Phase 6: Production Readiness

Status: Not Started Duration: 8-10 weeks Team Size: 4-5 engineers (1 SRE, 1 ML engineer, 1 Python, 1 Rust, 1 DevOps) Prerequisites: Phase 5 complete (security hardening) Start Date: TBD Target Completion: TBD

Overview

Phase 6 prepares OctoLLM for production deployment at scale with autoscaling, cost optimization, compliance implementation, advanced performance tuning, and multi-tenancy support.

Key Deliverables:

Autoscaling - HorizontalPodAutoscaler with custom metrics, VPA, cluster autoscaling
Cost Optimization - Right-sizing, spot instances, reserved capacity, LLM cost reduction
Compliance - SOC 2 Type II, ISO 27001, GDPR, CCPA, HIPAA readiness
Advanced Performance - Rust rewrites, model fine-tuning, advanced caching, speculative execution
Multi-Tenancy - Tenant isolation, authentication, data isolation, usage-based billing

Success Criteria:

✅ Autoscaling handles 10x traffic spikes without degradation
✅ Cost per task reduced by 50% vs Phase 5
✅ SOC 2 Type II audit passed
✅ P99 latency <10s for critical tasks (vs <30s in Phase 1)
✅ Multi-tenant isolation tested and verified
✅ Production SLA: 99.9% uptime, <15s P95 latency
✅ Zero customer-impacting security incidents in first 90 days

Reference: docs/doc_phases/PHASE-6-COMPLETE-SPECIFICATIONS.md (14,000+ lines)

Sprint 6.1: Autoscaling [Week 33-34]

Duration: 2 weeks Team: 2 engineers (1 SRE, 1 DevOps) Prerequisites: Phase 3 complete (Kubernetes deployment) Priority: HIGH

Sprint Goals

Implement HorizontalPodAutoscaler (HPA) for all services
Configure VerticalPodAutoscaler (VPA) for right-sizing
Set up cluster autoscaling for node pools
Create custom metrics for LLM workload scaling
Test autoscaling under load
Document scaling policies and runbooks

Architecture Decisions

Scaling Strategy: Hybrid approach (HPA for replicas, VPA for resource requests, cluster autoscaler for nodes) Metrics: CPU, memory, custom (queue depth, task latency, LLM token rate) Target Utilization: 70% CPU/memory (allows headroom for spikes) Scale-Up Policy: Aggressive (30s stabilization) Scale-Down Policy: Conservative (5 minutes stabilization to prevent flapping) Min/Max Replicas: Service-dependent (orchestrator: 3-20, arms: 2-10)

Tasks

HorizontalPodAutoscaler Setup (10 hours)

Install Metrics Server (1 hour)

Deploy metrics-server in kube-system namespace
Verify metric collection

Code example:

# Install metrics-server
kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

# Verify metrics available
kubectl top nodes
kubectl top pods -n octollm

Files to create: k8s/monitoring/metrics-server.yaml

Create HPA for Orchestrator (2 hours)

Scale based on CPU and custom metrics (task queue depth)
Aggressive scale-up, conservative scale-down

Code example:

# k8s/autoscaling/orchestrator-hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: orchestrator-hpa
  namespace: octollm
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: orchestrator
  minReplicas: 3
  maxReplicas: 20
  metrics:
  # CPU-based scaling
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

  # Memory-based scaling
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 75

  # Custom metric: task queue depth
  - type: Pods
    pods:
      metric:
        name: task_queue_depth
      target:
        type: AverageValue
        averageValue: "10"  # Scale up if >10 tasks per pod

  behavior:
    scaleUp:
      stabilizationWindowSeconds: 30
      policies:
      - type: Percent
        value: 100  # Double replicas
        periodSeconds: 30
      - type: Pods
        value: 4  # Or add 4 pods
        periodSeconds: 30
      selectPolicy: Max  # Choose most aggressive

    scaleDown:
      stabilizationWindowSeconds: 300  # 5 minutes
      policies:
      - type: Percent
        value: 50  # Remove 50% of pods
        periodSeconds: 60
      - type: Pods
        value: 2  # Or remove 2 pods
        periodSeconds: 60
      selectPolicy: Min  # Choose most conservative

Files to create: k8s/autoscaling/orchestrator-hpa.yaml

Create HPAs for All Arms (4 hours)

Planner Arm: Scale on CPU + task decomposition requests
Executor Arm: Scale on CPU + active executions
Coder Arm: Scale on CPU + code generation requests
Judge Arm: Scale on CPU + validation requests
Safety Guardian Arm: Scale on CPU + PII detection requests
Retriever Arm: Scale on CPU + search requests

Code example (Executor Arm):

# k8s/autoscaling/executor-arm-hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: executor-arm-hpa
  namespace: octollm
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: executor-arm
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

  - type: Pods
    pods:
      metric:
        name: active_executions
      target:
        type: AverageValue
        averageValue: "3"  # Max 3 concurrent executions per pod

  behavior:
    scaleUp:
      stabilizationWindowSeconds: 30
      policies:
      - type: Percent
        value: 100
        periodSeconds: 30
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Pods
        value: 1
        periodSeconds: 60

Files to create: k8s/autoscaling/executor-arm-hpa.yaml, similar for other arms

Implement Custom Metrics Exporter (3 hours)

Expose application metrics for HPA (task queue depth, active executions)
Use Prometheus adapter

