As organisations begin deploying AI workloads on their own infrastructure, backup and recovery becomes another area where traditional IT principles still apply, but where the way we think about the workload needs to change slightly.
An on-premises AI platform can contain a mixture of familiar infrastructure components and newer AI-specific elements. Depending on the application, this might include source documents, databases, vector indexes, model files, fine-tuned models, prompts, agent configurations, application code, containers, credentials, logs and significant amounts of supporting data.
The obvious response is to treat the AI server like any other server and simply back up everything on it. In some cases that may be entirely reasonable, but it is not always the most efficient or useful approach.
Like traditional backup designs, when the datasets are small, a full backup every night can be a great starting point. But once datasets get larger and cannot be protected within a single backup window, a more structured approach and some further analysis and design is required.
Some parts of an AI environment may contain valuable business data or intellectual property that would be difficult or impossible to recreate. Other components may be very large but relatively disposable because they can be downloaded or rebuilt from known sources. A large base model, for example, may take hundreds of gigabytes of storage but still be less important to protect than a few megabytes of configuration, prompts or training data that represent weeks of work.
The objective therefore should not simply be to back up the infrastructure. It should be to understand what is actually required to recreate the AI service.
Two questions provide a useful starting point:
What would be difficult or impossible to recreate?
and
What can be reliably rebuilt from a known source?
Start With Recovery, Not Backup
Nothing new here. The design of an AI backup strategy should begin with the same questions we would ask for any other business workload.
But a note of caution on visibility. A lot of AI implementations we see can have evolved from shadow IT or have been implemented rapidly.
Equally, what started as a small project providing some interesting features can all of a sudden become a critical part of the company workflow.
“The RPO and RTO may never have been discussed or agreed upon within the business.“
How long can the service be unavailable? How much data can the organisation afford to lose? What needs to be recovered first? What can be rebuilt rather than restored? Where does the authoritative copy of the data live?
Traditional concepts such as Recovery Point Objective, Recovery Time Objective, retention, immutability and offsite protection remain completely relevant.
AI does, however, introduce another consideration: rebuild time and reproducibility.
A model file that can be downloaded again in a few minutes may not justify extensive backup protection. An index that takes 20 hours to regenerate may be technically replaceable, but rebuilding it may not meet the recovery objective. A fine-tuned model may be reproducible in theory, but if recreating it requires access to the original dataset, scripts, configuration and many hours of GPU processing, restoring the finished model may be considerably easier.
The distinction between recoverable and reproducible therefore becomes important.
“Not Everything Has the Same Value”
It can be useful to divide an AI environment into four broad categories.
| Category | Examples | Typical approach |
| Irreplaceable or business-critical | Source data, proprietary training data, customised models | Protect carefully |
| Derived but expensive to recreate | Vector indexes, embeddings, trained adapters | Backup or rebuild depending on recovery objectives |
| Configuration and intellectual property | Prompts, agent definitions, application code, infrastructure configuration | Version control plus backup |
| Replaceable components | Base models, standard containers, operating systems, caches | Usually rebuild or re-download |
This classification will not be identical for every environment, but it provides a more useful starting point than simply measuring how much data exists on the AI server or servers.
Protect the Source Data
For most AI systems, the underlying business data remains the most important thing to protect.
This may include documents used for Retrieval Augmented Generation, databases, customer records, internal knowledge repositories, images, video, training datasets or other information used by the AI application.
In many cases the authoritative copy of this information already exists somewhere else. Documents may live in SharePoint or a file server. Business information may reside in SQL or an application database. Customer records may already be protected by the organisation’s existing backup platform.
The fact that an AI platform consumes that information does not necessarily mean another complete backup copy is required. The important thing is to know where the authoritative copy exists and whether it can be recovered reliably.
This becomes especially important where data has been transformed, cleaned or enriched before being presented to the AI system. If significant work has been performed to prepare a dataset, the transformed version may itself have value and may need protection alongside the original source.
RAG, Embeddings and Vector Databases
Retrieval Augmented Generation introduces another layer of data to consider.
A typical RAG workflow might look something like:
Original documents → processing and chunking → embeddings → vector database → AI application
The original documents are normally the critical asset because everything downstream can, at least in theory, be regenerated. That does not necessarily mean the vector database should be ignored.
For a relatively small knowledge base, rebuilding the embeddings and vector index may take only a short time and may be preferable to backing them up.
At a larger scale, however, regeneration may involve significant processing time, GPU or CPU resources, API consumption and operational effort. Rebuilding several million embeddings may be perfectly possible but still unacceptable if the business expects the service to be back online within an hour.
The practical question therefore becomes:
“Is it quicker and more reliable to restore or to rebuild it from the source data?”
That answer should drive the protection strategy.
Base Models May Not Need Traditional Backup
One of the largest components in many AI environments will be the models themselves. These can occupy tens or hundreds of gigabytes, and environments that maintain several models may consume substantial amounts of storage, but large size does not necessarily mean high backup priority.
