Planning Tools
Answer "Should We?"
Before "How?"
Orbital compute isn't right for every workload. Our planning tools help you evaluate feasibility, model constraints, and make informed decisions before committing to hardware.
Status: Feasibility Calculator and Thermal Simulator available now. Full suite expanding through 2026.
Multi-Constraint Feasibility Analysis
The only platform that evaluates thermal, power, latency, and cost simultaneously—before you commit to hardware.
Your workload profile is well-suited for orbital compute. High throughput inference with flexible latency SLA allows optimal scheduling around power cycles. Projected 43% energy savings vs equivalent terrestrial deployment.
Layer 1: Planning
The foundation of the RotaStellar platform. Before you can execute workloads in orbit, you need to know if orbital compute makes sense for your use case.
Feasibility First
Not every workload benefits from orbital compute. Our tools help you identify which workloads are candidates and which should stay on the ground.
Constraint Modeling
Thermal rejection, power generation, latency budgets - understand the constraints before they become surprises during deployment.
Decision Support
Get concrete numbers: cost comparisons, energy savings, latency projections. Make the business case with real data.
Feasibility Analyzer
Determine if orbital compute makes sense for your workload.
Input your compute requirements - TFLOPS, storage, bandwidth - and get a detailed analysis comparing orbital deployment against terrestrial alternatives. Includes cost projections, energy analysis, and risk factors.
Capabilities
- Workload characterization
- Orbit selection recommendations
- Cost comparison vs terrestrial
- Energy efficiency projections
- Risk factor analysis
Available Now
from rotastellar import PlanningTools
planner = PlanningTools(api_key="...")
result = planner.feasibility.analyze(
workload_type="ai_inference",
compute_tflops=100,
storage_tb=50,
latency_requirement_ms=200,
duration_months=12
)
print(f"Orbital viable: {result.viable}")
print(f"Recommended orbit: {result.orbit}")
print(f"Energy savings: {result.energy_savings_pct}%")
print(f"Cost comparison: {result.cost_vs_terrestrial}")
print(f"Key risks: {result.risks}")Thermal Modeler
Model heat rejection in vacuum and solar heating cycles.
Space eliminates convective cooling but enables radiative heat rejection. Our simulator models the complete thermal environment - solar heating, Earth albedo, eclipse cycles - to predict component temperatures and validate cooling designs.
Capabilities
- Radiative cooling modeling
- Solar and albedo heating
- Eclipse thermal cycling
- Component temperature maps
- Radiator sizing recommendations
Available Now
simulation = planner.thermal.simulate(
orbit_altitude_km=550,
orbit_inclination=53,
power_dissipation_kw=50,
radiator_area_m2=100,
duration_orbits=10
)
print(f"Max temp: {simulation.max_temp_c}°C")
print(f"Min temp: {simulation.min_temp_c}°C")
print(f"Thermal margin: {simulation.margin_c}°C")
for point in simulation.timeline[:3]:
print(f"{point.phase}: {point.temp_c}°C")
print(f" Solar input: {point.solar_input_w}W")
print(f" Radiated: {point.radiated_w}W")Latency Simulator
End-to-end latency modeling with ground station coverage.
Model the complete data path from user to orbital compute and back. Includes ground station visibility windows, inter-satellite link hops, and processing delays. Essential for understanding if your latency requirements can be met.
Capabilities
- Ground-to-orbit propagation
- ISL routing optimization
- Coverage gap analysis
- P50/P95/P99 latency distributions
- Ground station network modeling
Q2 2026
latency = planner.latency.analyze(
user_locations=["NYC", "LON", "TYO", "SYD"],
orbit_altitude_km=550,
ground_stations="global_network",
isl_enabled=True
)
print(f"P50 latency: {latency.p50_ms}ms")
print(f"P95 latency: {latency.p95_ms}ms")
print(f"P99 latency: {latency.p99_ms}ms")
print(f"Coverage: {latency.coverage_pct}%")
print(f"Max gap: {latency.max_gap_minutes}min")Power Planner
Solar panel sizing, eclipse periods, battery requirements.
Calculate power generation and storage requirements for your orbital compute deployment. Models solar panel efficiency degradation, eclipse duration by orbit, and battery cycling to ensure continuous operation.
Capabilities
- Solar array sizing
- Battery capacity planning
- Eclipse power management
- Degradation modeling over mission life
- Power budget optimization
Q2 2026
power = planner.power.analyze(
orbit_altitude_km=550,
continuous_load_kw=25,
peak_load_kw=50,
mission_duration_years=5
)
print(f"Solar array: {power.solar_area_m2}m²")
print(f"Battery: {power.battery_kwh}kWh")
print(f"Eclipse duration: {power.eclipse_minutes}min")
print(f"EOL margin: {power.eol_margin_pct}%")
print(f"Annual degradation: {power.degradation_pct}%")Built for the Full Stack
Planning Tools integrate with the rest of the RotaStellar platform.
Orbital Intelligence
Once you've planned your deployment, use Orbital Intelligence to understand the space environment - conjunction risks, debris tracking, anomaly detection.
→ ↑Orbital Runtime
Ready to execute? Orbital Runtime provides the scheduling, adaptive inference, and fault tolerance primitives for production workloads.
→Start planning your orbital deployment
Get early access to the Planning Tools and start evaluating orbital compute for your workloads.