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TAHO Labs logo
TAHO Labs
✓ verifiedPaid

Execution-fabric infrastructure that splits AI/compute workloads into units and packs GPUs to cut cost and raise utilization.

Bitbucket logo
Bitbucket
✓ verifiedFreemium

Atlassian's Git repository hosting for teams with built-in CI/CD pipelines and tight Jira integration for code review and deployment.

13M visits/mo
Pump logo
Pump
✓ verifiedFree

Free cloud cost-optimization platform that pools buying power to give startups enterprise-level AWS, GCP, and Azure discounts.

64K visits/mo3.2K saves
Qase logo
Qase
✓ verifiedFreemium

Test management platform unifying manual and automated test results with AI-assisted case generation, for scaling QA teams.

375K visits/mo
Pricing

No public pricing

No public pricing

Free trial available

No public pricing

Free: $0/user (up to 3 users, 2 projects, 500MB storage)
Startup: $24/user/month (up to 20 users, 1,000 AI credits/month)
Business: $30/user/month (up to 100 users, 2,000 AI credits/month)

Free trial available

Core features
  • Decomposes workloads into small routable units
  • Packs GPUs/CPUs to raise utilization
  • Works with Kubernetes, SLURM, CUDA and ROCm
  • Runs on cloud, on-prem and edge
  • Sits beneath existing orchestration with no rewrite
  • Git repository hosting
  • Bitbucket Pipelines CI/CD
  • Pull requests and code review
  • Native Jira integration
  • Branch permissions and access controls
  • IP allowlisting and security features
  • Automated cloud spend optimization
  • Group buying for enterprise discounts
  • Cost visibility and insights dashboards
  • Coverage across AWS, GCP, and Azure
  • No-cost service model
  • Central test case repository with reporting dashboards
  • AI conversion of manual test cases into automated test scripts
  • CI/CD-connected automated test orchestration
  • Requirements-to-test traceability reporting
  • MCP server for connecting AI agents to test data
  • 20+ integrations including Jira, GitHub, and Slack
Use cases
  • Increasing GPU cluster utilization
  • Cutting AI/compute infrastructure cost
  • Speeding up training and inference workloads
  • Getting more from existing hardware without migration
  • Source code management
  • CI/CD automation
  • Team code review
  • DevOps for Jira-based teams
  • Reducing startup cloud bills
  • Automating reserved-capacity savings
  • Gaining visibility into multi-cloud spend
  • Accessing enterprise pricing without scale
  • QA teams consolidating scattered CI, manual, and automated results
  • Engineering orgs converting manual test backlogs into automation
  • Enterprises needing audit-ready traceability for regulated software
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