If a model is a standard model that can be reliably obtained again from a trusted repository, there may be little benefit in repeatedly sending the same model files through the backup system.
In that case, what becomes important is the ability to reproduce the environment.
At a minimum, organisations should record the exact model being used, its version, quantisation, configuration, source and, where appropriate, a checksum or other method of confirming that the correct artefact has been retrieved. We discuss documentation of the system further below.
There may still be reasons to maintain local copies of base models, particularly where internet access is restricted, models have been removed from public repositories, or a specific validated release must be retained. That is a policy decision rather than an assumption that every model automatically requires conventional backup.
Fine-Tuned Models Are a Different Matter
Once an organisation begins modifying a model, the value of that model changes.
Fine-tuned models, LoRA adapters, proprietary weights, distilled models and other customised artefacts may represent substantial time, compute and intellectual property.
These should generally be treated very differently from standard downloadable models, and it may also be important to protect more than just the final model.
A properly recoverable training process may require the original training data, the base model version, training scripts, configuration, hyperparameters and evaluation results.
This is similar to protecting source code rather than simply backing up the finished application executable. Having the finished model is useful, but being able to understand how it was created and reproduce it is significantly more valuable.
Prompts, Agents and Workflows
Another area that can easily be overlooked is the application logic around the model.
In many modern AI systems, particularly agentic applications, much of the real intellectual property may sit outside the model itself. This can include system prompts, agent instructions, workflow definitions, routing rules, tool definitions, application configuration, permissions, guardrails and integration logic.
These files may be extremely small compared with the model or dataset, but they may represent many hours of development and refinement.
“The smallest files in the AI environment may therefore contain some of its most valuable intellectual property.”
Wherever possible, these components should be maintained in appropriate version control and configuration management systems rather than simply existing as files on an individual AI server.
The repositories themselves then become part of the broader backup and recovery strategy.
Application and Infrastructure Configuration
The same principle applies to the infrastructure supporting the AI environment.
Dockerfiles, container definitions, Kubernetes manifests, Helm charts, Terraform, Ansible, application source code and environment configuration all contribute to the ability to reproduce the service.
Where an environment is well documented and automated, rebuilding an AI server may be simpler and more reliable than restoring an old image of it. Those familiar with containers already have experience in this area.
But be careful. We have seen in the wild environments where the ability to rebuild the platform is effectively the responsibility of a single developer rather than part of an enterprise recovery process that centrally holds the configuration and does not go on leave.
This is not unique to AI. It reflects a broader change in infrastructure design and infrastructure as code, where configuration and automation increasingly matter as much as the operating system installed on an individual machine.
The more reproducible the infrastructure becomes, the less dependent recovery is on protecting complete server images indefinitely.
Secrets, Credentials and Encryption Keys
AI platforms also tend to integrate with multiple services.
This may include internal databases, cloud APIs, Microsoft 365, identity services, external applications and other business systems. The credentials supporting these integrations need their own recovery strategy.
API keys, service credentials, certificates and encryption keys should not simply be exported into an unprotected folder and included in a conventional backup job.
Appropriate secrets management, encryption and access control should be used, with documented procedures for recovering or recreating the credentials when required.
Encryption keys deserve particular attention. A successful restore of encrypted information is of little value if the organisation no longer has the keys required to access it.
Logs and Conversation History
Conversation history, logs and telemetry need to be considered separately because the question is often less about whether they can be backed up and more about whether they should be retained.
There may be legitimate reasons to keep them, including troubleshooting, auditing, security investigations, model evaluation or regulatory requirements.
“Regulation is often one of the reasons AI is brought on-premises in the first place, so careful consideration of logs is required.”
There may also be very good reasons not to retain them indefinitely. AI conversations can contain sensitive corporate information, personal data and information that users did not expect to become part of a permanent record.
The organisation therefore needs to make a deliberate decision about retention rather than assuming that more data is always better. Backup retention should align with the organisation’s broader privacy, compliance and data lifecycle requirements.
What Can Usually Be Rebuilt?
While every environment is different, there are several components that will often be candidates for rebuilding rather than restoring.
These may include operating systems, standard containers, downloadable base models, common software packages, temporary caches and, in some environments, embeddings and vector indexes.
“We can rebuild it” is only a valid recovery strategy if the organisation has documented how to do it and has tested that process.
The Existing Backup Platform May Already Do Most of the Work
Most organisations considering private AI will already have an established backup platform, and in many cases there is no reason to replace it simply because AI has been introduced.
Products such as Veeam and Commvault can continue to protect much of the underlying environment in the same way they protect other workloads today. This may include physical servers, virtual machines, file systems, application data, databases and container or cloud-based components, depending on the platform and how the AI application has been deployed.
The important distinction is that these platforms generally protect the infrastructure and data presented to them. They will not necessarily understand the complete logical structure of the AI application.
A backup product may successfully protect a server containing a model, vector database and application code, but that does not automatically mean the entire AI service can be returned to a known working state simply by restoring that server. The application may also depend on external repositories, databases, identity services, secrets, source documents and configuration stored elsewhere.
