Key Google Cloud (GCP) Changes — September to October 2025: Impact, Actions & Corporate Playbook
Comprehensive operational guide for cloud architects, DevOps, SREs, monitoring teams and security/legal: what changed in GCP during Sept–Oct 2025, why it matters, and step-by-step remediation & migration actions.
Published: October 23, 2025 Length: 4,000+ words
Executive summary
Between September and October 2025 Google Cloud introduced several platform-level changes that directly affect corporate cloud operations. The most material items we cover in this guide are:
- Cloud Logging quota model change — move from write requests per minute to volume-based regional quotas.
- Cloud Run GA features — multi-region deployments, .env file env-var support, and GPU support for jobs/services.
- Monitoring query language shift — deprecation of MQL in favour of PromQL-style APIs for some metrics & monitoring flows.
- Broader AI/ML & agent-capabilities announcements around Vertex AI and multi-agent workflows.
- Regional and service changes (identity, service-account behaviours), plus ongoing expansion in APAC/India.
Why this matters for enterprises: quota models and query language changes can break ingestion, alerting, and autoscaling — causing operational incidents. New serverless and GPU features create opportunities but require CI/CD, IaC and governance updates.
1. Cloud Logging quota model change — what changed
Change: In September 2025 Google Cloud removed the legacy quota dimension of write requests per minute for Cloud Logging and replaced it with volume-based regional quotas. This means quotas are now measured primarily by ingested bytes (or volume) across regions rather than simple write-ops counts.
Impact summary: teams relying on per-minute request assumptions (for throttling logic, autoscalers or per-minute alert thresholds) will see their operational behaviour change. High-frequency low-payload logs may appear less impactful, while fewer heavy logs (large payloads) can more quickly saturate regional quotas.
Detailed impact analysis
- Ingestion delays & failed writes: if a region hits a volume quota, further log ingestion can be delayed or rejected until quota replenishment.
- Dashboard & alerting noise: dashboards or SLOs that assume per-minute write quotas may under-report risk because volume spikes are rare but severe.
- Cost implications: ingesting large volumes in a single region may incur higher costs or unpredictable spikes if not monitored carefully.
Action checklist (immediate)
- Identify all ingestion pipelines that produce logs (apps, audit logs, middleware, agents).
- Measure current ingestion volume by region for the last 30–90 days.
- Enable/verify alerts for quota saturation and set thresholds for regional volume, not just request rate.
- Introduce log filtering/compression: reduce verbose logs, sample telemetry, batch or compress logs before send.
- Consider log routing to multiple regions where appropriate (and compliant).
Example: Terraform snippet to configure Logging sink with filter and routing (sample)
# Example: logging sink with filter to reduce verbose logs
resource "google_logging_project_sink" "reduced_sink" {
name = "reduced-logs"
project = var.project_id
destination = "storage.googleapis.com/${google_storage_bucket.logging_bucket.name}"
filter = "severity >= ERROR OR (resource.type = \"k8s_container\" AND labels.app = \"payments\")"
}
Best practice: treat logging volume as a first-class quota. Add logging-volume metrics to capacity planning and runbooks.
2. Cloud Run GA features: multi-region, .env support, GPU support
September–October 2025 saw several Cloud Run announcements become GA. These features offer better resilience and new use-cases for serverless workloads.
What reached GA (timeline)
- Sept 24, 2025: multi-region service deployment via single CLI/YAML/TF file (GA).
- Sept 10, 2025: .env file support — ability to set multiple environment variables from a .env file (GA).
- Oct 21, 2025: GPU support in Cloud Run jobs/services (GA).
Impact and opportunities
These GA features combined broaden Cloud Run's suitability for production workloads that need low-latency multi-region presence, structured environment variable management, and GPU-accelerated processing.
- Multi-region: improved latency & resiliency for global audiences, simplified failover patterns.
- .env support: reduces CI/CD complexity — no custom parsing scripts to inject env-var sets at deploy time.
