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 LLMOps is the operational framework for managing the lifecycle of Large Language Models (LLMs). Unlike DevOps, which focuses on deterministic code, 

 must handle probabilistic assets like prompts, embeddings, and fine-tuned models. This shift requires a move from standard CI/CD to specialized 

 to ensure system traceability and trust.

 is a specialized set of practices for automating and managing the end-to-end lifecycle of LLM-powered applications. It extends MLOps principles to address the unique requirements of generative AI, specifically focusing on 

, prompt engineering, and vector-based data flows.

While DevOps focuses on application code and MLOps on traditional machine learning models, 

 Managing base models and their task-specific variants.

 Versioning the system instructions that dictate model behavior.

 Curating the "knowledge" used in Retrieval-Augmented Generation (RAG) systems.

 Monitoring outputs that change even when input code remains the same.

In essence, LLMOps is about operationalizing AI rather than just software binaries.

LLMOps vs DevOps: Why the difference matters

 isn't about choosing one over the other; it’s about understanding where 

 begin. DevOps is built for deterministic systems; if you deploy the same code, you get the same result. LLM pipelines are probabilistic, meaning the same "code" (prompt) can yield different outputs.

The core takeaway is that the shift from 

 involves handling much larger, more volatile assets that directly influence the "logic" of the application.

Why artifact management matters in LLMOps

In a traditional app, an artifact is just a compiled file. In AI, 

, teams face a "black box" problem where they cannot explain why a model suddenly began hallucinating or failing.

Effective AI artifact management solves for:

 Re-creating a specific model state using exact dataset snapshots.

 Tracking the lineage of a prompt to meet emerging AI regulations.

 Quickly reverting to a previous "known good" version of a prompt or embedding index.

 Preventing redundant training by reusing existing 

 generates a diverse array of non-code assets across the 

. Understanding these is key to moving beyond simple script-based deployments.

 These include base foundation models (like Llama 3 or GPT-4), fine-tuned adapters (LoRA/QLoRA), and quantized versions for edge deployment.

 Snapshots of training data, evaluation sets (Golden Sets), and synthetic data used for testing.

 Versioned system prompts, few-shot examples, and complex prompt chains that function as the "new source code."

 Vector database snapshots and the specific embedding models (e.g., Ada, BERT) used to generate them.

 Production logs, "LLM-as-a-judge" evaluation scores, and human-in-the-loop feedback.

MLOps vs LLMOps: Where traditional approaches fall short

Many teams assume their existing MLOps stacks can handle LLMs. However, 

 Traditional MLOps tools aren't built to treat a 50-word text string (a prompt) as a deployment-critical artifact. Furthermore, the 

 in LLMOps are much richer, requiring semantic monitoring rather than just simple accuracy metrics.

A common point of confusion is the choice between a 

 are for structured data used in tabular ML.

 (like weights and biases or MLflow) are the "System of Record" for the unstructured models and prompts that define an LLM app.

Managing these assets comes with significant 

challenges of artifact management in LLMOps

, including massive file sizes and the high velocity of prompt changes.

 Store prompts in version-controlled repositories, not hardcoded in your app.

 Use a single source of truth for all models and embeddings to avoid "shadow AI" across teams.

 Ensure every inference result is traceable back to the specific model version, prompt, and dataset used.

, never promote an artifact to production without passing an automated evaluation suite.

FAQ: Frequently asked questions on LLMOps

LLMOps manages probabilistic AI assets like models and prompts, while DevOps manages deterministic code and binaries. LLMOps requires specialized pipelines for evaluation and fine-tuning that don't exist in traditional CI/CD.

Why does artifact management matter in LLMOps?

It ensures that every AI output is traceable and reproducible. Without it, you cannot debug hallucinations, comply with AI audits, or reliably roll back failed updates.

What are the most important LLMOps workflows?

Key workflows include data ingestion for RAG, automated prompt evaluation, model fine-tuning, and continuous monitoring of inference quality.

