Robotics infrastructure
Deploy multimodal inference for robotics teams.
Serve world models, robot policies, and synthetic-data pipelines without building the platform layer yourself.
Camera feed
Model output
{
"action": "pick",
"waypoints": [
[0.42, 0.18, 0.31],
[0.42, 0.18, 0.05]
],
"confidence": 0.94
}{
"id": "req_8f4k2...",
"model": "custom-vla-v3",
"tokens": 128,
"status": "completed"
}Platform fit
One platform for the robotics model layer
Use the same product surface for live endpoints, custom checkpoints, batch jobs, and operational controls. The point is faster iteration with fewer infrastructure decisions in the way.
Serve live systems
Hosted multimodal endpoints
Low-friction endpoints for multimodal and video-native workloads in production robotics systems.
Bring your stack
Custom checkpoints and forks
Deploy internal checkpoints, fine-tunes, and adapted models behind the same API surface.
Run offline work
Batch generation and replay
Process datasets, generate synthetic data, and run evaluation jobs without separate infrastructure.
Operate with control
Versions, logs, and access
Roll forward safely, inspect behavior, and keep production boundaries explicit for robotics teams.
Control surface
One platform, multiple ways to run
Start with the operational mode you need today. The deployment model can change without forcing a new integration pattern on the team.
Serve multimodal models behind stable APIs for live applications and internal tooling.
Run endpoints
Deployment patterns
Choose the boundary that fits your robotics stack
Hosted, private, and hybrid are operational choices, not separate products. Teams move between them as customer requirements and systems maturity change.
Fastest path
Hosted API
Use managed endpoints when the goal is shipping quickly with minimal infrastructure work.
Higher isolation
Private deployment
Use isolated environments when customers or procurement require stricter boundaries.
Split the workload
Hybrid edge and cloud
Keep local control loops close to the robot and offload heavier model work to the cloud.
Common workloads
What robotics teams actually run
These are recurring workloads, not a fixed menu. The platform is meant to cover the full model layer, from perception to offline evaluation.
Serve online
Live action loops
Run robot policies and structured action outputs behind a stable endpoint surface for production systems.
VLA inference · Structured action decoding
Improve offline
Perception and simulation
Pair scene understanding with synthetic-data generation to expand coverage without adding another workflow.
Multimodal perception · Synthetic data
Validate safely
Replay and evaluation
Use replay pipelines and world-model experiments to compare behavior before a new version reaches the field.
World models · Evaluation and replay
Developer surface
Built for teams that want to ship, not babysit infrastructure
Use a clean API surface for model serving, replay workloads, and deployment management. Start with hosted endpoints and expand into custom or private deployments as requirements tighten.
- Unified API surface for live and offline workloads
- Custom checkpoint onboarding without a new integration path
- Deployment and version control built into the same product
curl -X POST https://api.sprag.ai/v1/inference \
-H "Authorization: Bearer sk-..." \
-H "Content-Type: application/json" \
-d '{
"model": "your-custom-vla-v3",
"inputs": {
"image": "base64:...",
"instruction": "Pick up the red block"
},
"parameters": {
"max_tokens": 256,
"output_format": "waypoints"
}
}'Production constraints
Infrastructure decisions that hold up in production
These are the controls robotics teams usually need once pilots turn into deployed systems. The page should read like operations planning, not like a marketing checklist.
Elastic capacity
Autoscaling
Scale up for batch jobs and scale down when robotics workloads go quiet.
Operational fit
Regional placement
Deploy endpoints close to where robots operate and where data stays compliant.
Boundary control
Access control
Use scoped keys, audit visibility, and team-level permissions to separate environments.
Safe rollout
Endpoint versioning
Pin production to a known-good release while the next version is still under evaluation.