Cyata.cloud
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Now in private beta — 2026

The orchestration network for AI at cloud scale.

Cyata runs your models, agents, and data pipelines across a globally distributed mesh — so inference stays close to users, costs stay predictable, and every workload self-schedules in real time.

Request access → Explore the platform
<30ms
Edge inference p50
38 regions
Anycast mesh
1-click
Model to production
Trusted by teams building the next compute era
Northwind AI Helio Particle Labs Monogram Verge Systems Lattice
One fabric, three primitives

Everything AI needs to run,
everywhere at once.

Cyata unifies orchestration, hosting, and data into a single control plane — so you stop wiring infrastructure and start shipping intelligence.

01 · Orchestration

Distributed scheduling network

A declarative control plane places every inference, batch, and agent job on the optimal node — balancing latency, cost, and GPU availability in real time.

  • Topology-aware routing
  • Preemptible & spot scheduling
  • Multi-region failover, automatic
02 · Hosting

Low-latency model fabric

Run open and proprietary LLMs on an anycast edge fabric with continuous KV-cache replication and sub-30ms first-token, globally.

  • One-command model deploy
  • Autoscaling to zero & to thousands
  • Quantization & speculative decode built-in
03 · Data mesh

Autonomous agent data grid

A self-organizing data mesh where agents discover, stream, and govern context — with lineage, permissions, and cost attribution by default.

  • Live streaming context for agents
  • Per-tenant lineage & policies
  • Event-driven materialized views
How it works

A single control plane,
from request to result.

Cyata sits between your application and the world's compute. You declare intent; the mesh handles placement, scaling, and data movement.

L4 · Your app

Application layer

SDKs & REST/gRPC endpoints

Python SDKcyata.run()
TypeScript SDK@cyata/sdk
REST / gRPCapi.cyata.cloud
Webhooksasync events
L3 · Control plane

Orchestrator

Scheduling, placement, policy

Schedulertopology-aware
Policy enginecost · latency · SLA
Autoscaler0 → N replicas
Failoverregion-aware
L2 · Data mesh

Data plane

Context, vectors, lineage

Vector storestreaming
Object fabricanycast
Lineage graphper-tenant
Streaming busKafka-compatible
L1 · Edge fabric

Compute

GPUs in 38 regions

GPU podsH100 · B200
Inferencersspeculative
Agentssandboxed
WorkersWASM runtime
Edge inference

Models live where your users do.

Deploy once; Cyata fans your model across 38 anycast regions with shared KV-cache and weight streaming. First token arrives in under 30ms from any major city — no CDN config, no regional replicas to babysit.

  • Anycast routing

    Requests land on the closest healthy inferencer automatically.

  • Scale to zero

    Idle regions cold-start in <800ms with pre-warmed snapshots.

  • Open & proprietary

    Bring Llama, Mistral, Qwen — or host your own weights privately.

cyata deploy
# deploy a model to the global mesh $ cyata deploy --model llama3-70b \ --regions auto --replicas 0..32 ✓ resolved weights (142GB) ✓ streaming to 38 regions ✓ inferencer ready endpoint → https://api.cyata.cloud/v1/llama3 p50 first-token → 28ms $ cyata agent run agent.yaml ✓ scheduled across 6 nodes
Developer experience

From laptop to global in one command.

A single CLI and SDK replace a stack of Kubernetes YAML, load balancers, and bespoke autoscalers. Declare what you want; Cyata ships it to the mesh.

  • Declarative workloads

    Models, agents, and pipelines as code — versioned & reviewable.

  • Observability included

    Traces, tokens, cost, and carbon per request — no extra setup.

  • Bring your own cloud

    Run on Cyata's fabric, your AWS/GCP, or both — same control plane.

Read the docs →
38
Anycast regions on the mesh
<30ms
Median first-token latency
12k
Inference requests / sec peak
63%
Avg. compute cost saved
From the teams building on Cyata

Built for the workload that didn't exist until now.

"We replaced an entire platform team's worth of infra with one Cyata declarative file. Our agents now run in 14 regions for less than we used to spend on one."

AM
Ada Mensah
CTO, Northwind AI

"First-token under 30ms in São Paulo and Frankfurt simultaneously — without us touching a deploy script. Cyata's mesh is the edge we always wanted."

RK
Ravi Kapoor
Staff Eng, Helio

"The data mesh gave our agents real-time context with lineage by default. Compliance stopped being a project and became a property of the platform."

SL
Sara Lindqvist
Head of Data, Verge Systems
FAQ

Questions, answered.

Both — and the data plane in between. Cyata's control plane schedules workloads onto its own anycast GPU fabric and onto your existing clouds, so it functions as a host, an orchestrator, and a unified data mesh under one API.
Yes. Upload private weights to an isolated tenant and deploy them with the same one-command workflow. Weights are encrypted at rest and never reused across tenants.
You pay for compute while inferencers are warm and a small per-region keep-alive while scaled to zero. Most teams spend less than their previous single-region deployment while serving globally.
Yes — SSE and WebSocket streaming, structured outputs, tool calling, and a sandboxed agent runtime with per-step resource limits are all first-class on the platform.
You choose residency per project. The data mesh can pin vectors, objects, and lineage to specific regions to meet GDPR, LGPD, or HIPAA constraints.

Run your AI where the world runs.

Join the private beta and deploy your first model to 38 regions before lunch.

Request access → Read the docs
Thanks — we'll be in touch.