Neptune.ai vs Anyscale
Compare data AI Tools
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Neptune.ai
Experiment tracking, model registry, and metadata store that helps ML teams log, compare, and ship models with searchable runs and rich visualizations.
Pricing Free / $29 per month
Category data
Difficulty Beginner
Type Web App
Status Active
Anyscale
Fully managed Ray platform for building and running AI workloads with pay as you go compute, autoscaling clusters, GPU utilization tools and $100 get started credit.
Pricing Pay as you go
Category data
Difficulty Beginner
Type Web App
Status Active
Feature Tags Comparison
Only in Neptune.ai
mlopsexperiment-trackingmodel-registrymetadatavisualization
Shared
None
Only in Anyscale
raydistributedtraininginferencegpuautoscaling
Key Features
Neptune.ai
- • Flexible logging: Track metrics params artifacts and images from any framework using light SDKs and callbacks
- • Search and compare: Slice runs by tags configs and scores to pick winners with evidence not memory
- • Custom dashboards: Build live charts tables and tiles to monitor long trainings and share status
- • Model registry: Store versions stages and approvals so releases are auditable and reversible
- • Collaboration: Organize workspaces projects and roles so large teams stay coordinated
- • Artifacts: Keep predictions checkpoints and plots alongside metrics for reproducibility
Anyscale
- • Managed Ray clusters with autoscaling and placement policies
- • High GPU utilization via pooling and queue aware scheduling
- • Model serving endpoints with rolling updates and canaries
- • Ray compatible APIs so existing code ports quickly
- • Observability and cost tracking across jobs and users
- • Environment images with Python CUDA and dependency control
Use Cases
Neptune.ai
- → Track baselines and ablations to defend decisions in reviews
- → Monitor long running experiments and intervene when metrics drift
- → Promote models through staged approvals with clear lineage
- → Share results with PMs and leads using links and dashboards
- → Attach artifacts so future teams can reproduce findings quickly
- → Automate comparisons in CI to block regressions before merge
Anyscale
- → Scale fine tuning and batch inference on pooled GPUs
- → Port Ray pipelines from on prem to cloud with minimal edits
- → Serve real time models with canary and rollback controls
- → Run retrieval augmented generation jobs cost efficiently
- → Consolidate ad hoc notebooks into governed projects
- → Share clusters across teams with quotas and budgets
Perfect For
Neptune.ai
ML engineers, researchers, data scientists, MLOps and platform teams who need reliable tracking and registries
Anyscale
ml engineers data scientists and platform teams that want Ray without managing clusters and need efficient GPU utilization with observability and controls
Capabilities
Neptune.ai
SDKs and callbacks Intermediate
Runs at scale Intermediate
Versioned models Professional
Enterprise controls Professional
Anyscale
Managed Clusters Professional
Model Endpoints Intermediate
Utilization and Cost Intermediate
Enterprise Controls Intermediate
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