# SigOpt

**Canonical:** https://apis.io/providers/sigopt/  
**Website:** https://sigopt.com  
**APIs profiled:** 0

SigOpt was a machine learning experimentation and optimization platform whose API applied ensemble Bayesian and black-box optimization to tune model hyperparameters, run design-of-experiments, and track experiment metadata at scale. Developers integrated its REST API and first-party clients (Python, Java, R, scikit-learn, Spark, MATLAB) to drive automated hyperparameter search from their training loops. SigOpt was acquired by Intel in 2020 and its hosted API and platform were subsequently discontinued; sigopt.com now redirects to an Intel corporate redirector and the api./app./docs.sigopt.com hosts no longer resolve. The GitHub organization (github.com/sigopt) and the published client libraries remain public as historical, first-party artifacts, including an open-sourced server and optimization engine (sigopt-server, sigoptlite, libsigopt).

## Kin Score — 6.8 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 6.8).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 2.6 |
| Developer Ergonomics | 7.1 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Tags

Company, Machine-Learning, Optimization, Hyperparameter Tuning, Experimentation, Bayesian Optimization, MLOps, Data Science, Defunct

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/sigopt/). Scores are computed from the provider's own public artifacts under a published rubric.