NVIDIA NIM
Last reviewed
May 7, 2026
Sources
15 citations
Review status
Source-backed
Revision
v1 ยท 4,708 words
Improve this article
Add missing citations, update stale details, or suggest a clearer explanation.
Suggest edit
CtrlK
Last reviewed
May 7, 2026
Sources
15 citations
Review status
Source-backed
Revision
v1 ยท 4,708 words
Add missing citations, update stale details, or suggest a clearer explanation.
NVIDIA NIM (NVIDIA Inference Microservices) is a software platform from NVIDIA that packages AI models into containerized microservices for deployment across cloud environments, data centers, workstations, and edge hardware. Announced at NVIDIA GTC in March 2024, NIM reduces the operational complexity of running production AI inference by bundling model weights, optimized inference engines, and an OpenAI-compatible REST API into a single Docker container. Enterprises can pull a NIM from the NGC container registry, run it on any NVIDIA GPU infrastructure, and begin serving requests within minutes rather than weeks.
The platform sits at the intersection of NVIDIA's hardware business and its push into enterprise software. By tying optimized inference to NVIDIA's GPU lineup and offering production access through an NVIDIA AI Enterprise license, NVIDIA positions NIM as both a developer convenience layer and an enterprise software subscription.
Jensen Huang introduced NIM during his GTC 2024 keynote at the San Jose Convention Center, framing it as a way to turn NVIDIA's GPU installed base into a production AI deployment platform. The announcement came alongside a set of healthcare-specific NIMs under the BioNeMo brand, covering drug discovery, medical imaging, and genomics. NVIDIA described NIM as part of its broader strategy to sell software and services on top of its GPU hardware business, with NVIDIA AI Enterprise providing the commercial licensing framework.
Before NIM, deploying an open-weight language model to production required assembling several moving parts manually. A team would download model weights from a repository such as Hugging Face, select and configure an inference server (such as vLLM, Text Generation Inference, or NVIDIA Triton Inference Server), compile TensorRT engines for the target GPU architecture, write a serving wrapper that exposed an HTTP endpoint, and then containerize the whole stack. For a team without deep MLOps experience, that process could consume weeks of engineering time. For teams who did have that experience, the work was repeatable but not portable: an engine compiled for an A100 cluster would not run on an H100 without recompilation.
The problem was worse at scale. Running multiple models simultaneously meant maintaining separate container images, separate version pins, and separate optimization profiles. Updating a model from one version to the next triggered a full revalidation cycle. Fine-tuned adapters added another layer of bookkeeping.
NVIDIA's response was to pre-solve all of that for its own GPU ecosystem. Each NIM ships with multiple pre-compiled inference profiles, and the container selects the right profile at launch based on the detected hardware. The operator does not need to know whether TensorRT or vLLM is running under the hood; the container handles it.
The timing of NIM's release reflected industry dynamics in early 2024. The open-weight model ecosystem had matured significantly: Meta's Llama 2 and Mistral 7B had demonstrated that capable models could be self-hosted, and demand for on-premises inference was growing among enterprises that could not or would not send proprietary data to third-party API providers. At the same time, the tooling to actually deploy those models remained fragmented and difficult to validate. NIM addressed that gap by treating inference deployment as a product rather than a DIY engineering problem.
A NIM container is a Docker image stored in NVIDIA's NGC container registry at nvcr.io. The image includes:
/v1/chat/completions, /v1/completions, and /v1/embeddings endpointsThe container exposes port 8000 by default. To pull and start a NIM for Llama 3.1 8B, an operator authenticates to the NGC registry with an API key, then runs a single docker run command with the image path and a mount point for a local model cache directory.
NIM uses a profile system to select the appropriate inference backend. On first startup, the container inspects the available GPU hardware, reads the model's profile manifest, and loads the best matching engine. Profiles are tagged by:
For high-end data center GPUs with a pre-compiled TensorRT-LLM profile, the container loads that engine directly. For other NVIDIA GPUs or architectures without a compiled profile, it falls back to vLLM. SGLang is available for some models as a third option.
When a TensorRT-LLM profile is available, it provides the highest throughput of the three backends. TensorRT-LLM compiles the model graph into a GPU-specific execution plan, fuses operations, and applies continuous batching along with paged KV-cache management. NVIDIA publishes two TensorRT-LLM profile variants per model: a latency profile that minimizes time-to-first-token and inter-token latency for interactive workloads, and a throughput profile that maximizes tokens generated per second for batch workloads.
