Products / Model and stack benchmarking
Streak
Candidate models and infrastructure tested on your own workloads before you commit, so the choice rests on your results, not a leaderboard.
Request early accessThe problem
Model choices are usually made from a public leaderboard and a vendor demo. Neither uses your data, your prompts, your latency budget or your compliance rules.
Teams find out after the build that the model is too slow, too expensive at volume, or wrong on the cases that matter. By then the architecture is set around it.
How it works
Define the workload
Turn the use case into a test set from your own data, with the accuracy, latency, cost and compliance limits it has to meet.
Run the candidates
Test candidate models, and the infrastructure they would run on, against that set under the same conditions.
Compare on what matters
Score each option on accuracy, latency, cost at expected volume, and the failure cases you care about.
Get the blueprint
The chosen option comes with an infrastructure plan: compute, deployment architecture and integration points.
Keep the tests
The same test set becomes the starting suite for Fineness, so the model you chose keeps being checked after launch.
Who it is for
- Architects and engineering leads choosing a model or platform for a production use case
- CIOs and CTOs who need a defensible answer to "why this model?"
- Procurement and finance teams comparing AI vendor costs at real volume