Code example:

# orchestrator/metrics/custom_metrics.py
from prometheus_client import Gauge
from typing import Dict, Any

# Define custom metrics for autoscaling
task_queue_depth_gauge = Gauge(
    'task_queue_depth',
    'Number of tasks waiting in queue per pod',
    ['pod_name']
)

active_tasks_gauge = Gauge(
    'active_tasks',
    'Number of tasks currently being processed',
    ['pod_name']
)

class CustomMetricsExporter:
    """Export custom metrics for HPA."""

    def __init__(self, pod_name: str):
        self.pod_name = pod_name

    def update_queue_depth(self, depth: int):
        """Update task queue depth metric."""
        task_queue_depth_gauge.labels(pod_name=self.pod_name).set(depth)

    def update_active_tasks(self, count: int):
        """Update active task count metric."""
        active_tasks_gauge.labels(pod_name=self.pod_name).set(count)

# k8s/monitoring/prometheus-adapter-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-adapter-config
  namespace: monitoring
data:
  config.yaml: |
    rules:
    - seriesQuery: 'task_queue_depth{namespace="octollm"}'
      resources:
        overrides:
          namespace: {resource: "namespace"}
          pod_name: {resource: "pod"}
      name:
        matches: "^(.*)$"
        as: "task_queue_depth"
      metricsQuery: 'avg_over_time(task_queue_depth{<<.LabelMatchers>>}[1m])'

    - seriesQuery: 'active_executions{namespace="octollm"}'
      resources:
        overrides:
          namespace: {resource: "namespace"}
          pod_name: {resource: "pod"}
      name:
        matches: "^(.*)$"
        as: "active_executions"
      metricsQuery: 'avg_over_time(active_executions{<<.LabelMatchers>>}[1m])'

Files to create: orchestrator/metrics/custom_metrics.py, k8s/monitoring/prometheus-adapter-config.yaml

VerticalPodAutoscaler Setup (4 hours)

Install VPA (1 hour)
- Deploy VPA components (recommender, updater, admission controller)
- Code example:
```
# Install VPA
git clone https://github.com/kubernetes/autoscaler.git
cd autoscaler/vertical-pod-autoscaler
./hack/vpa-up.sh
```
- Files to create: k8s/autoscaling/vpa-install.sh

Create VPA Policies (2 hours)

Recommendation-only mode for initial analysis
Auto mode for non-critical services

Code example:

# k8s/autoscaling/orchestrator-vpa.yaml
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: orchestrator-vpa
  namespace: octollm
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: orchestrator
  updatePolicy:
    updateMode: "Auto"  # Auto, Recreate, Initial, or Off
  resourcePolicy:
    containerPolicies:
    - containerName: orchestrator
      minAllowed:
        cpu: 500m
        memory: 1Gi
      maxAllowed:
        cpu: 8000m
        memory: 16Gi
      controlledResources:
      - cpu
      - memory

Files to create: k8s/autoscaling/orchestrator-vpa.yaml

Monitor VPA Recommendations (1 hour)

Analyze recommendations for all services
Adjust resource requests based on data

Code example:

# scripts/analyze_vpa_recommendations.sh
#!/bin/bash
set -e

echo "=== VPA Recommendations Analysis ==="

for deployment in orchestrator planner-arm executor-arm coder-arm judge-arm safety-guardian-arm retriever-arm; do
    echo "\n--- $deployment ---"

    # Get VPA recommendations
    kubectl get vpa ${deployment}-vpa -n octollm -o json | \
        jq -r '.status.recommendation.containerRecommendations[] |
               "Container: \(.containerName)\n  Current CPU: \(.target.cpu)\n  Recommended CPU: \(.upperBound.cpu)\n  Current Memory: \(.target.memory)\n  Recommended Memory: \(.upperBound.memory)"'
done

Files to create: scripts/analyze_vpa_recommendations.sh

Cluster Autoscaler Setup (4 hours)

Configure Cluster Autoscaler (2 hours)

Set up node pools with min/max sizes
Configure autoscaler for each cloud provider

Code example (GKE):

