Private models in production, without building an ML platform team.
Managed Private AI covers the day-to-day work of running private models: serving health, capacity, model evaluation and upgrades, retrieval quality, and gateway operations. Your team decides which models and policies go to production.
What Managed Private AI includes.
Serving operations
Keep models running, tuned, and sized; investigate latency and errors.
Model lifecycle
Track new open-weight releases, evaluate candidates on your test sets, and propose upgrades.
Retrieval quality
Monitor ingestion, re-index after embedding changes, and maintain evaluation sets.
Gateway operations
Quotas, routing pools, and token management, adjusted as usage grows.
Capacity planning
Usage trends turned into recommendations before users feel the limits.
Reporting
Regular summaries of usage, incidents, changes, and model performance.
We propose. You approve.
Model changes alter outputs, so they go through the model registry's approval workflow. Data.co prepares candidates, runs evaluations against your test sets, and documents the results. A reviewer on your side approves promotion to production aliases.
Policies work the same way. We can draft and implement policy changes on request, but the policies that decide who may see what stay owned and approved by your team.
What a typical upgrade looks like.
- 1
Identify
A new open-weight release or quantization looks promising for one of your aliases.
- 2
Evaluate
We register it, serve it in a non-production alias, and run your evaluation sets.
- 3
Recommend
You receive results, resource impact, and license notes with a recommendation.
- 4
Promote
After your approval, we promote it in a change window, with rollback ready.
Who owns each decision in the AI stack.
The split below is the default we propose. The contract version is the one that counts.
| Component | Data.co | Your team |
|---|---|---|
| Inference endpoints (vLLM, Ollama) | Deploys, sizes, tunes, restarts | Approves hardware and capacity spend |
| Model registry and aliases | Registers candidates, runs evaluations | Approves production promotion |
| Evaluation sets | Maintains the harness, proposes new cases | Supplies questions and expected sources |
| AI gateway endpoints and quotas | Configures and adjusts | Decides which teams get access |
| External model endpoints | Registers them only on request | Decides whether any exist at all |
| Policies and signed bundles | Drafts and tests changes on request | Reviews, signs, and activates |
When answers slow down on a Monday morning.
A typical incident: time to first token climbs as a large team starts work, and the gateway begins returning upstream errors for one model alias. The on-duty engineer checks serving queue depth and GPU memory, confirms the alias is saturated rather than failing, and applies the runbook action pre-approved in the contract, such as shifting a routing pool to a second replica or lowering a batch limit that is causing memory pressure.
If the fix needs more than the runbook allows, for example moving a team to a smaller model or adding a node, the engineer escalates to your named contact with the evidence and options. Afterward, the incident record notes cause, actions, and a capacity recommendation, which feeds the next planning report so the same Monday does not repeat.
What stays true under management.
- Inference and data stay in your environment
- Our access is granted, scoped, and revocable by you
- Engineer sessions are logged where you can review them
- Production model changes need your approval
- Policies remain owned by your team
- External model APIs stay off unless your policy enables them
Questions buyers ask
Will Data.co engineers read our prompts or documents?
Operational work generally does not require it. Access to content is governed by the scope you grant and by your policies. If troubleshooting needs it, that is requested and logged per the contract.
How often are models updated?
As often as useful improvements appear and you approve them. We propose; your reviewers decide, and nothing changes in production without approval.
Does this include the hardware?
It can. Managed Private AI runs on your hardware, a private cloud environment, or dedicated servers Data.co hosts for you.
How do we measure whether it is working?
Regular reports cover availability, latency, usage, incidents, and retrieval and model evaluation results, against commitments defined in the contract.
Who checks model licenses?
We record each candidate's license terms in the recommendation and flag restrictions on commercial use or redistribution. Your legal or procurement team makes the final call on whether a license is acceptable.
Can we keep our own ML engineers involved?
Yes. Many teams keep fine-tuning or application work in-house and hand us serving, gateway, and lifecycle operations. The responsibility matrix records exactly where the line sits.
See it against your own use case.
A 30-minute technical walkthrough, policy simulation on your scenarios, and a deployment recommendation for on-prem, private cloud, or hybrid.