The Platform

One Unified Runtime.
Zero Compromises.

VertexStudio Runtime unifies model execution, agent orchestration, and observability into a single programmable surface.

router_config.yaml
# VertexStudio Unified Router v3
router:
  strategy: adaptive_latency
  nodes:
    - id: edge-npu-cluster
      type: edge
      latency_p99: 6ms
      cost_per_token: 0.00001
    - id: on-prem-h100
      type: gpu_cluster
      latency_p99: 42ms
      cost_per_token: 0.00008
    - id: cloud-burst
      type: cloud
      latency_p99: 180ms
      cost_per_token: 0.00020

  rules:
    - if: latency_sla < 10ms
      route_to: edge-npu-cluster
    - if: batch_size > 32
      route_to: on-prem-h100
    - default: cloud-burst

# ✓ Routing 2.4M req/day | Avg cost: $0.00003
agent_workflow.py
# VertexStudio Agent Orchestrator
from vertexstudio import AgentGraph, Memory

graph = AgentGraph("research_agent")

@graph.node
async def planner(state):
    plan = await llm.plan(state.task)
    return {"steps": plan.steps}

@graph.node
async def executor(state):
    results = []
    for step in state.steps:
        result = await tools[step.tool](
            step.args, memory=Memory.get()
        )
        results.append(result)
    return {"results": results}

@graph.node
async def synthesizer(state):
    return await llm.synthesize(state.results)

graph.edge("planner""executor""synthesizer")
# ✓ 25 max steps | Persistent memory | Auto-retry
observability.yaml
# VertexStudio Observability Stack
telemetry:
  traces:
    backend: opentelemetry
    sampling_rate: 1.0
    token_level: true
    agent_step_tracing: true

  metrics:
    backend: prometheus
    dashboards: grafana
    alerts:
      - name: latency_spike
        threshold: p99 > 50ms
        action: page_on_call
      - name: cost_overrun
        threshold: hourly_tokens > 10M
        action: auto_throttle

  cost_tracking:
    per_team: true
    per_model: true
    anomaly_detection: ml_based

# ✓ 99.97% uptime | <5min MTTR
.vertexstudio-ci.yaml
# VertexStudio ML CI/CD Pipeline
pipeline:
  trigger: push
  stages:

    - name: train
      runner: h100-cluster
      script: python train.py
      artifacts: model_checkpoint

    - name: evaluate
      gates:
        - metric: accuracy > 0.94
        - metric: latency_p99 < 10ms
        - metric: regression_delta < 1%

    - name: canary_deploy
      traffic_split: 5%
      duration: 30min
      auto_promote: on_success

    - name: production
      strategy: rolling
      zero_downtime: true
# ✓ Avg deploy time: 8min | 0 regressions
guardrails.py
# VertexStudio Guardrails Engine
from vertexstudio.guardrails import Pipeline

guards = Pipeline([
    PII_Detector(
        entities=["NAME","EMAIL","SSN","PHI"],
        action="redact",
        confidence=0.92
    ),
    ContentFilter(
        categories=["harmful","illegal","bias"],
        model="vertex-guard-v2"
    ),
    PromptInjection(
        scan="jailbreak|override|ignore",
        action="block_and_alert"
    ),
    AuditLogger(
        immutable=True,
        compliance=["SOC2","HIPAA","FedRAMP"]
    )
])

# ✓ <0.5ms overhead | 99.3% precision
System Architecture

Inference to Action
in Milliseconds

The VertexStudio stack from raw input to autonomous agent action — every hop engineered for speed, reliability, and cost efficiency.

Input Gateway Smart Router Edge Node Cloud GPU LLM Inference Agent Engine Action ~1ms ~5ms ~35ms ~6ms Total: < 50ms end-to-end

See the Runtime
on Your Workload

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