# k8s/autoscaling/cluster-autoscaler-gke.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: cluster-autoscaler
  namespace: kube-system
spec:
  replicas: 1
  template:
    spec:
      serviceAccountName: cluster-autoscaler
      containers:
      - image: k8s.gcr.io/autoscaling/cluster-autoscaler:v1.28.0
        name: cluster-autoscaler
        command:
        - ./cluster-autoscaler
        - --v=4
        - --stderrthreshold=info
        - --cloud-provider=gce
        - --skip-nodes-with-local-storage=false
        - --expander=least-waste
        - --node-group-auto-discovery=mig:namePrefix=octollm-node-pool
        - --balance-similar-node-groups
        - --skip-nodes-with-system-pods=false
        - --scale-down-delay-after-add=5m
        - --scale-down-unneeded-time=5m
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: cluster-autoscaler
rules:
- apiGroups: [""]
  resources: ["events", "endpoints"]
  verbs: ["create", "patch"]
- apiGroups: [""]
  resources: ["pods/eviction"]
  verbs: ["create"]
- apiGroups: [""]
  resources: ["pods/status"]
  verbs: ["update"]
- apiGroups: [""]
  resources: ["endpoints"]
  resourceNames: ["cluster-autoscaler"]
  verbs: ["get", "update"]
- apiGroups: [""]
  resources: ["nodes"]
  verbs: ["watch", "list", "get", "update"]
- apiGroups: [""]
  resources: ["pods", "services", "replicationcontrollers", "persistentvolumeclaims", "persistentvolumes"]
  verbs: ["watch", "list", "get"]
- apiGroups: ["extensions"]
  resources: ["replicasets", "daemonsets"]
  verbs: ["watch", "list", "get"]
- apiGroups: ["policy"]
  resources: ["poddisruptionbudgets"]
  verbs: ["watch", "list"]
- apiGroups: ["apps"]
  resources: ["statefulsets", "replicasets", "daemonsets"]
  verbs: ["watch", "list", "get"]
- apiGroups: ["storage.k8s.io"]
  resources: ["storageclasses", "csinodes", "csidrivers", "csistoragecapacities"]
  verbs: ["watch", "list", "get"]
- apiGroups: ["batch", "extensions"]
  resources: ["jobs"]
  verbs: ["get", "list", "watch", "patch"]
- apiGroups: ["coordination.k8s.io"]
  resources: ["leases"]
  verbs: ["create"]
- apiGroups: ["coordination.k8s.io"]
  resourceNames: ["cluster-autoscaler"]
  resources: ["leases"]
  verbs: ["get", "update"]

Files to create: k8s/autoscaling/cluster-autoscaler-gke.yaml

Create Node Pools with Labels (1 hour)

Separate pools for CPU-intensive and memory-intensive workloads
Use node affinity to schedule arms appropriately

Code example:

# terraform/gke-node-pools.tf
resource "google_container_node_pool" "cpu_optimized" {
  name       = "cpu-optimized-pool"
  cluster    = google_container_cluster.octollm.name
  node_count = 2

  autoscaling {
    min_node_count = 2
    max_node_count = 20
  }

  node_config {
    machine_type = "n2-highcpu-16"  # 16 vCPU, 16 GB RAM

    labels = {
      workload-type = "cpu-optimized"
    }

    taint {
      key    = "workload-type"
      value  = "cpu-optimized"
      effect = "NO_SCHEDULE"
    }
  }
}

resource "google_container_node_pool" "memory_optimized" {
  name       = "memory-optimized-pool"
  cluster    = google_container_cluster.octollm.name
  node_count = 2

  autoscaling {
    min_node_count = 2
    max_node_count = 10
  }

  node_config {
    machine_type = "n2-highmem-8"  # 8 vCPU, 64 GB RAM

    labels = {
      workload-type = "memory-optimized"
    }

    taint {
      key    = "workload-type"
      value  = "memory-optimized"
      effect = "NO_SCHEDULE"
    }
  }
}

Files to create: terraform/gke-node-pools.tf

Test Cluster Autoscaling (1 hour)
- Simulate load spike
- Verify nodes added automatically
- Verify nodes removed after scale-down
- Files to create: scripts/test_cluster_autoscaling.sh

Load Testing (4 hours)

Create Load Test Suite (2 hours)

Use k6 or Locust for load generation
Simulate realistic traffic patterns

Code example:

// tests/load/autoscaling_test.js
import http from 'k6/http';
import { check, sleep } from 'k6';
import { Rate } from 'k6/metrics';

const failureRate = new Rate('failed_requests');

export let options = {
  stages: [
    { duration: '2m', target: 10 },   // Ramp up to 10 users
    { duration: '5m', target: 10 },   // Steady state
    { duration: '2m', target: 50 },   // Spike to 50 users
    { duration: '5m', target: 50 },   // Hold spike
    { duration: '2m', target: 100 },  // Extreme spike
    { duration: '5m', target: 100 },  // Hold extreme spike
    { duration: '5m', target: 0 },    // Ramp down
  ],
  thresholds: {
    'failed_requests': ['rate<0.01'],  // <1% failure rate
    'http_req_duration': ['p(95)<15000'],  // P95 latency <15s
  },
};

const BASE_URL = 'http://octollm-gateway.octollm.svc.cluster.local';

export default function () {
  // Submit a task
  const payload = JSON.stringify({
    goal: 'Analyze this code for security vulnerabilities',
    constraints: {
      max_cost_tokens: 10000,
      max_time_seconds: 300
    },
    context: {
      code: 'def login(username, password):\n    query = f"SELECT * FROM users WHERE username=\'{username}\' AND password=\'{password}\'"'
    }
  });

  const params = {
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer test-token-123'
    },
  };

  const response = http.post(`${BASE_URL}/tasks`, payload, params);

  check(response, {
    'status is 201': (r) => r.status === 201,
    'has task_id': (r) => r.json('task_id') !== undefined,
  }) || failureRate.add(1);

  sleep(1);
}

Files to create: tests/load/autoscaling_test.js

Run Load Tests (2 hours)

Execute load tests against staging environment
Monitor autoscaling behavior
Verify SLA compliance (99.9% uptime, <15s P95 latency)
Generate load test report

Code example:

# scripts/run_load_test.sh
#!/bin/bash
set -e

echo "Starting autoscaling load test..."

# Run k6 load test
k6 run --out json=load_test_results.json tests/load/autoscaling_test.js

# Analyze results
python scripts/analyze_load_test.py load_test_results.json

# Check HPA events
echo "\n=== HPA Events ==="
kubectl get events -n octollm --field-selector involvedObject.kind=HorizontalPodAutoscaler

# Check pod scaling timeline
echo "\n=== Pod Count Timeline ==="
kubectl get pods -n octollm -l app=orchestrator --watch

echo "Load test complete. Review load_test_results.json for detailed metrics."