In practice, this means the existing backup product will often remain an important part of the solution, but it should sit within a broader recovery design.
A Veeam or Commvault backup of the underlying server may be entirely appropriate. Source code may be recovered from Git. A database may require application-consistent protection. Secrets may need to be recovered from a separate vault. A vector database may either be restored or rebuilt depending on the recovery objective.
As part of the design, also ensure individual files from within a backup, including an image-level backup, can be restored quickly and correctly. Sometimes a file of only a few kilobytes or megabytes may be critical to the whole environment.
The better question is therefore not simply “Does our backup product support AI?”
It is:
“Can our existing backup platform protect the components we have identified as important, and do we have a documented way of recovering the complete AI service?”
That tends to lead to a much more useful design.
Recovery Testing Becomes Even More Important
Again like traditional recovery, a successful restore of an AI environment should not simply be measured by whether the files have returned. The recovered application needs to be tested as a complete service.
That may include confirming that the correct model loads, GPU acceleration is operating correctly, the vector database is accessible, RAG searches return sensible results, authentication works, agents can access the correct tools and external integrations reconnect successfully.
It may also be worth validating that the recovered system produces results consistent with the expected application behaviour. Have you got a way to test this? Have you developed a benchmark or validation test that confirms the restored environment is behaving as expected?
“The real recovery test is whether users can once again use the AI service as intended.”
One practical way of improving recoverability is to automatically document the state of the AI platform. A simple inventory script can record model versions, GPU drivers, runtimes, container images, application versions and important configuration. That inventory can then form part of the recovery runbook, providing a known reference for rebuilding the environment rather than relying on memory or outdated documentation.
AI Does Not Change the Fundamentals
AI introduces some new types of data and some very large files, but most of the underlying backup principles remain familiar.
Critical data still needs to be protected. Recovery objectives still need to be defined. Backup copies should still be separated from production systems and protected against ransomware. Immutability and offsite copies still matter, and restore procedures still need to be tested.
What changes is the way we classify the components.
A $100,000 GPU server may contain an operating system, some containers and several large downloadable models, all of which could potentially be rebuilt.
Meanwhile, a tiny repository containing prompts, agent logic and proprietary configuration may represent the difficult part of the environment to recreate.
Hardware value and data value are not the same thing.
Once that is understood, some components will need conventional backup, some will be better protected through version control, some may require application-aware protection, and others may simply be rebuilt from known sources.
The objective is not to back up everything because it exists.
The objective is to have a tested, practical and proportionate way of returning the AI service to operation when something goes wrong.
Backup Summary Table
| Component | Importance | Typical protection approach | Rebuildable? | Notes |
| Source business data | Very high | Existing backup platform / application-aware backup | Usually no | Often the most important asset in the environment |
| Training data | Very high | Backup + retention policy | Sometimes | Particularly important if curated or transformed |
| Fine-tuned models / LoRA adapters | Very high | Backup | Technically, but often expensive | May represent significant IP and GPU time |
| Prompts / agent definitions / workflows | High | Git/version control + backup | Difficult | Small files, but often high intellectual value |
| Application source code | High | Git/version control + repository backup | Yes, if repository exists | Protect the repository rather than just the deployed copy |
| Vector database | Medium to high | Backup or database-consistent export | Usually | Depends heavily on rebuild time and RTO |
| Embeddings | Medium | Backup or regenerate | Usually | May be expensive or slow to recreate at scale |
| Processed / cleaned datasets | High | Backup | Sometimes | Can represent substantial work even when original data is protected |
| Base models | Low to medium | Usually re-download; retain metadata | Usually yes | Record exact model, version, quantisation and source |
| Container images | Low to medium | Registry or rebuild from definitions | Usually yes | Keep Dockerfiles, manifests and image versions |
| Operating system | Low | Rebuild | Yes | More important to document version and configuration |
| GPU drivers / CUDA / runtimes | Medium | Document rather than traditionally back up | Yes | Exact versions may be important for reproducibility |
| Infrastructure configuration | High | Git / configuration management / backup | Yes | Terraform, Ansible, Helm, Compose, Kubernetes manifests |
| Secrets / certificates / keys | Very high | Secure secrets platform + protected recovery process | Sometimes | Do not simply include plaintext secrets in normal backups |
| Logs / telemetry | Low to medium | Retain according to policy | Usually | Driven by audit, compliance and troubleshooting needs |
| Conversation history | Variable | Policy-driven | Usually | Privacy and retention may matter more than backup |
| Temporary cache files | Very low | Do not normally back up | Yes | Usually safe to recreate |
| AI platform inventory | High | Retain with runbook / repository | Yes, but should be captured regularly | Records exact state needed for recovery |
The important point is that backup priority should not be driven by file size or hardware value. Some of the largest components in an AI environment may be easy to recreate, while some of the smallest may contain the most important intellectual property.
The protection strategy should therefore be based on business value, recovery time and reproducibility rather than simply backing up everything that happens to reside on the server.
