FloydHub vs Lambda Labs Cloud
Compare specialized AI Tools
11% Similar based on 1 shared tag
F
FloydHub
FloydHub was a managed training and deploying platform for deep learning experiments that simplified data mounting jobs metrics and collaboration but it permanently shut down in 2021.
Pricing Discontinued
Category specialized
Difficulty Beginner
Type Web App
Status Active
L
Lambda Labs Cloud
GPU cloud for training and inference with H100 and newer instances clusters private clouds containers storage and usage based hourly billing.
Pricing Pay as you go
Category specialized
Difficulty Beginner
Type Web App
Status Active
Feature Tags Comparison
Only in FloydHub
mlopsdiscontinuedexperimentsdeploy
Shared
training
Only in Lambda Labs Cloud
gpucloudh100inference
Key Features
FloydHub
- • Reproducible environments for experiments with simple job launch and logs that reduced setup toil for fast iteration during research
- • Dataset mounting and snapshots that kept inputs consistent across runs so results remained comparable and easy to audit for teams
- • Team workspaces and collaboration that allowed shared projects and roles so students and startups could coordinate work simply
- • Run metrics and comparisons that surfaced loss curves and scores so selection and reporting were faster for notebooks and papers
- • CLI and UI control that matched developer needs so power users scripted pipelines while newcomers clicked through safe defaults
- • Early model deployment paths that exposed inference endpoints for demos which helped small teams share progress with stakeholders
Lambda Labs Cloud
- • Instant H100 class instances for training and inference
- • One click clusters for distributed jobs with fast fabric
- • Per hour pricing with no egress fees and clear quotas
- • Prebuilt images for PyTorch CUDA and common stacks
- • Terraform and API to automate provisioning at scale
- • Private networking roles and quotas for control
Use Cases
FloydHub
- → Migration planning from legacy accounts to modern notebook services with artifact export so research continuity is preserved for teams
- → Experiment tracking adoption using current open source stacks that replicate run history dashboards and metrics for new projects
- → Student lab environments updated to contemporary cloud notebooks that mirror the low friction FloydHub approach for coursework and demos
- → Prototype to demo flows rebuilt on managed inference endpoints which recreate the fast shareability that FloydHub enabled for stakeholders
- → Dataset governance modernization that replaces snapshots with versioned buckets and policies to keep experiments auditable and compliant
- → Team collaboration standardized on workspaces and role based access in current tools to maintain the simple getting started experience
Lambda Labs Cloud
- → Train LLMs and diffusion models on H100 with multi node templates
- → Run high throughput inference with autoscaled instances
- → Burst to cloud from on prem boxes during peak demands
- → Host internal notebooks with GPU acceleration for teams
- → Standardize golden images for controlled environments
- → Benchmark models cost per token across GPU types
Perfect For
FloydHub
teams modernizing from legacy MLOps tools educators and small research groups that need a clear path from historical FloydHub workflows to current platforms with better governance and support
Lambda Labs Cloud
ML engineers research labs platform teams and enterprises that need fast H100 access predictable cost and automation friendly provisioning
Capabilities
FloydHub
Experiments and Jobs Basic
Snapshots and Datasets Basic
Shared Workspaces Basic
Modern Replacements Intermediate
Lambda Labs Cloud
GPU instances Professional
One click clusters Professional
API and Terraform Intermediate
Private cloud options Intermediate
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