Files to create: scripts/run_load_test.sh, scripts/analyze_load_test.py

Testing Requirements

Unit Tests

HPA configuration validation (5 test cases)
VPA policy validation (5 test cases)
Custom metrics exporter (10 test cases)

Integration Tests

HPA scaling behavior (scale up, scale down, flapping prevention)
VPA resource adjustment
Cluster autoscaler node provisioning
End-to-end autoscaling under load

Performance Tests

Load test: 10x traffic spike (verify autoscaling handles without degradation)
Stress test: 100x traffic spike (verify graceful degradation)
Soak test: 24-hour sustained load (verify no memory leaks or resource drift)

Documentation Deliverables

Autoscaling architecture diagram
HPA configuration guide
VPA tuning guide
Cluster autoscaler runbook
Load testing procedures
Troubleshooting guide (scaling issues)

Success Criteria

HPA scales services within 60 seconds of load increase
VPA recommendations reduce resource waste by >30%
Cluster autoscaler provisions nodes within 5 minutes
Load test passes with <1% failure rate and P95 latency <15s
Cost per task unchanged despite autoscaling overhead

Common Pitfalls

HPA Flapping: Too aggressive scale-down causes constant scaling up/down—use longer stabilization windows
VPA Disruption: Auto mode restarts pods—use recommendation mode for critical services
Node Affinity Conflicts: Pods can't schedule if no matching nodes—ensure default node pool
Custom Metrics Lag: Prometheus scrape interval causes scaling delays—reduce to 15s for autoscaling metrics
Resource Limits: HPA can't scale if pods hit resource limits—ensure limits > requests

Estimated Effort

Development: 22 hours
Testing: 6 hours
Documentation: 3 hours
Total: 31 hours (~2 weeks for 2 engineers)

Dependencies

Prerequisites: Phase 3 complete (Kubernetes deployment, monitoring stack)
Blocking: None
Blocked By: None

Sprint 6.2: Cost Optimization [Week 35-36]

Duration: 2 weeks Team: 3 engineers (1 SRE, 1 ML engineer, 1 Python) Prerequisites: Sprint 6.1 complete (autoscaling) Priority: HIGH

Sprint Goals

Right-size all services based on actual usage
Implement spot/preemptible instances for non-critical workloads
Purchase reserved capacity for baseline load
Optimize LLM costs (prompt caching, smaller models, fine-tuning)
Implement request batching and deduplication
Reduce cost per task by 50% vs Phase 5

Architecture Decisions

Compute: Mix of on-demand (20%), spot instances (60%), reserved capacity (20%) LLM Strategy: Use cheapest model per task type (GPT-3.5 for simple, GPT-4 for complex) Caching: Aggressive prompt caching with semantic similarity matching Batching: Batch similar requests to reduce LLM API overhead Fine-Tuning: Fine-tune smaller models (Mistral 7B) to replace GPT-3.5 for common patterns

Tasks

Right-Sizing (8 hours)

Analyze Resource Usage (3 hours)

Use VPA recommendations and Prometheus metrics
Identify over-provisioned services

Code example:

# scripts/analyze_resource_usage.py
import requests
from datetime import datetime, timedelta
from typing import Dict, List, Any

class ResourceAnalyzer:
    """Analyze resource usage and identify optimization opportunities."""

    def __init__(self, prometheus_url: str):
        self.prometheus_url = prometheus_url

    def analyze_service(
        self,
        service_name: str,
        days_lookback: int = 30
    ) -> Dict[str, Any]:
        """Analyze resource usage for a service."""

        end_time = datetime.now()
        start_time = end_time - timedelta(days=days_lookback)

        # Query CPU usage
        cpu_query = f'''
            avg_over_time(
                rate(container_cpu_usage_seconds_total{{
                    namespace="octollm",
                    pod=~"{service_name}-.*"
                }}[5m])[{days_lookback}d:5m]
            )
        '''

        cpu_usage = self._query_prometheus(cpu_query)

        # Query memory usage
        memory_query = f'''
            avg_over_time(
                container_memory_working_set_bytes{{
                    namespace="octollm",
                    pod=~"{service_name}-.*"
                }}[{days_lookback}d:5m]
            )
        '''

        memory_usage = self._query_prometheus(memory_query)

        # Get current resource requests
        current_requests = self._get_current_requests(service_name)

        # Calculate waste
        cpu_waste_percent = (
            (current_requests['cpu'] - cpu_usage['p95']) /
            current_requests['cpu'] * 100
        )

        memory_waste_percent = (
            (current_requests['memory'] - memory_usage['p95']) /
            current_requests['memory'] * 100
        )

        return {
            'service': service_name,
            'current_cpu_request': current_requests['cpu'],
            'p95_cpu_usage': cpu_usage['p95'],
            'cpu_waste_percent': cpu_waste_percent,
            'current_memory_request': current_requests['memory'],
            'p95_memory_usage': memory_usage['p95'],
            'memory_waste_percent': memory_waste_percent,
            'recommendation': self._generate_recommendation(
                current_requests,
                cpu_usage,
                memory_usage
            )
        }

    def _query_prometheus(self, query: str) -> Dict[str, float]:
        """Query Prometheus and return percentile statistics."""
        # Implementation: Call Prometheus API, calculate percentiles
        pass

    def _get_current_requests(self, service_name: str) -> Dict[str, float]:
        """Get current resource requests from Kubernetes."""
        # Implementation: Call Kubernetes API
        pass

    def _generate_recommendation(
        self,
        current: Dict[str, float],
        cpu_usage: Dict[str, float],
        memory_usage: Dict[str, float]
    ) -> str:
        """Generate right-sizing recommendation."""

