The software layer for orbital compute
The Cloud Breaks in Space.
We're Fixing It.
Kubernetes doesn't understand orbital mechanics. PyTorch doesn't adapt to solar eclipses. TensorFlow doesn't expect bit flips. We're building the runtime that does.
Why Cloud Assumptions Break
Space compute isn't just "AWS in orbit." Every cloud assumption fails.
| Cloud Assumption | Space Reality |
|---|---|
| Always-on network | Intermittent: eclipses, orbital motion, ground station handovers |
| Stable power | Variable: solar flux cycles, eclipse periods, battery limits |
| Low, predictable latency | LEO: 5-40ms RTT; GEO: 240ms+; ISL hops add variability |
| Reliable hardware | Radiation causes bit flips; SEUs are normal, not exceptional |
| Manual intervention | Near-zero tolerance - you can't send a technician to LEO |
Three Layers. One Platform.
From planning to intelligence to execution - the complete stack for orbital compute.
Orbital Runtime
Execute workloads across Earth and orbit. Scheduling, adaptive inference, and fault tolerance designed for space constraints.
Explore → 02Orbital Intelligence
Track 10,000+ objects, analyze conjunction risks, detect anomalies. The situational awareness layer runtime depends on.
Explore → 01Planning Tools
Feasibility analysis, thermal modeling, latency simulation, power budgeting. Answer "should we?" before "how?"
Explore →Orbital Runtime: The Core Differentiator
Three primitives that make computing in space actually work.
Orbit Scheduler
Workload orchestration that understands orbital mechanics, energy availability, and network topology. Kubernetes for Earth + orbit.
Adaptive Runtime
Inference that bends, not breaks. Dynamically adjust precision, layer activation, and context length to stay within power and thermal constraints.
Resilient Compute
Fault-tolerant ML for radiation environments. Detect corruption, bound error propagation, re-execute only what's needed.
"The winning companies won't sell 'space servers.' They'll sell software brains. We're building the primitives that make orbital compute work - years before the hardware is common."
Developer-First
APIs and SDKs for every capability. Build orbital compute into your applications.
from rotastellar import RotaStellar
client = RotaStellar(api_key="...")
# Planning: Check feasibility
feasibility = client.planning.analyze(
workload="ai_inference",
compute_tflops=100
)
# Runtime: Adaptive inference
result = client.runtime.generate(
model="llama-70b",
prompt="...",
energy_budget=340, # Watts
quality="best_effort"
)
print(f"Response: {result.text}")
print(f"Adaptations: {result.adaptations}")The Timeline
We're building software before the hardware is common - so it's ready when you need it.
| Year | Industry | RotaStellar |
|---|---|---|
| 2025-2026 | First orbital compute satellites (experimental) | Simulators, research, open-source tools |
| 2027-2028 | First commercial orbital DC deployments | Production runtime for early adopters |
| 2029+ | Scaling orbital compute infrastructure | Default orchestration layer |
Backed by Research
Every runtime primitive is grounded in published research and open benchmarks.
From the Blog
All posts →Ready to build for space?
Get early access to the platform. Start with planning tools today, be ready for orbital runtime tomorrow.