AI app for it and development · no coding needed
Managed GPU model deployment control plane
Reduce the number of rented platforms and manual handoffs needed to deploy and run AI models.
Made for: Engineering teams deploying and running custom AI models on cloud GPUs
What it does for you
The problem
Model deployment is split across several rented platforms, so teams juggle separate dashboards, APIs, GPU quotas and compliance evidence.
What it gives you
Reviewed deployment plan and running endpoint
What you give it
Model artifactsGPU requirementsAPI specificationscompliance constraintsmonitoring rules
Build your own version of Cerebrium, Mystic BYOC and more
One app with what these 8 AI tools do, yours to keep and change: Cerebrium, Mystic BYOC, OmegaCloud.ai, Mystic Turbo Registry, nCompass Tech, Together AI, Exla FLOPs, Inference Engine by GMI Cloud.
Everything these tools do, in one app
- Serverless deployment Deploy AI applications without managing underlying infrastructure.Found in Cerebrium, OmegaCloud.ai, Together AI
- GPU acceleration Use GPUs to accelerate AI inference and training workloads.Found in Cerebrium, OmegaCloud.ai, nCompass Tech and 3 more
- Custom model support Bring and deploy your own custom AI models.Found in Mystic BYOC, Together AI
- API access Integrate AI capabilities into other tools via APIs.Found in Mystic BYOC, nCompass Tech, Together AI
- Performance monitoring Monitor model performance and health in real time.Found in nCompass Tech, Inference Engine by GMI Cloud
- Model versioning Track and manage different versions of deployed models.Found in Inference Engine by GMI Cloud
- Team collaboration Share and collaborate on models across teams.Found in Mystic BYOC
- Automatic database setup Automatically provision databases for AI applications.Found in OmegaCloud.ai
- One-command deployment Deploy applications with a single command without configuration files.Found in OmegaCloud.ai
- Fast cold starts Minimize wait times during deployment with quick startup.Found in Cerebrium
- High uptime Ensure high availability with 99.999% uptime backed by tier 3 data centers.Found in Cerebrium
- Compliance support Meet security standards like HIPAA and SOC 2 Type I.Found in Cerebrium
- No rate limits Use the public inference API without enforced rate limits.Found in nCompass Tech
- OpenAI API compatibility Integrate by changing API keys and base URLs to match OpenAI standards.Found in nCompass Tech
- Custom GPU kernels Optimize inference speed with custom GPU kernels.Found in nCompass Tech
- Multimodal pipeline Process text, image, video, and audio in a single workflow.Found in Inference Engine by GMI Cloud
- Unified dashboard Manage infrastructure components like bare metal, containers, firewalls, and IPs from one console.Found in Inference Engine by GMI Cloud
- Large GPU clusters Instantly provision clusters of 64 to 128+ GPUs.Found in Exla FLOPs
- SSH access Access bare metal nodes directly via SSH for full control.Found in Exla FLOPs
- Fast local storage Use high-speed NVMe storage on each node for I/O operations.Found in Exla FLOPs
- Dynamic GPU sourcing Source GPU capacity across multiple providers to ensure availability.Found in Exla FLOPs
- Free credits Start with free credits to explore the platform.Found in Cerebrium, OmegaCloud.ai, nCompass Tech
- Pay-as-you-go pricing Scale resources according to usage without long-term commitments.Found in Cerebrium, Mystic BYOC, Exla FLOPs
- Advanced model architectures Leverage innovative architectures like Cocktail SGD, FlashAttention 2, and Monarch Mixer.Found in Together AI
- Fine-tuning and custom model builds Fine-tune pre-trained models and build custom models.Found in Together AI
- Open-source initiatives Support open-source projects like RedPajama.Found in Together AI
- Managed inference platforms Offer managed and white-labelled inference platforms for enterprises.Found in nCompass Tech
- Confidential computing Enhance data privacy and security with confidential computing (planned).Found in OmegaCloud.ai
How it works, step by step
- Deploy AI applications without managing underlying infrastructure
- Accelerate inference and training workloads on GPUs
- Bring and deploy custom AI models
- Integrate AI capabilities into other tools via APIs
- Monitor model performance and health in real time
- Track and manage deployed model versions
- Share and collaborate on models across teams
- Automatically provision databases for AI applications
- Deploy applications with a single command without configuration files
- Minimize deployment wait times with fast cold starts
- Maintain high availability against an agreed uptime target
- Support security standards such as HIPAA and SOC 2 Type I
- Serve inference through the public API without enforced rate limits
- Accept OpenAI-compatible API keys and base URLs
- Optimize inference speed with custom GPU kernels
- Process text, image, video and audio in a single workflow
- Manage bare metal, containers, firewalls and IPs from one console
- Provision clusters of 64 to 128+ GPUs
- Access bare metal nodes directly via SSH
- Use high-speed NVMe storage on each node for I/O operations
- Source GPU capacity across multiple providers to ensure availability
- Start with free credits to explore the platform
- Scale resources according to usage without long-term commitments
- Support advanced model architectures such as FlashAttention 2 and Monarch Mixer
- Fine-tune pre-trained models and build custom models
- Support open-source model initiatives
- Offer managed and white-labelled inference platforms for enterprises
- Enhance data privacy with confidential computing where available
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed deployment plan and running endpoint with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Managed GPU model deployment control plane with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Managed GPU model deployment control plane with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data200 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce the number of rented platforms and manual handoffs needed to deploy and run AI models. For engineering teams deploying and running custom AI models on cloud GPUs, convert model artifacts, GPU requirements, API specifications, compliance constraints and monitoring rules into a reviewed deployment plan and running endpoint. The benefit is a testable hypothesis, measured through successful deployments per engineer hour and endpoint uptime against the agreed target; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect model artifacts, GPU requirements, API specifications, compliance constraints and monitoring rules, then follow this sequence: 1. Deploy AI applications without managing underlying infrastructure. 2. Accelerate inference and training workloads on GPUs. 3. Bring and deploy custom AI models. Resolve uncertain cases with qualified reviewers, approve reviewed deployment plan and running endpoint, and measure successful deployments per engineer hour and endpoint uptime against the agreed target against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved cloud region and one supported GPU family; final security, compliance and production release decisions remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve model integrity, source attribution, license accuracy and usage permissions. Engineering owners approve substantive changes and production scope. One approved cloud region and one supported GPU family; final security, compliance and production release decisions remain with the engineering team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One approved cloud region and one supported GPU family; final security, compliance and production release decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: deploy AI applications without managing underlying infrastructure; accelerate inference and training workloads on GPUs. Support the third module with operator review: bring and deploy custom AI models. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned model registries, authorized code repositories and permitted monitoring sources. Cloud GPU providers, container registries, secret stores and observability destinations. Start with file exchange and validate destination specifications before promising direct production deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Deployment workspace, Endpoint and GPU monitor, Model registry and review. Use a project list for deployments, a central canvas for pipeline and endpoint configuration, and a right-hand panel for GPU quotas, compliance rules and comments. Let users compare model versions and deployment revisions side by side. Display draft, review requested, approved and running states. Provide a client preview link with comments anchored to the relevant endpoint or model version. Make the task-specific outcome reviewed deployment plan and running endpoint visible beside its evidence, review state and value baseline.