- GPU support: allows AI/ML inference and data processing on serverless containers, but cost and runtime constraints apply.
Action checklist (Cloud Run)
- Audit Cloud Run services and identify candidates for multi-region deployment (latency-sensitive, global traffic).
- Update Terraform/Cloud Build templates to include multi-region deployment blocks and switch to
.envwhere appropriate. - For AI/ML workloads: benchmark CPU vs GPU cost & latency on sample workloads and update runbooks to include GPU quotas and instance limits.
- Test CI/CD pipelines: ensure secret management remains secure when using
.envfiles (use secret stores, not plaintext in repos).
Sample gcloud YAML: multi-region Cloud Run service (illustrative)
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: my-service
spec:
template:
spec:
containers:
- image: gcr.io/my-project/my-image:latest
traffic:
- percent: 100
latestRevision: true
# Multi-region CLI example (pseudo):
# gcloud run deploy my-service --image gcr.io/my-project/my-image --region us-central1,asia-south1 --platform managed
Reminder: multi-region deployments may increase egress and inter-region replication costs. Validate traffic routing & DNS behaviour.
3. Monitoring query language changes — MQL deprecation & PromQL-style adoption
In Sept 2025 GCP announced deprecation signals for certain Monitoring Query Language (MQL) usage in favour of PromQL-style APIs or Prometheus-compatible approaches for a subset of metrics and monitoring flows. This has implications for dashboards, alerting and autoscalers that embed MQL directly.
Impact
- Dashboards using MQL will continue to function short-term, but updates and new features will prioritise PromQL compatibility.
- Autoscalers and scalers that rely on MQL may need migration for long-term reliability and support.
- Third-party integration layers (KEDA, custom scalers) will require adapter updates to use PromQL-style endpoints.
Action checklist (monitoring teams)
- Inventory all MQL usage across dashboards, alerting policies, and autoscalers (KEDA, HPA integrations).
- Prioritise migration candidates (production-critical alerts, autoscalers) for early migration to PromQL queries.
- Create conversion templates: common MQL patterns and their PromQL equivalents (where possible).
- Test in staging and validate that alert fidelity, latency and query costs meet SLOs.
Migration example (pattern)
Common MQL query patterns (e.g., rate-based CPU usage across pods) often have direct PromQL equivalents such as rate(container_cpu_usage_seconds_total[1m]). Work through each query and validate semantics.
4. AI/ML & agentic capabilities — Vertex AI, multi-agent systems and integrations
During Sept–Oct 2025 Google Cloud announced enhancements to agentic workflows, multi-agent orchestration and deeper Vertex AI integrations — signalling a push towards more production-ready, integrated generative AI workflows.
What this means for enterprises
- New architecture patterns: use of multi-agent pipelines for data acquisition, model selection, orchestration and monitoring.
- Governance & compliance: new model components require updated policies on data usage, PII handling and model explainability.
- Cost & capacity planning: agentic workflows often increase both inference and orchestration costs due to multiple chained models.
Action checklist (AI teams)
- Map existing Vertex AI workloads and identify candidates for agentic enhancements.
- Run pilot projects to measure latency, accuracy and cost of multi-agent vs single-model architectures.
- Update ML governance: model cards, data lineage, drift monitoring and human-in-the-loop checkpoints.
5. Regional & service updates (identity, service accounts, expansions)
Beyond product-specific changes, there were several smaller but important updates related to regions and identity behaviours. Corporates with APAC/India footprints should pay attention to new local regions and any changes to Service Account authentication patterns.
Action checklist
- Refresh resource inventory and map by region.
- Update DR playbooks where new regions change RTO/RPO calculations.
- Validate service-account authentication for automation (cron jobs, pipelines) against new behaviours in affected services.
How to adapt in a corporate environment — an actionable playbook
The following steps synthesise the guidance above into a repeatable corporate playbook for platform teams.
