Why I Built My Own Local AI Readiness Test
I've been self-hosting AI models on Proxmox for about two years now. Not as an experiment, but as actual infrastructure replacing tools I used to pay for. When people ask me if local AI can replace SaaS, I don't have a clean answer anymore because the question itself is wrong.
The real question is: which specific tasks can I handle locally, and which ones still need external services? That's what pushed me to build what I call a "workforce readiness audit" — a structured way to test whether my self-hosted setup can actually do the work.
What I Actually Run
My current stack runs on a Proxmox host with 128GB RAM and an RTX 4090. I use Ollama for model management and have about 15 different models pulled locally, ranging from 7B to 70B parameters. Most of my daily work happens through containers running:
- Open WebUI (for interactive testing)
- n8n workflows (for automated tasks)
- A custom Python service I wrote to handle batch processing
The models I test most often are Llama 3.1 variants, Mistral, Qwen, and occasionally some specialized coding models. I don't run everything at once — I load what I need based on the task.
The Testing Framework I Use
I didn't invent some formal methodology. I just started tracking which SaaS tools I was still paying for and why. Then I built tests around those specific use cases.
Document Processing
I used to pay for a service that extracted structured data from PDFs and invoices. My test was simple: feed 50 real invoices through a local model and compare accuracy against the paid tool. I used Llama 3.1 70B with a structured output format.
What worked: extraction accuracy was about 92% compared to the SaaS tool's 96%. Good enough for my volume.
What didn't: processing time was 3-4x slower locally. I had to batch overnight instead of real-time.
Code Review and Documentation
I tested whether local models could replace GitHub Copilot for code review comments and documentation generation. I ran this on actual pull requests from my projects.
What worked: Qwen 2.5 Coder 32B gave surprisingly good inline comments and could explain complex functions clearly.
What didn't: it missed context from other files more often than Copilot. I had to manually feed more context, which broke my workflow.
Email Drafting and Triage
I tested automated email responses using n8n workflows connected to local models. The goal was to replace a virtual assistant service I was using.
What worked: basic triage and categorization worked perfectly. I could route emails automatically based on content.
What didn't: draft quality was inconsistent. Some responses were great, others were awkwardly formal or missed the tone completely. I ended up keeping this as a semi-automated flow where I review before sending.
Meeting Transcription and Summarization
I tested Whisper (local) against Otter.ai and similar services. I recorded 20 actual meetings and ran them through both.
What worked: transcription accuracy was nearly identical. Whisper large-v3 handled my accent and technical jargon well.
What didn't: summarization from local models was hit-or-miss. Sometimes brilliant, sometimes it missed the entire point of the meeting. I kept Whisper for transcription but still use a paid service for summaries on important calls.
The Reality of Resource Constraints
Running these tests exposed hard limits I wasn't expecting.
GPU memory is the real bottleneck. I can run one 70B model comfortably, but if I try to run multiple services simultaneously, I start swapping to system RAM and everything slows down dramatically. This means I can't just "replace" multiple SaaS tools at once — I have to choose what runs when.
Power consumption matters more than I thought. My electricity bill went up about $40/month when I started running models regularly. That's not huge, but it's real cost that offsets some of the SaaS savings.
Model switching takes time. Loading a new model into GPU memory takes 30-60 seconds. If I'm switching contexts frequently, that adds up to noticeable friction compared to hitting an API instantly.
What I Actually Replaced
After six months of testing, here's what I genuinely moved off SaaS:
- Document extraction (saved $89/month)
- Meeting transcription (saved $30/month)
- Basic code documentation (saved $10/month from a Copilot seat I wasn't fully using)
- Email triage and categorization (saved $150/month from VA service)
Total savings: about $280/month, or $3,360/year.
But I kept paying for:
- Advanced code completion (GitHub Copilot, $10/month)
- Meeting summaries for critical calls (Otter.ai, $17/month)
- Customer support chat (still using a managed service, $200/month)
The Mistakes I Made
I wasted weeks trying to replace tools that didn't actually cost much. I spent hours optimizing a workflow to replace a $5/month service. The time cost wasn't worth it.
I initially tried to run everything 24/7. My power bill spiked and I realized most tasks could run on-demand or in batches. Now I only keep lightweight models loaded constantly.
I underestimated the maintenance burden. Models need updating, workflows break when dependencies change, and I have to monitor for quality drift. This takes 2-3 hours per month that I didn't account for initially.
How I Actually Test New Capabilities
When a new model drops, I don't just benchmark it. I run it through my real workload for a week:
- Replace one existing workflow completely
- Track quality issues in a simple text file
- Measure actual time savings (not theoretical)
- Calculate real costs (power + maintenance time)
- Decide: keep it, tweak it, or revert
I don't care about benchmark scores. I care if it handles my actual invoices, my actual code, my actual meetings.
What This Taught Me About "AI Readiness"
The readiness audit isn't really about the models. It's about honestly assessing:
Your actual workload: Do you have consistent, repeatable tasks worth automating? Or are you trying to replace one-off creative work that doesn't benefit from automation?
Your infrastructure: Can you actually run the models you need? Do you have the GPU memory, the power budget, the cooling?
Your tolerance for imperfection: Local models make different mistakes than SaaS tools. Are those mistakes acceptable in your context?
Your maintenance capacity: Are you willing to spend a few hours per month keeping things running?
For me, the answer to all of these was "yes, but with limits." I can replace some tools profitably. Others aren't worth the effort yet.
Key Takeaways From Two Years of Testing
Local AI works best for high-volume, consistent tasks where slight quality variations don't matter much. Document processing, transcription, basic triage — these are wins.
It works poorly for tasks requiring perfect context awareness or where mistakes are costly. Code completion, critical communication, customer-facing content — I still use SaaS for these.
The economics only work if you already have the hardware or can amortize it across multiple uses. Buying a $2000 GPU just to replace $50/month in SaaS makes no sense.
Model quality is improving faster than I can keep up. Something that didn't work six months ago might work now. I re-test quarterly.
The real value isn't cost savings — it's control. I can process sensitive data locally, customize exactly how things work, and not worry about API rate limits or service changes.
What I'm Testing Next
I'm currently running experiments with:
- Structured data extraction from complex technical documents
- Automated code refactoring suggestions (not just completion)
- Multi-step research workflows using multiple specialized models
- Voice-to-action commands for my home automation setup
Some of these will work. Some won't. I'll know in a few months based on actual usage, not benchmarks.
The workforce readiness audit isn't a one-time thing. It's an ongoing process of testing what's possible now, not what might be possible someday.
