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What does your model evaluation pipeline actually look like in production?
Offline evals, golden sets, LLM-as-judge, human review: what are you running before a model or prompt change ships, and what has caught real regressions?
Community
Ask, compare and learn from people who run this stuff.
This is where people who run enterprise technology compare notes before a decision: what they’re evaluating, what they’d pick again and what went wrong. Most members are practitioners. Anyone who works for a vendor carries their company’s name on every post.
The bar is specifics over opinions: what you ran, at what scale and what you learned. Moderators check each new member’s first posts.
- Spaces
- 23
- Threads
- 45
Question of the week
How are you controlling observability costs without losing visibility?
Sampling, tiered retention, OpenTelemetry pipelines, switching vendors? What cut the bill the most?
Threads
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Datadog vs Grafana stack vs OpenTelemetry + open source
If you have run more than one, compare them on cost at your scale, time to value and on-call experience.
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Have AI coding assistants changed how you review code?
More PRs, bigger PRs, new review checklists? Share what changed in your process and quality metrics.
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How are you handling semantic layers and metric definitions across BI tools?
dbt semantic layer, a headless BI layer, or definitions living in each dashboard tool? What has reduced the “two numbers for revenue” problem for you?
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How many Kubernetes clusters do you run, and how do you keep upgrades sane?
Fleet size, managed vs self-managed, and the tooling that keeps version upgrades from becoming a quarterly fire drill.
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How are you controlling observability costs without losing visibility?
Sampling, tiered retention, OpenTelemetry pipelines, switching vendors? What cut the bill the most?
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Which AI coding assistant is your team standardising on?
One answer per team, please.
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How are you measuring platform engineering success?
DORA metrics, developer surveys, time-to-first-deploy, cost per service? What convinced leadership the platform team is worth it?
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Where is your unstructured data growing fastest?
Pick the biggest driver in your environment.
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How are you approaching AI governance and model risk management?
Inventories, risk tiers, approval workflows, monitoring. What framework did you adopt and what does day-to-day governance look like?
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What is your approach to zero-downtime schema migrations at scale?
Expand-and-contract, online schema change tools, feature flags? Share what has kept large-table migrations boring.
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What is the one cloud architecture decision you would undo if you could?
Account structure, networking, region choice, managed-service lock-in: the lessons that cost the most are the most useful to others.
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Composable vs suite: what is your application strategy?
Best-of-breed SaaS stitched together, or consolidating on fewer platforms? What is driving your direction?
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Which workflow did you automate with an AI agent first, and did it stick?
We are hearing a lot about agent pilots and far less about agents still running six months later. What was the first use case, what guardrails did you need, and is it still in production?
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Post-VMware licensing changes: what did you actually do?
Renegotiated, moved to another hypervisor, containerised, or went to public cloud? Share your path and what surprised you.
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SASE rollouts: single vendor or best of breed?
Share where you landed and what you learned about migration sequencing, user experience and cost.
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How are you allocating AI and GPU costs back to teams?
Shared GPU pools and model APIs make showback harder. What allocation model is working?
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RAG vs fine-tuning vs long context: what is working for your use case?
Share the use case, the approach you chose and the results.
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Operational resilience rules: what changed in your architecture?
For regulated teams (CPS 230, DORA and similar), which technical changes did compliance actually require?
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Where do most of your inference workloads run today?
Pulse check for FY27 planning conversations.
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Which skills are you hiring for in FY27 that you were not a year ago?
AI engineering, platform, FinOps, security automation? Share what is on your hiring plan.
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How are you getting GPU capacity for AI in 2026?
Reserved cloud capacity, neoclouds, on-prem clusters, shared internal pools? What are lead times and utilisation like?
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How are you preparing the network for AI workloads?
East-west bandwidth, GPU cluster fabrics, data movement between sites: what has changed in your network plans?
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OpenShift vs managed Kubernetes (EKS/AKS/GKE) for regulated workloads
For teams in financial services, government or healthcare: which did you choose and what tipped it: compliance evidence, support, cost or skills?
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How far along is your zero-trust journey, honestly?
Identity-first access, device posture, microsegmentation: which parts are done and which keep slipping?
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Build vs buy for internal AI agents: framework or platform?
LangGraph/CrewAI-style frameworks versus packaged agent platforms from the big vendors. If you have tried both, where did each win on time-to-value, control and cost?
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Where is edge AI delivering real value today?
Computer vision on the line, retail analytics, predictive maintenance? Share deployed examples, not pilots.
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What is your approach to managing thousands of edge devices?
Fleet management, updates, security patching and observability at the edge: what tools and practices have held up?
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Is the lakehouse finally the default, or still a migration project?
Share where you are: fully on a lakehouse, hybrid with a warehouse, or not convinced yet, and what drove the decision.
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What makes a vendor proof of concept worth your time?
Success criteria, timeboxes, data access, who is in the room. Share what separates useful PoCs from sales theatre.
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Which FinOps practice saved you the most money in the last year?
Commitment management, rightsizing, tagging and showback, architecture changes? Numbers welcome.
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Replacing VPNs with ZTNA: what broke?
Legacy apps, contractor access, performance, user experience: share the gotchas so others can plan for them.
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How are you securing employee use of generative AI tools?
Blocking, allow-listing, DLP, enterprise licences, policy and training: what combination is working in practice?
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Primary cloud strategy for FY27?
Which best describes your organisation’s direction for the next 12 months?
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How is AI changing the shape of your technology team?
Fewer junior roles, new roles, reskilling programmes? What are you seeing in your organisation?
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What is in your internal developer platform, and what did developers actually adopt?
Golden paths, templates, portals, self-service environments. Which parts get used every day and which became shelfware?
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Is a single control plane across clouds worth the complexity?
Some teams swear by a unified management layer; others keep each cloud native. What is working for you?
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What does your generative AI cost per useful answer look like?
Token spend, retrieval cost, caching, model routing. How are you measuring and reducing cost without hurting quality?
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Liquid cooling: are you retrofitting data centres or waiting?
For teams planning dense GPU racks, what is the realistic path for your facilities?
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Where do you research before shortlisting a vendor?
Peers, analysts, communities, review sites, AI assistants? What do you trust most, and what has changed in the last year?
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