Managed GPU model deployment control plane

For

Engineering teams deploying and running custom AI models on cloud GPUs

Solves

Model deployment is split across several rented platforms, so teams juggle separate dashboards, APIs, GPU quotas and compliance evidence.

Delivers

Reviewed deployment plan and running endpoint

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce the number of rented platforms and manual handoffs needed to deploy and run AI models.

1. Deploy AI applications without managing underlying infrastructure.
2. Accelerate inference and training workloads on GPUs.
3. Bring and deploy custom AI models.
4. Integrate AI capabilities into other tools via APIs.
5. Monitor model performance and health in real time.
6. Track and manage deployed model versions.
7. Share and collaborate on models across teams.
8. Automatically provision databases for AI applications.
9. Deploy applications with a single command without configuration files.
10. Minimize deployment wait times with fast cold starts.
11. Maintain high availability against an agreed uptime target.
12. Support security standards such as HIPAA and SOC 2 Type I.
13. Serve inference through the public API without enforced rate limits.
14. Accept OpenAI-compatible API keys and base URLs.
15. Optimize inference speed with custom GPU kernels.
16. Process text, image, video and audio in a single workflow.
17. Manage bare metal, containers, firewalls and IPs from one console.
18. Provision clusters of 64 to 128+ GPUs.
19. Access bare metal nodes directly via SSH.
20. Use high-speed NVMe storage on each node for I/O operations.
21. Source GPU capacity across multiple providers to ensure availability.
22. Start with free credits to explore the platform.
23. Scale resources according to usage without long-term commitments.
24. Support advanced model architectures such as FlashAttention 2 and Monarch Mixer.
25. Fine-tune pre-trained models and build custom models.
26. Support open-source model initiatives.
27. Offer managed and white-labelled inference platforms for enterprises.
28. Enhance data privacy with confidential computing where available.
29. Compare the reviewed result with the recorded baseline and value assumptions.
30. Capture corrections and named-owner approval before consequential use.
31. Export a versioned reviewed deployment plan and running endpoint with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Model artifacts, GPU requirements, API specifications, compliance constraints and monitoring rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect model artifacts

4. 3

   GPU requirements

5. 4

   API specifications

6. 5

   Compliance constraints and monitoring rules

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed deployment plan and running endpoint

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, model versions, endpoint revisions, client comments, approval states, usage allowances, GPU quotas, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: deploy AI applications without managing underlying infrastructure; accelerate inference and training workloads on GPUs. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering teams deploying and running custom AI models on cloud GPUs use it to solve "model deployment is split across several rented platforms, so teams juggle separate dashboards, APIs, GPU quotas and compliance evidence"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Successful deployments per engineer hour and endpoint uptime against the agreed target.
4. Measure, then decide. Track successful deployments per engineer hour and endpoint uptime against the agreed target; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed deployment plan and running endpoint. Retain the explicit scope boundary: One approved cloud region and one supported GPU family; final security, compliance and production release decisions remain with the engineering team.

What the build depends on. Model upload and preview, asynchronous deployment jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved cloud region and one supported GPU family; final security, compliance and production release decisions remain with the engineering team.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined deployment package. Offer a monthly production allowance after repeat demand. Quote complex multi-region, confidential-computing or specialist GPU work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed deployment plan and running endpoint. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce the number of rented platforms and manual handoffs needed to deploy and run AI models. Demonstrate a concrete reviewed deployment plan and running endpoint using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams deploying and running custom AI models on cloud GPUs professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed deployment plan and running endpoint from a small authorized input set, with a transparent calculation of successful deployments per engineer hour and endpoint uptime against the agreed target and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams deploying and running custom AI models on cloud GPUs and inspect a recent example of model deployment split across several rented platforms.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure successful deployments per engineer hour and endpoint uptime against the agreed target, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Successful deployments per engineer hour and endpoint uptime against the agreed target. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Successful deployments per engineer hour and endpoint uptime against the agreed target; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed deployment plan and running endpoint. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved deployment patterns, GPU configurations and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering teams deploying and running custom AI models on cloud GPUs. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Cerebrium, Mystic BYOC, OmegaCloud.ai, Mystic Turbo Registry, nCompass Tech, Together AI, Exla FLOPs and Inference Engine by GMI Cloud. Compare this product with the buyer's present method on successful deployments per engineer hour and endpoint uptime against the agreed target. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

GPU hours, inference attempts, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed deployment plan and running endpoint. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.