#47 in AI Development PlatformsUpdated 2026-08

SigOpt

by SigOpt · Open source experiment optimisation, self-hosted or run in-memory

16.3 — BenchRank score out of 100

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Overview

SigOpt runs optimisation experiments, including searches across multiple competing metrics and many constraints. Its self-hosted server lets teams organise, visualise and share experiment progress without data leaving their own servers, and sigopt[lite] runs the core module in-memory via pip. There is an XGBoost integration for learning model hyperparameters.

Best for: ML and research teams tuning models or simulations who need experiment data kept self-hosted
Pricing: No prices are shown; the page describes open source releases obtained by git clone or pip install.
Runs on: Self-hosted

Strengths and trade-offs

Strengths

Self-hosted server keeps experiment data on your own servers
Handles multiple competing metrics and constrained searches
sigopt[lite] runs the core module in-memory via a pip install
XGBoost integration for learning model hyperparameters

Trade-offs

No hosted or managed service is described on the page
You must run your own server or Python install to use it
Page shows no pricing, licence or support terms
Site copyright reads 2024, so current maintenance is unclear

How this score is made up

Each dimension is scored out of 100 and combined into the headline score using fixed weights.

MCP support: 0 out of 100
Documentation: 0 out of 100
Agent friendliness: 41 out of 100
Changelog: 0 out of 100
Marketing site structure: 70 out of 100
Operational trust: 0 out of 100

Measured, but not part of the score

Useful to know, but not a mark for or against the product — so these do not affect the ranking.

Openness: 60 out of 100
Maintenance: 0 out of 100

This doesn’t look right — report a problem with SigOpt’s score

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