Databricks vs Papr

Genie Agents answer natural-language data questions with SQL queries, result tables, and visualizations.

Autonomous AI agent for data teams inside the Databricks workspace: plans and runs multistep data engineering, data science, machine-learning and dashboard work in notebooks, the SQL editor and pipelines, grounded in Unity Catalog metadata.

A control plane for building, evaluating, governing and monitoring AI agents across proprietary and open-source models, with native MCP support.

Automated machine learning that trains and evaluates classification, regression and forecasting models on a dataset, and writes an editable notebook with the training code for every trial.

A managed service for fine-tuning open-source LLMs or training custom models on enterprise data using dedicated GPU infrastructure.

Managed MLflow on Databricks: experiment tracking, tracing, evaluation and model management for classical machine learning, deep learning, generative AI applications and agents, governed through Unity Catalog.

Databricks' tooling for building, fine-tuning, serving and evaluating models and agents on lakehouse data.

Lakehouse platform for storing, governing, querying and sharing enterprise data.

Agentic SIEM built on the Databricks security lakehouse: ingests and normalises security telemetry to OCSF, manages detections as code, and runs agents that author rules, triage alerts and hunt threats.

AI Runtime provides serverless GPU compute for training, fine-tuning and batch inference of custom models.

Enterprise AI gateway providing centralized cost tracking/budgets, model access (Claude, GPT, Gemini, Grok and others), security/governance (access policies, PII/PHI filtering, audit trails), smart routing and observability across an organization's AI systems.

A managed hybrid semantic, keyword and vector search service (formerly Mosaic AI Vector Search) with automatic data sync and Unity Catalog governance.

Databricks Feature Store provides a governed layer for building, sharing, and serving machine learning features.

Document Intelligence uses AI functions to parse, extract, and classify enterprise documents.

A managed endpoint service for deploying and governing classical ML models, generative models and agents from one interface.

API that upgrades the vector embeddings a team already has into graph-aware embeddings: one call at index time attaches 14 relational dimensions (entities, relations, time, ownership) to each vector and one call at query time reranks the top results, keeping the user's embedding model and vector database; processed in memory and not stored, with VPC deployment and audit logs for enterprise teams.