On H100 SXM hardware, NVIDIA benchmarks show approximately 2x throughput improvement for Llama 3.1 8B compared to a default deployment: roughly 1,200 tokens per second with TensorRT-LLM optimization versus around 610 tokens per second without it.
The HTTP server inside each NIM exposes endpoints that are structurally identical to OpenAI's API. A developer who has code calling client.chat.completions.create() from the OpenAI Python SDK can point that code at a locally running NIM by changing a single base URL, with no other modifications. NVIDIA also exposes its own extended endpoints for features that have no OpenAI equivalent, such as per-request LoRA adapter selection.
The /v1/models endpoint returns metadata about the loaded model, and the OpenAI-compatible format allows direct integration with frameworks such as LangChain, LlamaIndex, and most other LLM orchestration libraries.
NIM supports dynamic loading of Low-Rank Adaptation (LoRA) adapters without restarting the container. Adapters trained with the NVIDIA NeMo framework or the Hugging Face PEFT library can be stored in a local directory, and the NIM loads them into a multi-tier cache at runtime. When a client submits an inference request, it can specify a named LoRA adapter; the server pulls the corresponding weights and applies them to the base model computation.
This multi-LoRA serving mode lets a single NIM container serve dozens of fine-tuned variants of one base model simultaneously, without duplicating the base weights on disk or in GPU memory. NVIDIA's documentation refers to deploying a large collection of adapters this way as a "swarm of LoRA adapters."
NVIDIA ships Helm charts for deploying NIM on Kubernetes clusters. The NVIDIA NIM Operator (available as a separate component) automates the lifecycle of NIM pods, handling version upgrades, health checks, and GPU resource scheduling. NIM integrates with NVIDIA's GPU Operator for Kubernetes, which manages CUDA drivers and device plugins across cluster nodes.
For multi-NIM deployments, NVIDIA provides the NVIDIA Inference Manager (part of NVIDIA AI Enterprise), which routes requests across multiple running NIMs and provides load balancing.
The NVIDIA NIM Operator is a Kubernetes operator designed to automate NIM deployment and management within a cluster. It handles the full pod lifecycle: pulling updated container images from NGC, draining connections before rolling restarts, restoring previous versions on failure, and reporting health status to Kubernetes cluster management tools. The operator reads a custom resource definition (CRD) that specifies which NIM to run, the GPU resource limits, and the model cache mount path. Once the CRD is applied, the operator handles the rest, including authentication to the NGC registry using a Kubernetes secret.
For air-gapped deployments (environments with no internet access), NVIDIA supports pre-pulling NIM container images to an internal container registry and mounting pre-downloaded model weights from a local network storage volume, so that NIMs never need to contact NVIDIA's infrastructure after initial setup.
NIM containers expose Prometheus-compatible metrics at a /metrics endpoint. Standard metrics include request count, request latency histograms, token throughput (input tokens per second and output tokens per second), batch size distribution, KV-cache utilization, and GPU memory usage. These metrics integrate with Grafana dashboards for production monitoring. NVIDIA provides reference dashboard configurations alongside its Helm charts.
Request-level logging can be configured to emit structured JSON logs, which can be forwarded to centralized logging systems such as Elasticsearch or Splunk. Log fields include request ID, model name, prompt token count, completion token count, latency, and the selected inference profile.
In August 2024, NVIDIA extended the NIM platform with NIM Agent Blueprints, a catalog of pre-built, end-to-end generative AI application workflows. A Blueprint packages multiple NIM containers, orchestration logic, sample code, and deployment charts for a specific enterprise use case. Developers can take a Blueprint, plug in their own data and branding, and deploy a functioning application rather than building each component from scratch.
The initial Blueprint releases covered three use cases:
NVIDIA announced plans for monthly Blueprint releases covering customer service, content generation, software engineering, retail shopping advisors, and R&D applications. Partners including Accenture, Deloitte, Cisco, Dell Technologies, Hewlett Packard Enterprise, Lenovo, SoftServe, and World Wide Technology were among the first systems integrators offering Blueprint-based solutions to enterprise customers.