        # Add 20% buffer to P95 usage for headroom
        recommended_cpu = cpu_usage['p95'] * 1.2
        recommended_memory = memory_usage['p95'] * 1.2

        if recommended_cpu < current['cpu'] * 0.8:
            return f"Reduce CPU request to {recommended_cpu:.2f} cores"
        elif recommended_cpu > current['cpu'] * 1.2:
            return f"Increase CPU request to {recommended_cpu:.2f} cores"

        if recommended_memory < current['memory'] * 0.8:
            return f"Reduce memory request to {recommended_memory / 1e9:.2f} GB"
        elif recommended_memory > current['memory'] * 1.2:
            return f"Increase memory request to {recommended_memory / 1e9:.2f} GB"

        return "Current sizing is appropriate"

Files to create: scripts/analyze_resource_usage.py

Apply Right-Sizing (2 hours)
- Update resource requests/limits for all services
- Deploy changes incrementally
- Monitor for performance regressions
- Files to update: All deployment YAML files
Calculate Cost Savings (1 hour)
- Compare costs before/after right-sizing
- Generate cost savings report
- Files to create: docs/cost-optimization/right-sizing-report.md

Set Up Cost Monitoring Dashboard (2 hours)

Grafana dashboard for cost tracking
Alert on cost anomalies

Code example:

{
  "dashboard": {
    "title": "OctoLLM Cost Monitoring",
    "panels": [
      {
        "title": "Total Monthly Cost",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(kube_pod_container_resource_requests{namespace='octollm'} * on(node) group_left() node_cost_hourly) * 730"
          }
        ]
      },
      {
        "title": "Cost by Service",
        "type": "piechart",
        "targets": [
          {
            "expr": "sum by (pod) (kube_pod_container_resource_requests{namespace='octollm'} * on(node) group_left() node_cost_hourly) * 730"
          }
        ]
      },
      {
        "title": "LLM API Costs",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(llm_cost_usd_total)"
          }
        ]
      }
    ]
  }
}

Files to create: k8s/monitoring/grafana-dashboards/cost-monitoring.json

Spot Instances (6 hours)

Create Spot Instance Node Pool (2 hours)

Configure with appropriate labels and taints
Set up fallback to on-demand if spot unavailable

Code example:

# terraform/gke-spot-node-pool.tf
resource "google_container_node_pool" "spot_pool" {
  name       = "spot-pool"
  cluster    = google_container_cluster.octollm.name
  node_count = 5

  autoscaling {
    min_node_count = 3
    max_node_count = 50
  }

  node_config {
    machine_type = "n2-standard-8"
    spot         = true  # Preemptible/spot instance

    labels = {
      workload-type = "spot"
    }

    taint {
      key    = "workload-type"
      value  = "spot"
      effect = "NO_SCHEDULE"
    }

    metadata = {
      disable-legacy-endpoints = "true"
    }
  }
}

Files to create: terraform/gke-spot-node-pool.tf

Configure Services for Spot Tolerance (3 hours)

Add node affinity to prefer spot instances
Implement graceful shutdown for preemption
Add PodDisruptionBudgets to ensure availability

Code example:

# k8s/arms/executor-deployment.yaml (updated for spot)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: executor-arm
  namespace: octollm
spec:
  replicas: 5
  template:
    spec:
      # Prefer spot instances, fallback to on-demand
      affinity:
        nodeAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
          - weight: 100
            preference:
              matchExpressions:
              - key: workload-type
                operator: In
                values:
                - spot

      tolerations:
      - key: workload-type
        operator: Equal
        value: spot
        effect: NoSchedule

      # Graceful shutdown for preemption
      terminationGracePeriodSeconds: 60

      containers:
      - name: executor-arm
        lifecycle:
          preStop:
            exec:
              command: ["/bin/sh", "-c", "sleep 30"]  # Drain connections
---
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: executor-arm-pdb
  namespace: octollm
spec:
  minAvailable: 2  # Ensure at least 2 replicas always available
  selector:
    matchLabels:
      app: executor-arm

Files to update: All arm deployment YAML files

Test Spot Instance Preemption (1 hour)
- Simulate preemption events
- Verify graceful failover
- Files to create: scripts/test_spot_preemption.sh

LLM Cost Optimization (10 hours)

Implement Prompt Caching (4 hours)

Cache LLM responses with semantic similarity matching
Use vector embeddings to find similar prompts

Code example:

# orchestrator/llm/cached_client.py
from openai import AsyncOpenAI
from qdrant_client import QdrantClient
from sentence_transformers import SentenceTransformer
from typing import Dict, Any, Optional, List
import hashlib
import json

class CachedLLMClient:
    """LLM client with semantic caching."""

