Databricks vs Arize Phoenix
Compare data AI Tools
0% Similar based on 0 shared tags
Databricks
Unified data and AI platform with lakehouse architecture collaborative notebooks SQL warehouse ML runtime and governance built for scalable analytics and production AI.
Pricing Usage based, contact sales
Category data
Difficulty Beginner
Type Web App
Status Active
A
Arize Phoenix
Open source LLM tracing and evaluation that captures spans scores prompts and outputs, clusters failures and offers a hosted AX service with free and enterprise tiers.
Pricing Free, SaaS tiers by quote
Category data
Difficulty Beginner
Type Web App
Status Active
Feature Tags Comparison
Only in Databricks
lakehouseetlsqlmlmlflowvector-search
Shared
None
Only in Arize Phoenix
llmobservabilitytracingevaluationopensourceotel
Key Features
Databricks
- • Lakehouse storage and compute that unifies batch streaming BI and ML on open formats for cost and portability across clouds
- • Collaborative notebooks and repos that let data and ML teams build together with version control alerts and CI friendly patterns
- • SQL Warehouses that power dashboards and ad hoc analysis with elastic clusters and fine grained governance via catalogs
- • MLflow native integration for experiment tracking packaging registry and deployment that works across jobs and services
- • Vector search and RAG building blocks that bring enterprise content into assistants under governance and observability
- • Jobs and workflows that schedule pipelines with retries alerts and asset lineage visible in Unity Catalog for audits
Arize Phoenix
- • Open source tracing and evaluation built on OpenTelemetry
- • Span capture for prompts tools model outputs and latencies
- • Clustering to reveal failure patterns across sessions
- • Built in evals for relevance hallucination and safety
- • Compare models prompts and guardrails with custom metrics
- • Self host or use hosted AX with expanded limits and support
Use Cases
Databricks
- → Build governed data products that serve BI dashboards and ML models without copying data across silos
- → Modernize ETL by shifting to Delta pipelines that handle streaming and batch with fewer moving parts and clearer lineage
- → Deploy RAG assistants that search governed documents with vector indexes and access controls for safe retrieval
- → Scale experimentation with MLflow so teams compare runs promote models and enable reproducible releases
- → Consolidate legacy warehouses and data science clusters to reduce cost and drift while improving security posture
- → Serve predictive features to apps using online stores that sync from batch and streaming pipelines under catalog control
Arize Phoenix
- → Trace and debug RAG pipelines across tools and models
- → Cluster bad answers to identify data or prompt gaps
- → Score outputs for relevance faithfulness and safety
- → Run A B tests on prompts with offline or online traffic
- → Add governance with retention access control and SLAs
- → Share findings with engineering and product via notebooks
Perfect For
Databricks
data engineers analytics leaders ML engineers platform teams and architects at companies that want a governed lakehouse for ETL BI and production AI with usage based pricing
Arize Phoenix
ml engineers data scientists and platform teams building LLM apps who need open source tracing evals and an optional hosted path as usage grows
Capabilities
Databricks
Delta Pipelines Professional
SQL Warehouses Professional
MLflow and Features Professional
Vector and RAG Intermediate
Arize Phoenix
Spans and Context Professional
Built in and Custom Intermediate
Clustering and Search Intermediate
Hosted AX Basic
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