Running Pipette as a service, AI Evaluation Observability

Pipette Agency Implementation, On-Device Model Selection for Clients

Learn how to use Pipette's device-specific leaderboards and Pareto frontier visualizations to help clients select foundation models that meet their latency and memory constraints. This course teaches agencies how to deliver model benchmarking as a service, interpret performance tradeoffs across hardware targets, and build repeatable workflows for on-device AI project scoping.

Open the decision record for Pipette

What does running Pipette for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Not published, Pipette is open-source with no pricing_tiers provided, so vendor cost is $0 software but agency must budget hardware and engineering time they must supply.
Time to first value
Not published, setup_complexity is medium and time_to_value is days, but no explicit hour count is published
Payback
Not modeled, client price, labor rate, hardware amortization, usage cost, and expected engagement volume are not supplied in the Level 1 data
Guided implementation
8 hours

Is Pipette worth running as a client service?

Pipette offers transparent, device-specific benchmarking for on-device models that agencies with ML engineering depth can productize as an advisory service. What the evidence does not support is any revenue, margin, or ROI figure, and the absence of pricing tiers, integrations, API signals, and white-label capability means all client pricing and positioning must be supplied by the agency.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • Target devices named in Pipette data: Galaxy S26 Ultra
  • MacBook Pro
  • Ryzen AI Max+ 395
  • iPhone 17 Pro
  • Model submissions accepted via Pipette's community submission workflow

People and inputs

  • Engineering time to reproduce Pipette's benchmark harness on real hardware
  • Device lab or access to each named hardware target
  • Analysis capacity to interpret decode throughput, prefill latency, E2E latency, and peak RAM across context lengths
  • Ability to read IFBench scores and Pareto frontier charts for client recommendations

Included with the course

6 working documents for delivering this service.

  • On-Device Model Selection Checklistchecklist
  • Pareto Frontier Analysis Worksheet for Client Proposalsworksheet
  • Device-Specific Leaderboard Reference Guideguide
  • Peak RAM Budget Validation Templatetemplate
  • Model Benchmarking Project Scope SOPsop
  • Quality-Speed Tradeoff Presentation Decktemplate

Listed by name. These documents are not yet published as individual downloads.