You will get a ComfyUI pipeline on RunPod serverless, cost-tuned with a fallback path
Project details
I put your ComfyUI workflow on RunPod serverless so it scales with demand instead of pinning a GPU you pay for around the clock.
What that means: a custom worker image with your nodes baked in, models on a network volume so cold starts do not re-download weights, an autoscaling pool that picks the cheapest card that actually fits the job, and an async API where the request returns immediately and your client polls for the result.
The part most deployments miss is cost. On a production pipeline of my own, moving off a GPU-starved US region on an H100 onto a co-located EU 4090 took it to roughly one to three cents per image, about four times cheaper, with no change to output. Same method here: right-size the card, co-locate the region, tune the keep-warm tail against your real traffic instead of leaving it at the default.
Higher tiers add an automatic fallback endpoint, so a provider outage degrades instead of failing.
You get the working deployment, source code, a setup file, and handover docs written so your team runs it without me. Plus a fixed scenario set you can rerun after any change.
Eighteen years in IT before this, help desk through Director of IT.
What that means: a custom worker image with your nodes baked in, models on a network volume so cold starts do not re-download weights, an autoscaling pool that picks the cheapest card that actually fits the job, and an async API where the request returns immediately and your client polls for the result.
The part most deployments miss is cost. On a production pipeline of my own, moving off a GPU-starved US region on an H100 onto a co-located EU 4090 took it to roughly one to three cents per image, about four times cheaper, with no change to output. Same method here: right-size the card, co-locate the region, tune the keep-warm tail against your real traffic instead of leaving it at the default.
Higher tiers add an automatic fallback endpoint, so a provider outage degrades instead of failing.
You get the working deployment, source code, a setup file, and handover docs written so your team runs it without me. Plus a fixed scenario set you can rerun after any change.
Eighteen years in IT before this, help desk through Director of IT.
AI Algorithms
Convolutional Neural Network, Transformer Model, Variational AutoencoderAI Applications
AI Content Creation, AI Text-to-Image, AI-Generated Video, Image Processing, Image-to-Image TranslationAI Models
Stable DiffusionWhat's included
| Service Tiers |
Starter
$350
|
Standard
$700
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | ||
Model Deployment | |||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | - |
Model Tuning | - | ||
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | |||
Source Code |
Frequently asked questions
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AS
Asif S.
Aug 11, 2026
Upwork Talent Accelerator: AI Chatbot Developer
Really impressed with the final results and the attention to detail throughout the project.
About Christian
AI Agent & Automation Engineer | Python, LLM Integration | 18 Yrs IT
Pittston, United States - 5:01 pm local time
Most AI projects stall in the same place. The demo works, then someone asks what happens when it gets something wrong, and nobody has an answer. I build the answer in from the start: identity verification before any privileged action, policy gates that decide what runs automatically and what stops for a human, and an audit log for everything.
What I do:
- AI agents and workflow automation. Multi-agent systems that resolve helpdesk tickets end to end across Microsoft 365 tenants: password resets with identity verification, distribution list and Teams group management, approval routing. Built in Python against the Microsoft Graph API.
- Microsoft 365 and Azure automation. Graph API integrations for provisioning, reporting, and the admin work that should not be manual. PowerShell where PowerShell fits.
- Custom tools and integrations. FastAPI services, API integrations, and scripts that connect systems never meant to talk to each other. I have shipped a live SaaS product and published a Python package to PyPI.
- Retrieval and data extraction. RAG on pgvector with recency decay layered onto similarity, so relevance does not quietly mean stale. Playwright scrapers and extraction pipelines that clean, score and rank what they pull, with tests over the scoring logic.
Background: 18 years in IT, help desk through Director of Information Technology. Twelve of those inside a HIPAA-regulated healthcare provider, three of them owning the budget and the vendor decisions. Four years at an MSP running Microsoft 365 and Azure across many client tenants. I know what production means because I ran it before I built for it.
I have been building with LLMs since ChatGPT launched, which is about as long as anyone has.
How I work: I tell you what I think will actually work, including when the answer is do not build this. I write documentation. I hand things off so you are not dependent on me.
Tell me what is eating your team's time and I will tell you whether AI is the right fix.
Steps for completing your project
After purchasing the project, send requirements so Christian can start the project.
Delivery time starts when Christian receives requirements from you.
Christian works on your project following the steps below.
Revisions may occur after the delivery date.
Review and plan
I review your workflow and models, confirm the target region and card class, and tell you upfront if anything in the graph will not run on serverless.
Build and deploy
I build the custom worker image with your nodes, load the models onto a network volume, and stand up the serverless endpoint.