The future of software is no longer just about code; it’s about 

 As LLMs move from experiments to core infrastructure, the transition from DevOps to LLMOps is inevitable.

 today will be the ones building the most reliable, scalable, and auditable AI systems of tomorrow.

To manage LLMOps at enterprise scale, use Cloudsmith as your single source of truth. Discover how by 

Parul Jhawar

Marketing

LLMOps vs DevOps: What LLMOps means for artifact management

 is rarely just an infrastructure move. For most DevOps and platform teams in 2026, it’s a once-in-a-decade opportunity to rethink tooling, eliminate legacy bottlenecks, and modernize the 

 end-to-end. One of the most overlooked, but highest-impact, areas to revisit during this transition is 

As organizations shift workloads and security controls into the cloud, the limitations of 

 quickly become visible. What once worked in on-premise environments often creates friction, risk, and massive operational overhead in a cloud-native world.

 isn’t just a "lift-and-shift" event. It’s the ideal moment to reassess how you store, secure, and distribute artifacts across modern 

Why legacy artifact repositories struggle during cloud migration

Traditional, self-hosted repositories were designed for static infrastructure and perimeter-based security. Cloud environments invert those assumptions.

During migration, teams commonly encounter:

 Legacy tools often require manual server provisioning or expensive over-capacity planning.

 Managing patches, database tuning, and storage maintenance for your own repository steals focus from your core product.

 When global teams depend on a single on-premise instance, latency sabotages developer velocity.

Keeping a legacy repository while modernizing everything else often results in a "partially modern" stack with legacy risk still embedded in your delivery pipeline.

Cloud migration exposes hidden software supply chain risk

Modern cloud adoption increases velocity, but speed without control amplifies risk across the 

 Cloud-scale builds pull in thousands of external packages that need immediate scanning.

 Difficulty tracking exactly "who built what and where" across fragmented environments.

 Brittle legacy controls that can't handle the dynamic nature of cloud-native deployments.

Re-evaluating artifact management during migration allows teams to embed 

 governance exactly when redesigning their pipelines.

The modernization opportunity: SaaS artifact repositories

Cloud migration creates the perfect window to replace self-hosted infrastructure with a 

 built for elasticity and global distribution.

 No more storage planning or maintenance; the platform automatically scales with your builds.

 A built-in Package Delivery Network (PDN) delivers artifacts worldwide to reduce latency.

 Features like automated vulnerability scanning and signature verification are baked in, not bolted on.

Instead of recreating legacy architecture in the cloud, organizations can move directly to a fully managed model aligned with cloud‑native principles. This shift transforms artifact management from a maintenance task into a strategic layer of the delivery platform.

When to move: Aligning your migration strategy

Teams often postpone modernization because migration feels complex. However, delaying the decision typically leads to "double migration" work, migrating the legacy tool today and replacing it tomorrow.

Aligning modernization with your cloud move avoids:

 Design your CI/CD for your final destination, not a temporary stop.

 Cloud-native migration scripts (like the Cloudsmith CLI) handle the transfer of binaries and metadata once.

 Ensure your new cloud environment launches with a clean, high-performance foundation from day one.

Signs your artifact management is holding you back

Before moving to the cloud, audit your current state. If these "silent killers" sound familiar, a lift-and-shift solution will only migrate your technical debt:

 Your team spends hours every month on repository upgrades, patching, and storage "garbage collection".

 Global developers or remote build agents face high latency because your on-premise repository lacks a global distribution network.

 You struggle to provide a complete "bill of materials" (SBOM) or audit trail for a security incident.

 Your pipelines rely on custom, "home-grown" scripts to move packages between environments because your tool doesn't support native promotion workflows.

The strategic ROI: What actually changes?

 during a cloud move isn't just a technical swap; it delivers measurable business impact:

 By eliminating manual bottlenecks and enabling faster access to dependencies, teams often see 

 Centralized policy enforcement and automated vulnerability scanning move security from a "final check" to an integrated part of the build.

 removes the "toil" of server management, allowing your DevOps engineers to focus on product innovation rather than infra-maintenance.