    def __init__(
        self,
        openai_client: AsyncOpenAI,
        qdrant_client: QdrantClient,
        embedding_model: SentenceTransformer,
        similarity_threshold: float = 0.95,
        collection_name: str = "llm_cache"
    ):
        self.openai = openai_client
        self.qdrant = qdrant_client
        self.embedding_model = embedding_model
        self.similarity_threshold = similarity_threshold
        self.collection_name = collection_name

        # Create collection if not exists
        self._init_collection()

    def _init_collection(self):
        """Initialize Qdrant collection for cache."""
        from qdrant_client.models import Distance, VectorParams

        try:
            self.qdrant.create_collection(
                collection_name=self.collection_name,
                vectors_config=VectorParams(
                    size=384,  # all-MiniLM-L6-v2 embedding size
                    distance=Distance.COSINE
                )
            )
        except Exception:
            pass  # Collection already exists

    async def chat_completion(
        self,
        messages: List[Dict[str, str]],
        model: str = "gpt-4-turbo-preview",
        temperature: float = 0.0,
        **kwargs
    ) -> Dict[str, Any]:
        """Create chat completion with semantic caching."""

        # Create cache key from messages
        prompt = self._messages_to_text(messages)
        cache_key = self._create_cache_key(prompt, model, temperature)

        # Check exact match cache first (fast)
        exact_match = await self._check_exact_cache(cache_key)
        if exact_match:
            return exact_match

        # Check semantic similarity cache (slower)
        if temperature == 0.0:  # Only use semantic cache for deterministic requests
            semantic_match = await self._check_semantic_cache(prompt, model)
            if semantic_match:
                return semantic_match

        # Cache miss - call LLM
        response = await self.openai.chat.completions.create(
            messages=messages,
            model=model,
            temperature=temperature,
            **kwargs
        )

        # Store in cache
        await self._store_in_cache(cache_key, prompt, model, response)

        return response.model_dump()

    def _messages_to_text(self, messages: List[Dict[str, str]]) -> str:
        """Convert messages to single text for embedding."""
        return "\n".join(f"{m['role']}: {m['content']}" for m in messages)

    def _create_cache_key(
        self,
        prompt: str,
        model: str,
        temperature: float
    ) -> str:
        """Create deterministic cache key."""
        key_input = f"{prompt}|{model}|{temperature}"
        return hashlib.sha256(key_input.encode()).hexdigest()

    async def _check_exact_cache(self, cache_key: str) -> Optional[Dict[str, Any]]:
        """Check Redis for exact cache hit."""
        # Implementation: Query Redis
        pass

    async def _check_semantic_cache(
        self,
        prompt: str,
        model: str
    ) -> Optional[Dict[str, Any]]:
        """Check Qdrant for semantically similar cached responses."""

        # Generate embedding
        embedding = self.embedding_model.encode(prompt).tolist()

        # Search for similar prompts
        results = self.qdrant.search(
            collection_name=self.collection_name,
            query_vector=embedding,
            limit=1,
            score_threshold=self.similarity_threshold,
            query_filter={
                "must": [
                    {"key": "model", "match": {"value": model}}
                ]
            }
        )

        if results and results[0].score >= self.similarity_threshold:
            # Cache hit
            cached_response = results[0].payload["response"]
            return json.loads(cached_response)

        return None

    async def _store_in_cache(
        self,
        cache_key: str,
        prompt: str,
        model: str,
        response: Any
    ):
        """Store response in both exact and semantic caches."""

        # Store in Redis (exact match)
        # Implementation: Store in Redis with TTL

        # Store in Qdrant (semantic similarity)
        embedding = self.embedding_model.encode(prompt).tolist()

        self.qdrant.upsert(
            collection_name=self.collection_name,
            points=[
                {
                    "id": cache_key,
                    "vector": embedding,
                    "payload": {
                        "prompt": prompt,
                        "model": model,
                        "response": json.dumps(response.model_dump()),
                        "timestamp": datetime.utcnow().isoformat()
                    }
                }
            ]
        )

Files to create: orchestrator/llm/cached_client.py

Implement Model Selection Strategy (3 hours)

Route to cheapest model capable of solving task
Use complexity classifier to determine required model

Code example:

# orchestrator/llm/model_selector.py
from typing import Dict, Any, List
import re

class ModelSelector:
    """Select cheapest LLM model for a given task."""

    # Cost per 1M tokens (input/output)
    MODEL_COSTS = {
        "gpt-4-turbo-preview": (10.00, 30.00),
        "gpt-4": (30.00, 60.00),
        "gpt-3.5-turbo": (0.50, 1.50),
        "mistral-7b-instruct": (0.20, 0.20),  # Self-hosted
    }

    # Model capabilities
    MODEL_CAPABILITIES = {
        "gpt-4-turbo-preview": {"reasoning": 10, "coding": 9, "knowledge": 10},
        "gpt-4": {"reasoning": 10, "coding": 10, "knowledge": 10},
        "gpt-3.5-turbo": {"reasoning": 7, "coding": 7, "knowledge": 8},
        "mistral-7b-instruct": {"reasoning": 6, "coding": 6, "knowledge": 6},
    }

    def select_model(
        self,
        task_description: str,
        required_capability: str = "reasoning",
        min_capability_score: int = 7
    ) -> str:
        """Select cheapest model meeting requirements."""

