Pricing

We have plans for teams both large and small. If you’d like to learn more or schedule a demo, contact us
and we’ll help find the plan that is right for you.

Looking for our academic pricing?  Click here for more information.

StarterWorkgroupEnterprise
Free!Starting at $2500/monthContact us
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Platform Access
Number of Seats13Custom
Number of Experiments per Month525Custom
Number of Observations per Experiment100500Up to 100,000
Number of Parameters per Experiment420Up to 150
Integrations
Client Libraries1YesYesYes
Open Source Integrations2YesYesYes
Enterprise Integrations3YesYes
Features
Parallel OptimizationYesYes
Number of Parallel Suggestions10Custom
Report Failed observationsYesYesYes
Categorical ParametersYesYes
Multimetric OptimizationYes
Benchmarking4Yes
Success / Support
Support SLAOnline Access1 business day24 hours
Named Success ManagerYes
Ongoing TrainingYes
Early Access ProgramYes
Quarterly Review and UpdateYes
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1Includes Python, R, Java

2Includes Spark and scikit-learn

3Includes MATLAB

4Includes Grid Search and Random Search

Why SigOpt?

Does SigOpt work for my problem?

The technology behind SigOpt has been helping experts in academia tune their complicated experiments for decades. SigOpt has helped firms get the most out of their products with less trial and error in fields from aerospace to finance to cosmetics to ecommerce to routing and beyond. If you are tuning any product via trial and error, we can help you do it better and faster.

Download our PDF brochure to learn more about SigOpt’s value to your industry:

How do I start using SigOpt?

Please sign up for a plan above. Or contact us
and we’ll get you started with a pilot. We provide an easy-to-use API and web interface so you can dive right in.

How does SigOpt work?

SigOpt provides an ensemble of the latest techniques in academic optimization research, built on top of advanced open source optimization systems that the founding team has built over many years in the field. First, we suggest the next best variation of your product to try, given what you have observed. Then, we create a feedback loop in which we provide optimal suggestions, and you report how they perform. We quickly iterate to the best possible variation by trading off exploration (learning about the parameters that we are tuning) and exploitation (using the information we have to get the highest possible return).

Frequently Asked Questions

For answers to more frequently asked questions about SigOpt, visit our FAQ.