1. Inventory & tagging
Create a centralized inventory of all GCP resources, owners, and regions. Use enforced tags and org policies to ensure visibility. Prioritize:
- Logging & monitoring pipelines
- Cloud Run services
- AI/ML projects using Vertex AI
2. Communication & change calendar
Establish an internal GCP change calendar — map vendor announcements to impact windows. Notify stakeholders (DevOps, security, legal, finance) with at least one migration owner per item.
3. Automation & IaC readiness
Ensure Terraform/Cloud Build templates are modular and patchable. Examples:
# Use variables and modules to control multi-region and GPU
module "cloudrun_service" {
source = "./modules/cloudrun"
name = "payments-service"
image = var.image
regions = var.regions # e.g. ["us-central1","asia-south1"]
gpu_config = var.gpu_config
}
4. Migration roadmap
Phased approach:
- Discovery & Inventory (2–4 weeks)
- Proof-of-Concept for high-risk items (4–8 weeks)
- Staged migration (per environment) with monitoring (2–6 weeks per service)
- Retire old patterns & update runbooks
5. Training & enablement
Deliver hands-on workshops on:
- PromQL basics and MQL → PromQL conversion patterns
- Cloud Run multi-region design & cost modeling
- Vertex AI agent design and governance
Cost & performance considerations
Any change — quotas, multi-region or GPU support — impacts your cost profile. Suggested actions:
- Run a cost simulation for moving a service from single-region to multi-region (include egress & replication).
- Benchmark CPU vs GPU for serverless workloads; GPUs are faster but can be more expensive if utilization is low.
- Audit logging retention and sampling to control ingestion cost following the volume-based quota change.
Detailed technical checklists & templates
Logging teams — checklist
- Export current regional ingestion metrics (30/90-day window).
- Identify top 10 sources by volume (apps, agents, platform logs).
- Implement filtering and structured logging to reduce payload size.
- Use sinks and storage tiering for long-term retention (export to Cloud Storage / BigQuery).
DevOps & Cloud Run — checklist
- Update IaC modules for multi-region deployment.
- Adopt
.envfile support in CI/CD where secure (secrets remain in Secret Manager). - Validate GPU quotas and set pod-level constraints to avoid runaway cost.
Monitoring & SRE — checklist
- Inventory MQL usage and tag migration priority.
- Create PromQL equivalents for critical MQL queries.
- Test autoscalers/KEDA with PromQL endpoints and adjust thresholds.
Sample internal communication (email / slack post)
Use this template to notify stakeholders about the Cloud Logging quota model change:
Subject: Action required — GCP Cloud Logging quota model change (Sept–Oct 2025)
Hi all,
GCP changed Cloud Logging quotas from "write requests per minute" to volume-based regional quotas. Impact: ingestion may slow or be rejected if a region's volume quota is reached. Please:
1) Review logging volume for your projects by region.
2) Implement filtering/compression where possible.
3) Report back by [DATE] with remediation status.
Owner: platform-logging-team@example.com
Thanks,
Platform Engineering
FAQ & common scenarios
Q: Will my old MQL dashboards stop working immediately?
A: No — deprecation timelines usually include a long sunset period. However, new features and updates may favour PromQL, so plan migration early.
Q: Should we move everything to multi-region Cloud Run?
A: Not necessarily. Choose services that benefit from reduced latency or require higher availability. Balance against added cost and complexity.
Q: How urgent is migrating logging pipelines?
A: Medium-high — if your org runs heavy logging ingestion in a single region, monitor quotas immediately and apply filtering to avoid operational impacts.
Conclusion — what to do this week
- Enable regional logging volume alerts & export historical usage.
- Identify top Cloud Run services and test multi-region deployment in staging.
- Inventory MQL usage and begin converting critical queries to PromQL equivalents.
- Set pilots for agentic Vertex AI use-cases and update governance documents.
Leave a Reply