        # Determine task complexity
        complexity = self._assess_complexity(task_description)

        # Filter models by capability
        suitable_models = [
            model for model, capabilities in self.MODEL_CAPABILITIES.items()
            if capabilities.get(required_capability, 0) >= min(complexity, min_capability_score)
        ]

        if not suitable_models:
            # Fallback to most capable model
            return "gpt-4-turbo-preview"

        # Select cheapest suitable model
        cheapest = min(
            suitable_models,
            key=lambda m: sum(self.MODEL_COSTS[m])
        )

        return cheapest

    def _assess_complexity(self, task_description: str) -> int:
        """Assess task complexity (1-10 scale)."""

        complexity_indicators = {
            # High complexity
            r"multi-step|complex|advanced|intricate": 9,
            r"requires.*reasoning|logical.*deduction": 8,
            r"analyze|evaluate|compare": 7,

            # Medium complexity
            r"explain|describe|summarize": 6,
            r"translate|convert|transform": 5,

            # Low complexity
            r"list|enumerate|identify": 4,
            r"yes|no|true|false": 3,
            r"simple|basic|straightforward": 2,
        }

        max_complexity = 5  # Default medium complexity
        for pattern, score in complexity_indicators.items():
            if re.search(pattern, task_description, re.IGNORECASE):
                max_complexity = max(max_complexity, score)

        return max_complexity

Files to create: orchestrator/llm/model_selector.py

Fine-Tune Specialist Models (3 hours)

Collect training data from task logs
Fine-tune Mistral 7B for common patterns
Replace GPT-3.5 calls with fine-tuned model

Code example:

# scripts/fine_tune_specialist.py
from datasets import Dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TrainingArguments,
    Trainer
)
from typing import List, Dict, Any
import json

class SpecialistModelTrainer:
    """Fine-tune specialist models for common tasks."""

    def __init__(self, base_model: str = "mistralai/Mistral-7B-Instruct-v0.2"):
        self.base_model = base_model
        self.tokenizer = AutoTokenizer.from_pretrained(base_model)
        self.model = AutoModelForCausalLM.from_pretrained(
            base_model,
            load_in_4bit=True,  # QLoRA for efficient fine-tuning
            device_map="auto"
        )

    def prepare_training_data(
        self,
        task_logs_path: str,
        task_type: str
    ) -> Dataset:
        """Prepare training data from task logs."""

        # Load task logs
        with open(task_logs_path) as f:
            logs = [json.loads(line) for line in f]

        # Filter by task type
        relevant_logs = [
            log for log in logs
            if log.get("task_type") == task_type
        ]

        # Format for instruction tuning
        training_examples = []
        for log in relevant_logs:
            training_examples.append({
                "instruction": log["input_prompt"],
                "output": log["llm_response"]
            })

        return Dataset.from_list(training_examples)

    def fine_tune(
        self,
        dataset: Dataset,
        output_dir: str,
        num_epochs: int = 3
    ):
        """Fine-tune model on dataset."""

        training_args = TrainingArguments(
            output_dir=output_dir,
            num_train_epochs=num_epochs,
            per_device_train_batch_size=4,
            gradient_accumulation_steps=4,
            learning_rate=2e-5,
            warmup_steps=100,
            logging_steps=10,
            save_steps=100,
            evaluation_strategy="steps",
            eval_steps=100,
            load_best_model_at_end=True
        )

        trainer = Trainer(
            model=self.model,
            args=training_args,
            train_dataset=dataset,
            tokenizer=self.tokenizer
        )

        trainer.train()
        trainer.save_model(output_dir)

if __name__ == "__main__":
    trainer = SpecialistModelTrainer()

    # Fine-tune for code review task
    dataset = trainer.prepare_training_data(
        task_logs_path="logs/task_logs.jsonl",
        task_type="code_review"
    )

    trainer.fine_tune(
        dataset=dataset,
        output_dir="models/mistral-7b-code-review"
    )

Files to create: scripts/fine_tune_specialist.py

Request Optimization (4 hours)

Implement Request Batching (2 hours)
- Batch similar requests to reduce API overhead
- Use async processing with batch windows
- Files to create: orchestrator/llm/batch_processor.py
Implement Request Deduplication (2 hours)
- Detect duplicate requests in flight
- Return cached result to duplicate requesters
- Files to create: orchestrator/middleware/deduplication.py

Testing Requirements

Unit Tests

Resource analyzer calculations (10 test cases)
Model selector logic (15 test cases)
Prompt caching (20 test cases)
Request batching (10 test cases)

Integration Tests

End-to-end cost tracking
Spot instance failover
LLM cost reduction verification
Fine-tuned model accuracy vs base model

Performance Tests

Cost per task benchmark (before/after optimization)
Cache hit rate measurement (target >60%)
Fine-tuned model latency vs GPT-3.5

Documentation Deliverables

Cost optimization strategy guide
Right-sizing procedures
Spot instance configuration guide
LLM cost reduction techniques
Fine-tuning runbooks

Success Criteria

Cost per task reduced by 50% vs Phase 5
Resource waste reduced by >30%
LLM cache hit rate >60%
Fine-tuned models achieve >95% accuracy of GPT-3.5 on target tasks
Zero performance degradation from cost optimizations

Common Pitfalls

Over-Optimization: Aggressive right-sizing causes OOM kills—maintain 20% buffer
Spot Instance Unavailability: Spot capacity shortages in peak hours—keep on-demand fallback
Cache Staleness: Cached responses become outdated—implement TTL and versioning
Fine-Tuning Overfitting: Model only works on training distribution—use diverse dataset
Premature Optimization: Optimize before understanding usage patterns—collect 30+ days data first

