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Feasibility Analysis

Determine whether your workload is suitable for orbital deployment and get recommendations for optimal configuration.
Status: Early Access — Request API key

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Overview

Feasibility analysis evaluates:
  • Technical viability — Can this workload run in space?
  • Optimal orbit — Which orbital regime best fits your requirements?
  • Cost estimation — What will deployment and operation cost?
  • Risk assessment — What are the key challenges?

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Quick Start

from rotastellar import RotaStellar

client = RotaStellar(api_key="rs_...")

result = client.planning.analyze(
    workload="ai_inference",
    compute_tflops=100,
    storage_tb=10,
    bandwidth_gbps=1,
    latency_sla_ms=50
)

print(f"Viable: {result.viable}")
print(f"Recommendation: {result.recommendation}")
print(f"Orbit: {result.orbit}")
print(f"Cost: ${result.cost_monthly}/mo")

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Parameters

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Required

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workload
string
required
Type of workload. Options:
  • ai_inference — ML model inference
  • ai_training — ML model training
  • data_processing — General data processing
  • edge_compute — Edge computing workloads
  • storage — Data storage and retrieval
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compute_tflops
number
required
Required compute capacity in TFLOPS (FP16 equivalent)

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Optional

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storage_tb
number
Required storage in terabytes
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bandwidth_gbps
number
Required bandwidth to/from ground in Gbps
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latency_sla_ms
number
Maximum acceptable latency in milliseconds
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availability_sla
number
Required availability (e.g., 0.999 for 99.9%)
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region
string
Geographic region for ground connectivity. Options: global, north-america, europe, asia-pacific

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Response

{
  "viable": true,
  "recommendation": "LEO constellation with 6 satellites provides optimal balance of latency and cost",
  "orbit": {
    "type": "LEO",
    "altitude_km": 550,
    "inclination_deg": 53,
    "constellation_size": 6
  },
  "cost": {
    "monthly": 125000,
    "setup": 2500000,
    "currency": "USD"
  },
  "power": {
    "required_kw": 15.5,
    "solar_array_m2": 45,
    "battery_kwh": 120
  },
  "latency": {
    "p50_ms": 25,
    "p95_ms": 45,
    "p99_ms": 65
  },
  "risks": [
    {
      "category": "thermal",
      "severity": "medium",
      "description": "Eclipse periods require thermal management"
    }
  ],
  "alternatives": [
    {
      "orbit": "MEO",
      "tradeoff": "Higher latency (80ms) but 40% lower cost"
    }
  ]
}

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Workload Types

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AI Inference

Best for models that need low-latency inference close to data sources. 
result = client.planning.analyze(
    workload="ai_inference",
    compute_tflops=100,
    latency_sla_ms=50
)

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AI Training

For training models on orbital data (e.g., Earth observation). 
result = client.planning.analyze(
    workload="ai_training",
    compute_tflops=500,
    storage_tb=100
)

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Data Processing

General-purpose compute for data transformation and analysis. 
result = client.planning.analyze(
    workload="data_processing",
    compute_tflops=50,
    bandwidth_gbps=10
)

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Cost Factors

FactorImpact
Orbit altitudeHigher = cheaper launch, more latency
Constellation sizeMore satellites = better coverage, higher cost
Power requirementsHigher power = larger solar arrays
BandwidthMore bandwidth = more ground stations
RedundancyHigher availability = more satellites

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Next Steps

Thermal Simulation

Model heat management for your configuration

Latency Simulation

Detailed latency modeling for your use case