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 |
Four Layers. One Platform.
From planning to intelligence to execution to coordination - the complete stack for orbital compute.
Distributed Compute
Coordinate AI across Earth and space. Federated learning, model partitioning, and bandwidth-optimized synchronization for hybrid infrastructure.
Explore → 03Orbital Runtime
Execute workloads in 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
)
# Distributed: Train across Earth-space
job = client.distributed.train(
model="llama-70b",
nodes=["ground-1", "orbital-1"],
compression="gradient-100x"
)
# Runtime: Execute in orbit
result = client.runtime.generate(
model="llama-70b",
prompt="...",
energy_budget=340 # Watts
)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.
Scheduling & Orchestration
Orbit-aware placement, energy optimization, latency modeling.
→Distributed AI
Federated learning, model partitioning, Earth-space coordination.
→Adaptive Inference
Energy-aware execution, dynamic precision, graceful degradation.
→Fault Tolerance
Radiation effects on ML, detection mechanisms, recovery strategies.
→Ready to build for space?
Get early access to the platform. Start with planning tools today, be ready for orbital runtime tomorrow.