Blueprints are published on build.nvidia.com alongside documentation, sample code, and Helm charts. Each Blueprint is designed around what NVIDIA calls a "data-driven generative AI flywheel": user interactions with the deployed application generate new data, which can be used to fine-tune LoRA adapters, which are then loaded back into the NIM without restarting the service. NVIDIA NeMo handles the fine-tuning step, and NVIDIA AI Foundry provides the production environment for managing adapter versions.
The customer service digital human Blueprint integrates several NIM types in sequence. An audio NIM transcribes the user's voice. An LLM NIM generates a text response. A text-to-speech NIM converts the text to audio. An Avatar NIM (from NVIDIA ACE, the Avatar Cloud Engine platform) drives the facial animation of a rendered character. All four components communicate over the local network using the same OpenAI-compatible API format, which makes it possible to swap any component for an alternative NIM without rewriting the orchestration logic.
NVIDIA later rebranded NIM Agent Blueprints simply as NVIDIA Blueprints, expanding the catalog beyond agentic workflows to include reference architectures for video analytics, document processing, and scientific computing.
The NIM catalog at build.nvidia.com lists over 100 models across multiple domains. The catalog is organized into several categories.
The LLM catalog includes models from multiple providers packaged as NIMs:
| Model family | Examples |
|---|---|
| Meta Llama | Llama 3.1 8B, 70B, 405B; Llama 3.2 1B, 3B, 11B, 90B |
| Mistral / Mixtral | Mistral 7B, Mistral NeMo 12B, Mixtral 8x7B, Mixtral 8x22B, Mistral Large |
| Gemma 2 2B, 9B, 27B | |
| Microsoft | Phi-3 Mini, Phi-3 Medium, Phi-3.5 |
| NVIDIA Nemotron | Nemotron-4 340B, Nemotron-Mini 4B, Llama 3.1 Nemotron 70B |
| DeepSeek | DeepSeek-R1 (added as preview, January 2025) |
| Code-focused | Code Llama 70B, Qwen2.5 Coder |
Llama 3.1 405B, one of the largest openly licensed models available, requires multiple H100 GPUs and is served with tensor parallelism enabled across those GPUs.
NVIDIA also packages its own fine-tuned Nemotron models. Llama 3.1 Nemotron 70B is a Nemotron post-trained version of Llama 3.1 70B that NVIDIA released as an instruction-following model with particularly high scores on MT-Bench. The Nemotron-Mini 4B model is tuned for edge and workstation deployment on RTX hardware.
The model catalog is updated regularly as new open-weight models are released. NVIDIA typically adds a NIM for a major new model within weeks of the model's public release, though the timeline depends on the GPU compatibility work required to produce TensorRT-LLM profiles for it.
NIM includes a set of text and multimodal embedding NIMs for search and retrieval applications:
| Model | Description |
|---|---|
| NV-EmbedQA-E5-v5 | Text embedding for question-answering retrieval |
| NV-EmbedCode-v1 | 7B Mistral-based model for code retrieval |
| NV-CLIP | Multimodal image and text embedding |
| Llama 3.2 NeMo Retriever Embedding | 1.6B multimodal embedding for RAG pipelines |
Vision language model NIMs accept image plus text inputs and generate text outputs:
| Model | Description |
|---|---|
| Llama 3.2 Vision 11B / 90B | Meta's multimodal Llama models |
| Mistral NeMo VLM | 12B vision-language model |
| Nemotron Nano 12B v2 VL | Multi-image and video understanding |
| NVIDIA NVLM-D-72B | High-parameter vision model |
| NEVA-22B | NVIDIA's own vision-language model |
NVIDIA's Riva speech platform is packaged as NIM containers for automatic speech recognition (ASR), text-to-speech synthesis (TTS), neural machine translation (NMT), and speaker diarization. Riva NIMs include both streaming and batch inference modes, with the streaming ASR NIM able to return partial transcriptions with low latency, suitable for live transcription applications.
For healthcare and life sciences, NVIDIA offers BioNeMo NIMs covering protein structure prediction (ESMFold), generative molecular design (MolMIM), molecular docking (DiffDock), and genomics analysis. These launched alongside the initial NIM announcement at GTC 2024, with NVIDIA describing them as more than two dozen healthcare-specific microservices.