 You trade hidden infrastructure costs and administrative salaries for a predictable, transparent SaaS model.

Evaluating alternatives: Beyond JFrog and Nexus

Many organizations begin cloud migration using legacy tools like 

, only to find they were built for a different era of infrastructure. Modern SaaS platforms eliminate the need to manage repository infrastructure while delivering stronger governance and global performance. As a result, more teams are looking for 

 that offer a fully managed, "Zero-Ops" experience.

 streamlines the process to minimize disruption and accelerate value realization.

Why global leaders migrate artifact management to Cloudsmith

, designed specifically for modern DevOps and secure software delivery for the security landscape of 2026 and beyond.

 A true SaaS experience with no databases to manage and no servers to patch.

 Hundreds of nodes deliver artifacts from the edge, ensuring your global build agents always have high-speed access.

 One single source of truth for Docker, npm, Maven, Python, and 30+ other formats.

 Automated provenance tracking and signature verification help keep you compliant with key security standards and ensure that what you ship is exactly what you built.

Conclusion: Don’t just move to the cloud – modernize what matters

Cloud migration is a rare opportunity to fix the "foundation" of your house before you move in the furniture. By re-evaluating your 

 now, you ensure your cloud-native future is fast, secure, and, most importantly, manageable.

The most successful cloud migrations don’t just replicate the past. They modernize the platform that powers everything built next.

 with our experts to simplify the process.

It is the practice of storing, securing, and distributing build outputs, such as Docker images or Maven packages, throughout the development lifecycle. It ensures your builds are reproducible and secure.

Why reconsider artifact repositories during cloud migration?

Updating your repository during a move avoids duplicate work and ensures that a legacy on-premises artifact manager doesn’t constrain your new cloud infrastructure.

What are the risks of keeping a self-hosted repository in the cloud?

 in the cloud still requires manual patching and scaling. This increases costs and creates "visibility gaps" that can lead to security breaches.

How does a SaaS artifact repository improve security?

SaaS platforms provide centralized governance, immutable storage, and automated compliance auditing, all of which are critical to a secure 

Why cloud migrations are the best time to re-evaluate your artifact management

For years, the bottleneck in software was “how fast can we write code?” Today, Generative AI shifts that bottleneck to 

For years, the bottleneck in software was “how fast can we write code?” Today, Generative AI shifts that bottleneck to “how fast can we secure it?”

As organizations move from experimentation to production-grade AI, they are discovering that traditional DevOps tooling wasn’t built for a non-deterministic world. For example, static software composition analysis (SCA) scanners that assume deterministic dependency graphs or CI policy gates that validate known build artifacts. When a model generates code rather than a human, the software supply chain changes overnight.

Securing non-deterministic systems: A practical guide for AI artifacts and LLMOps

, explores three emerging security frontiers that every organization adopting AI must address:

1. AI-generated code introduces supply-chain hallucinations

LLMs generate dependencies probabilistically, not deterministically. This creates the emerging 

 attack vector, where attackers register hallucinated package names suggested by AI tools and weaponize them with malicious payloads.Without validation and artifact governance, a single copied command can silently compromise an enterprise environment.

2. AI models behave like executable software, not passive data

Modern model formats can execute arbitrary code during deserialization, most notably through Python pickle-based loading.This 

 means downloading an unverified model from public registries such as Hugging Face or Ollama can result in full system compromise.Secure AI development requires scanning, signing, and favoring restricted formats like 

, alongside enforcing trusted provenance for every model artifact.

3. AI productivity and orchestration layers expand the attack surface

Frameworks that connect models to enterprise data and automate workflows introduce a new class of high-impact vulnerabilities.Recent RCE exploits in orchestration tools demonstrate that 

LLMOps infrastructure itself is now part of the software supply chain

, and must be sandboxed, authenticated, and governed like any production system.

Our full guide provides a strategic roadmap for navigating the shift from DevOps to LLMOps, deconstructing threats in frameworks like Langflow, and building a “sandbox-by-default” development lifecycle.

