Databricks vs New Relic
Relationship
Overlaps on data analysis, evaluation and observability and knowledge retrieval; New Relic's scale not recorded.
Assembled from the recorded fields for this pair, not hand-checked. The comparison below is read from each company’s own profile.
3 shared capabilities — Across all their products, both list data analysis, evaluation and observability and knowledge retrieval.
Ludbee capability tags · every product each company sellsShared product type — Both ship AI agent, application, data service and 1 more.
Ludbee product recordsAligned comparison
Capability overlap
Shared · 3
Not verified for New Relic · 13
Recorded for Databricks. New Relic’s product records say nothing either way — a missing record is not a missing capability.
New Relic has no capability Databricks lacks, among the 3 recorded here.
Products, side by side
Algorithmic pairing — assembled from recorded fields, not hand-checked
Databricks
Application
AI-native business-intelligence product comprising AI/BI Dashboards (AI-assisted dashboard/visualization creation) and Genie Spaces (conversational natural-language exploration of data), built into the Databricks Data + AI Platform with Unity Catalog governance and no per-seat licensing.
AI/BI Dashboards provides AI-assisted authoring, data visualization and forecasting.
Agentic customer data platform embedded in Databricks: builds Customer 360 profiles with agentic identity resolution, syncs audiences to martech and adtech tools, and runs marketing agents that plan and personalise campaigns.
AI coworker (the evolution of the earlier Databricks Assistant/Genie) that lets business users ask questions, take action and drive outcomes over enterprise data via natural language, integrating with Slack, Teams, Jira, Google Drive and Salesforce, with mobile apps.
AI agent
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.
Platform
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.
Data service
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.
New Relic
Application
New Relic AI analyzes telemetry data and answers questions about monitored systems.
New Relic Applied Intelligence detects anomalies and helps engineers understand and resolve incidents.
AI agent
New Relic Autopilot investigates incidents, identifies likely root causes and recommends next actions.
Platform
Observability for AI applications: LLM performance, error and cost, token and quality metrics, MCP server call lifecycle, and maps of multi-agent interactions.
Data service
New Relic Ground Truth combines telemetry, dependencies, incidents and changes into operational context for AI agents.
No counterpart
Databricks sells these in a stack layer with no product recorded for New Relic yet — nothing on the other side to compare them against.
Agent platform
A control plane for building, evaluating, governing and monitoring AI agents across proprietary and open-source models, with native MCP support.
Developer tool
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.
Infrastructure service
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.
Model API
A managed endpoint service for deploying and governing classical ML models, generative models and agents from one interface.