDK and an OpenAI-compatible client. Publication date: 2026-08-12.

• Langfuse is an open-source observability platform for LLM applications that traces prompts/responses, tracks token usage and cost, and provides debugging analytics.
• The guide deploys Langfuse via Docker Compose using Traefik (TLS), PostgreSQL (metadata), ClickHouse (trace/metrics analytics), Redis (cache/queue), and S3-compatible object storage.
• Container images and versions shown include traefik:v3.7.0, postgres:17, clickhouse/clickhouse-server:26.5.1-alpine, and redis:7-alpine; Langfuse images used are langfuse/langfuse:3 and langfuse/langfuse-worker:3.