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Sync Tensors.
Train Faster. Ship Models.

Distributed synchronization platform for ML pipelines. Seamlessly replicate tensors, checkpoints, and embeddings across your training cluster with sub-millisecond latency.

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Global edge network operational • 99.997% uptime

2.4M+

Tensors Synced Daily

850+

Research Teams

<12ms

P95 Sync Latency

99.997%

SLA Guarantee

Built for Distributed Training

Optimized for high-throughput tensor operations and checkpoint management at scale

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Tensor Replication

Multi-region tensor mirroring with automatic conflict resolution. Delta encoding reduces bandwidth by up to 94%.

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Checkpoint Streaming

Real-time checkpoint uploads during training. Resume from any point with guaranteed consistency across nodes.

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WebSocket Telemetry

Live metrics streaming via persistent connections. Monitor loss curves, gradients, and hardware stats in real-time.

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Auto-Sync Policies

Cron-based or event-driven synchronization. Configure per-tensor TTL, compression, and priority queues.

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Zero-Trust Security

mTLS authentication, per-request signing, and optional client-side encryption for sensitive model weights.

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Edge Caching

Global CDN for frequently accessed embeddings and pre-trained layers. Reduce origin load by 10-100x.

How TensorSync Works

Three simple steps to integrate with your ML pipeline

Install the CLI or SDK

One-line install for Python, Go, or Rust. Authenticate with your API key and configure sync targets.

Wrap your tensor operations

Use our PyTorch/TensorFlow wrappers or raw gRPC API. Tensors are automatically chunked, compressed, and routed.

Monitor & scale

Track sync health via dashboard or Prometheus metrics. Auto-scale bandwidth based on training phase.

Quick Integration Example

Full Documentation →

# Install TensorSync SDK for PyTorch
pip install tensorsync[torch]

# Initialize client with your API key
from tensorsync import TensorClient
client = TensorClient(api_key="tsk_••••••••", region="eu-central")

# Sync a tensor with automatic delta compression
client.sync(
    tensor=model.state_dict(),
    path="experiments/llama3/step-42k",
    compress=True,
    callback=lambda status: print(f"Synced: {status.progress}%")
)

# Stream training metrics via WebSocket
client.telemetry.stream(
    job_id="train-job-7x9",
    metrics=["loss", "grad_norm", "gpu_util"],
    endpoint="wss://edge.tensorsync.xyz/ws"
)

# Pull latest checkpoint for inference
checkpoint = client.pull(
    model="vision-transformer-base",
    version="v3.2.1",
    format="safetensors"
)

Simple, Usage-Based Pricing

Pay only for what you sync. No minimum commitments.

Researcher

$0/mo

For individual experiments

50 GB tensor storage
200 GB/month egress
5 sync jobs concurrent
Community support
Basic metrics dashboard

Create Free Account

Lab

$79/mo

For research teams & startups

2 TB tensor storage
5 TB/month egress
Unlimited sync jobs
Priority email support
Advanced analytics & alerts
Custom retention policies
Team access controls

Start 14-Day Trial

Enterprise

Custom

For production ML platforms

Unlimited storage & egress
Dedicated edge nodes
SLA 99.99%+
SSO/SAML & audit logs
On-premise deployment option
Dedicated solutions engineer

Contact Sales

Sync Tensors.Train Faster. Ship Models.