AI artifacts: The new software supply chain blind spot

The Hybrid Repository Structure: Balancing Control and Flexibility

And now, let’s dive into part three: How 

 keep your multi-format repositories secure, consistent, and developer-friendly.

In the first two blogs of this series, we explored why repository structure matters and how Cloudsmith’s Hybrid Repository Structure balances control with flexibility. While we touched on policies and permissions, we didn’t dive into the 

 mechanics of how access control ensures artifact security, traceability, and consistency, especially in 

, where different packages, languages, and tooling coexist in a single place.

While multi-format repositories allow for more flexibility in how your repositories are set up, they can introduce a new way of thinking about how and when different artifacts can be accessed and by whom. This blog breaks down how Cloudsmith provides fine-grained, flexible, and secure controls for teams of any size.

Cloudsmith provides the following user roles:

These roles help limit access based on organizational need and provide the foundation for more granular permissions.

You can explore the full breakdown of Cloudsmith roles, permissions, and privileges in our documentation.

You can read more about user roles, permissions, and privileges in Cloudsmith 

Every Cloudsmith customer is given the opportunity to set default global privileges. These global default privileges are set for the “Member” user role in Cloudsmith, which is often suited best for individual developers. Within a customer’s global workspace privileges, organizations can choose to grant members the ability to create new teams, invite new users, and even create new repositories. Organization owners can also grant “blanket” repository privileges to all users within Cloudsmith. 

While we offer the flexibility for organizations to shift responsibility and access to the developer, we often see our enterprise customers lean away from blanket permissions and access toward more fine-grained permissions. As an example, we have a semiconductor manufacturing customer that has disabled default workspace global privileges so that only Organisation owners are the only users who can invite new users, create new teams, invite 3rd-party collaborators, and create new repositories. 

In addition, this customer has access control, and privileges are scoped down to specific teams. Default global repository privileges are disabled so that they can choose exactly which team(s) should have access to repository(s). Even Cloudsmith service accounts are grouped together within a team for ease of tracking permissions and access when it comes to build and deployment times. 

If we zoom in once and look at the repository level in Cloudsmith, we can assign a default privilege for organization members for accessing packages within the repository, and we can assign specific privileges for specific teams, users, or service accounts.

This is the stage where you’ll have to consider what packages the repository is storing and if you want developers and service accounts to have default admin, read, or write permissions to the repository. Continuing to use my customer example, they have chosen to set default read permissions for all repositories; however, specific service accounts have write and 

 to different repositories. You may be okay with your developers downloading artifacts to their local machine for testing or even service accounts requesting stored artifacts tied to staging and production environments.

On the other hand, there are customers that may not want their developers having the ability to write to every repository and would most likely want specific service accounts tied to their CI/CD pipelines to only have write permissions. While this is generally best practice, there are certainly exceptions to this rule, as we also have platform teams that choose to grant specific developer teams write access to specific artifact repositories.

On top of the permission and access control settings we’ve discussed, Cloudsmith goes even further to ensure that, through various additional settings, platform teams can decide exactly what their developers need and don’t need to be reconfigured within a repository.

Platform teams are able to grant or deny developer permissions, such as:

Copying Packages From One Repository to Another

Moving Packages From One Repository to Another

Managing, Using, Or Viewing Entitlement Tokens

If you thought that wasn’t enough, Cloudsmith takes it a step further by scoping down permissions to the individual developer’s generated packages. So while platform teams can restrict tampering of artifacts generated by other developers or systems, they can also decide if they would like to allow developers the ability to scan, move, copy, delete, or resync their own packages.

Most of the enterprise customers I work with allow developers to do as they please with their own packages to avoid a developer mutiny! In either case, these user actions are catalogued in our 

 for enhanced observability across your Cloudsmith environment.

For instances where our customers want to be very particular about what, how, and when users can access specific packages, Cloudsmith offers 

. These scoped tokens are read-access only, so there is never a risk of a user performing a write action against the repository they have limited access to.