Estimated Effort

Development: 28 hours
Testing: 6 hours
Documentation: 3 hours
Total: 37 hours (~2 weeks for 3 engineers)

Dependencies

Prerequisites: Sprint 6.1 (autoscaling), Phase 3 (monitoring)
Blocking: None
Blocked By: None

Sprint 6.3: Compliance Implementation [Week 37-38]

(Abbreviated for space - full version would be 1,200-1,500 lines)

Sprint Goals

Achieve SOC 2 Type II compliance
Implement ISO 27001 controls
Ensure GDPR compliance (data protection, right to erasure)
Ensure CCPA compliance (opt-out, data disclosure)
HIPAA readiness (encryption, access controls, audit logs)
Pass external compliance audits

Key Tasks (Summary)

SOC 2 Type II Preparation (12 hours)
- Implement security controls (TSC)
- Document policies and procedures
- Conduct internal audit
- Contract external auditor
ISO 27001 Implementation (10 hours)
- Risk assessment and treatment
- Information security policies
- Access control procedures
- Incident management
GDPR Compliance (8 hours)
- Data protection impact assessment (DPIA)
- Consent management
- Right to erasure implementation
- Data portability
CCPA Compliance (6 hours)
- Consumer rights implementation (opt-out, disclosure)
- Privacy policy updates
- Data inventory and mapping
HIPAA Readiness (6 hours)
- Encryption at rest and in transit
- Access controls and audit logs
- Business associate agreements (BAA)
- Breach notification procedures

Estimated Effort: 42 hours (~2 weeks for 2 engineers)

Sprint 6.4: Advanced Performance [Week 39-40]

(Abbreviated for space - full version would be 1,200-1,500 lines)

Sprint Goals

Rewrite performance-critical components in Rust
Fine-tune LLM models for specific tasks
Implement advanced caching strategies (multi-tier, predictive)
Add speculative execution for anticipated tasks
Achieve P99 latency <10s (vs <30s in Phase 1)
Reduce LLM API costs by additional 30%

Key Tasks (Summary)

Rust Performance Rewrites (16 hours)
- Rewrite Planner Arm in Rust (2x faster)
- Rewrite Judge Arm in Rust (3x faster)
- Optimize Reflex Layer (target <5ms P95)
Model Fine-Tuning (12 hours)
- Fine-tune task decomposition model
- Fine-tune code generation model
- Fine-tune validation model
- Deploy fine-tuned models
Advanced Caching (10 hours)
- Multi-tier caching (L1: Redis, L2: Qdrant, L3: S3)
- Predictive cache warming
- Cache invalidation strategies
Speculative Execution (8 hours)
- Predict next likely task based on patterns
- Precompute results in background
- Serve from cache when requested
Performance Benchmarking (4 hours)
- Comprehensive performance test suite
- Compare Phase 6 vs Phase 1 metrics
- Latency reduction verification

Estimated Effort: 50 hours (~2.5 weeks for 2 engineers)

Sprint 6.5: Multi-Tenancy [Week 41-42]

(Abbreviated for space - full version would be 1,200-1,500 lines)

Sprint Goals

Implement tenant isolation (network, storage, compute)
Add authentication and authorization per tenant
Implement usage-based billing
Create tenant management portal
Test multi-tenant security isolation
Document multi-tenancy architecture

Key Tasks (Summary)

Tenant Isolation (12 hours)
- Kubernetes namespaces per tenant
- Network policies for isolation
- Separate database schemas
- Qdrant collections per tenant
Authentication and Authorization (10 hours)
- Multi-tenant Auth0 integration
- Tenant-scoped API keys
- Role-based access control (RBAC) per tenant
Usage-Based Billing (10 hours)
- Meter API calls, LLM tokens, compute time
- Integrate with Stripe for billing
- Generate invoices and usage reports
Tenant Management Portal (8 hours)
- React admin dashboard
- Tenant provisioning and configuration
- Usage analytics and billing
Security Testing (6 hours)
- Tenant isolation verification
- Cross-tenant access attempts (should all fail)
- Data leakage testing

Estimated Effort: 46 hours (~2.5 weeks for 2 engineers)

Phase 6 Summary

Total Tasks: 80+ production readiness tasks across 5 sprints Estimated Duration: 8-10 weeks with 4-5 engineers Total Estimated Hours: ~206 hours development + ~40 hours testing + ~25 hours documentation = 271 hours

Deliverables:

Autoscaling infrastructure (HPA, VPA, cluster autoscaler)
50% cost reduction vs Phase 5
SOC 2 Type II, ISO 27001, GDPR, CCPA compliance
P99 latency <10s (67% improvement vs Phase 1)
Multi-tenant production platform

Completion Checklist:

Autoscaling handles 10x traffic spikes
Cost per task reduced by 50%
SOC 2 Type II audit passed
P99 latency <10s achieved
Multi-tenant isolation verified
Production SLA: 99.9% uptime, <15s P95 latency
Zero security incidents in first 90 days
Public API and documentation published

Next Steps: Production launch and customer onboarding

Document Version: 1.0 Last Updated: 2025-11-10 Maintained By: OctoLLM Production Team

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