HyperOptimizer lets you easily run parallel trials, and shows which configurations perform best (and worst). For trading strategies, ML models, simulations, data pipelines, and custom algorithms.
Best score
0.942
Trials
128
Guardrail
<200ms
Artifacts
12
SECURITY
Optimization often involves sensitive models, proprietary algorithms, private datasets, or internal workflows. HyperOptimizer is designed around containerized execution, isolated trials, and clear boundaries between orchestration, metrics, and workload data.
SETUP
Bring a containerized workload, define the search space, and run scalable experiments without assembling infrastructure.
Package your workload as a Docker image and run it with your existing CLI entrypoint. No SDK dependency is required.
Define the search inputs and objective. Example:
learning_rate,
batch_size,
and latency constraints.
Run parallel trials, monitor progress, and compare outcomes as the best configuration rises to the top.
PRODUCT
Define parameters, run scalable trials, inspect every metric, and keep the configurations that actually perform.
Experiment history
Compare past runs, inspect artifacts, and keep auditable records of which configurations shipped and why.
WHY IT MATTERS
HyperOptimizer replaces the operational headache around experimentation: orchestration, execution, retries, metrics, logs, and result comparison.
Messy, slow, infrastructure-heavy
Provision compute
Set up worker nodes and capacity.
Configure orchestration
Manage scheduling, autoscaling, and networking.
Wire tuning tools
Connect frameworks, storage, and retries.
Debug failed jobs
Chase retries, runtime errors, and drift.
Collect scattered metrics
Manually stitch logs and outcomes across runs.
Managed optimization, from image to result
Bring your container
Use any Dockerized model, script, strategy, or workload.
Define your search space
Set parameters, bounds, and constraints.
Choose objective metrics
Optimize for what matters to your workload.
Run parallel trials
Launch scalable trials with managed retries.
Compare ranked results
Review metrics, logs, artifacts, and winners.
USE CASES
If it runs in a container and emits results, HyperOptimizer can search for better configurations.
Tune training parameters, preprocessing, model settings, and evaluation metrics.
Optimize strategy parameters, risk settings, entry/exit logic, and backtest configurations.
Search large configuration spaces for engineering, scientific, Monte Carlo, or agent-based experiments.
Tune batch sizes, parallelism, memory limits, indexing settings, and processing behavior.
Optimize prompts, retrieval settings, routing logic, thresholds, and tool parameters.
Run black-box optimization for any script, service, or algorithm that reports metrics.
WORKFLOW
Bring a Docker image or containerized job.
Set parameters, ranges, objectives, constraints, and budgets.
HyperOptimizer schedules, monitors, retries, and captures results.
Rank configurations by metrics, logs, artifacts, cost, and custom scores.
FAQ
Can't find the answer you're looking for? Reach out to our support team.
No. Trading strategies are one use case. HyperOptimizer is designed for any containerized workload with parameters, metrics, and an objective.
Those are powerful optimization frameworks. HyperOptimizer provides the managed infrastructure, orchestration, trial execution, metric collection, dashboard, and workflow around the optimization process.
A container: a containerized workload, a search space, and one or more metrics to optimize.
Yes. HyperOptimizer is metric-agnostic: optimize for accuracy, return, drawdown, latency, throughput, token cost, runtime, or any custom score you report.
No. Workloads can be ML models, trading strategies, simulations, data pipelines, LLM workflows, or any custom algorithm that can run in a container and emit metrics.
HyperOptimizer is designed for controlled environments with containerized, isolated trial execution. It supports private images and keeps workload execution separated from orchestration and experiment metadata.
Yes. You can run private container images as part of optimization workflows, with access and execution controlled by your environment configuration.
Yes. Experiments can be configured with resource limits, trial budgets, and runtime constraints so optimization stays within operational boundaries.
Yes. Multiple trials run in parallel, and scheduling adapts as results come in so experiments converge faster.
Each trial runs independently. Failed trials are surfaced in the dashboard with logs and status, and remaining trials continue based on your configured budget.
Join the waitlist for managed optimization infrastructure built for models, strategies, simulations, pipelines, and custom algorithms. Try the platform, share your feedback, and help shape the future of optimization workflows. Want us to support your framework or stack? Let us know.