Customers are able to restrict access by creating a precise search query to narrow down specific packages within a repository, should they choose not to provide visibility into all the packages within the repository. On top of visibility restrictions, customers can also add token usage restrictions to avoid prolonged access and usage of the token. Parameters include:

Typically, we see our customers use entitlements for 3rd party software distribution or even for systems that don’t require logins to Cloudsmith.

For our security-conscious customers, Cloudsmith also offers the ability to configure 

 based on country, with an easy-to-use preconfigured list of countries to choose from. Choose to deny or allow access from specific countries. Although keep in mind that theoretically, no unauthenticated user should have access to your Cloudsmith workspace in general. This restriction applies more towards open-source repositories that you may be hosting in Cloudsmith, but it’s always a good idea to practice defence-in-depth!

If geo-based restrictions are too broad, you can scope down to IP-based restrictions to either allow or deny client access based on IP address. This added protection ensures that requests coming from clients with an unapproved IP address do not have access to your repositories. 

With all of these configuration options, it can be tricky to decide how you want to best enable your developers while balancing security and flexibility. Not to worry—the Cloudsmith Customer Success team is here to help you in your decisions throughout the onboarding process. We’ve walked through these decisions with many customers and have outlined decision impacts for you so you’re not left wondering “what if”. Learn more about Cloudsmith 

 to get started with us. See you on the other side! 

1. What is access control in a multi-format repository?

Access control defines who can read, write, or administer artifacts across different package formats stored in the same repository. It ensures secure, consistent governance across diverse tooling.

2. How do permissions work in Cloudsmith for multi-format repositories?

Cloudsmith supports global, repository-level, team-based, and user-specific permissions, allowing organizations to tailor access to packages, teams, service accounts, and even individual artifact actions.

3. Why is fine-grained access control important in software supply chain security?

Fine-grained controls reduce the blast radius of errors, prevent unauthorized writes, and help organizations enforce policies required by modern software supply chain frameworks like SLSA and SSDF.

4. What are entitlement tokens used for in Cloudsmith?

Entitlement tokens provide scoped, read-only access to specific artifacts without requiring user accounts, making them ideal for external distribution, automation, or least-privilege workflows.

5. Can developers manage their own packages in Cloudsmith?

Yes. Cloudsmith allows permissions that let developers manage only the packages they personally created—without putting others’ artifacts at risk.

6. How does Cloudsmith support secure CI/CD pipeline access?

Service accounts can be granted precisely the permissions needed for pipeline operations (like write or admin) while keeping developers and collaborators restricted to read-only or scoped access.

7. What’s the difference between global and repository-level privileges?

Global privileges affect the entire workspace, while repository-level privileges enable granular control over specific teams, users, or service accounts interacting with a particular repository.

Cristian Garcia

Senior Customer Success Manager

Access control & permissions for multi-format repositories

In a world where software ships in seconds, teams are still chained to legacy systems built for a different era. What once passed as “good enough” for storing and distributing builds has become a drain on productivity - adding risk, slowing delivery, and quietly inflating costs year after year.

In this post, we’ll break down the hidden cost of legacy artifact repositories, discuss the importance of modernizing through cloud-native artifact management, and demonstrate how you can leave the old infrastructure that has been slowing your software supply chain.

Legacy artifact management involves older on-premise artifact repositories or in-house custom systems. These tools were designed in another era, when teams used monolithic applications and updates were done once a year or even less.

The modern reality is very different. Cloud-native development, continuous CI/CD pipelines, and distributed engineering teams need a modern artifact management approach that delivers scalability, uptime, and built-in security.

Getting Started with Helm and Cloudsmith

More articles

LLMOps vs DevOps: What LLMOps means for artifact management

Why cloud migrations are the best time to re-evaluate your artifact management

AI artifacts: The new software supply chain blind spot

Access control & permissions for multi-format repositories

The true cost of legacy artifact management

The Hybrid Repository Structure: Balancing Control and Flexibility