ESMFold is a protein language model from Meta that predicts protein 3D structure from amino acid sequences. Packaged as a NIM, it exposes a REST API that accepts a FASTA-format amino acid string and returns structure predictions in PDB format, allowing downstream molecular visualization and docking tools to consume the output directly.
MolMIM (Molecular Multimodal Information Model) is a generative chemistry model that proposes new drug-like molecules with specified physicochemical properties. The MolMIM NIM accepts a SMILES string and a set of property constraints, and returns novel candidate molecules ranked by predicted property values.
DiffDock is a diffusion model for protein-ligand docking. Given a protein structure and a small molecule, it predicts the 3D binding pose, which is useful for evaluating whether a candidate compound is likely to bind to a therapeutic target.
NIM also covers image and video generation, including Stable Diffusion and other diffusion-based models, available through the visual generative AI NIM catalog. These NIMs accept text prompts (and optionally input images) and return generated images via a REST API. Enterprise deployments use these for content creation pipelines, product visualization, and synthetic data generation for training computer vision models.
NIM has two access tiers with distinct terms.
Any developer with a free NVIDIA Developer Program account can generate an NGC API key and pull NIM containers for development and testing. The hosted inference API at build.nvidia.com provides free access to NIM endpoints (backed by NVIDIA DGX Cloud infrastructure) for evaluation, limited by a rate cap per key. This tier does not require an NVIDIA AI Enterprise license and is intended for prototyping.
Deploying NIM in production requires an NVIDIA AI Enterprise license. NVIDIA AI Enterprise is a commercial software subscription that includes NIM along with other NVIDIA AI frameworks such as NeMo, Riva, and RAPIDS.
NVIDIA AI Enterprise pricing (as of 2024-2025):
| License type | Price per GPU |
|---|---|
| 1-year subscription | $4,500 |
| 2-year subscription | $9,000 |
| 3-year subscription | $13,500 |
| 5-year subscription | $18,000 |
| Perpetual license | $22,500 (includes 5 years of support) |
Cloud deployments on AWS, Google Cloud, Azure, and Oracle Cloud can use a pay-as-you-go model at approximately $1 per GPU per hour, billed on top of the cloud provider's instance costs.
EDU institutions and startups in NVIDIA's Inception program receive a 75% discount on list prices. Startups can purchase up to 64 one-year subscriptions at reduced rates through the Inception program; companies in the Connect program for software developers can buy up to 16.
Pricing is per GPU rather than per NIM or per model. A server with eight H100 GPUs running a single NIM costs the same license fee as one running eight NIMs on the same hardware.
NVIDIA AI Enterprise also includes a 90-day free evaluation license for organizations that want to test production NIM deployment before committing to a subscription. The evaluation license covers the same features as the paid subscription and can be activated through the NVIDIA AI Enterprise portal.
NVIDIA AI Enterprise bundles more than just NIM. The subscription includes NVIDIA NeMo (for fine-tuning and training), NVIDIA Riva (for speech AI), NVIDIA Merlin (for recommendation systems), NVIDIA cuOpt (for route optimization), and NVIDIA RAPIDS (for GPU-accelerated data science). This bundling means that organizations already using other NVIDIA AI software may find that NIM production access comes included in a license they already hold.
NIM is accessible through three primary routes:
Hosted API (build.nvidia.com): Free endpoints hosted on DGX Cloud hardware, returning completions via the OpenAI-compatible format. Suitable for prototyping and evaluation. Rate-limited per API key.
Self-hosted containers: Pull from nvcr.io using an NGC API key, run on any supported NVIDIA hardware. Requires NVIDIA AI Enterprise for production. Operators have full control over infrastructure, data residency, and model versions.
Cloud marketplace deployments: AWS SageMaker, Google Cloud Vertex AI, Microsoft Azure AI, and Oracle Cloud all offer NIM through their respective AI model catalogs. On these platforms, the NIM container runs on GPU instances managed by the cloud provider, and the NVIDIA AI Enterprise license can be included in the cloud billing.
NVIDIA also supports deployment on NVIDIA-Certified Systems, a program covering validated server hardware from Cisco, Dell Technologies, Hewlett Packard Enterprise, Lenovo, and Supermicro. For on-premises deployments, validated Kubernetes distributions include Red Hat OpenShift, VMware Tanzu, and Canonical Charmed Kubernetes.
The enterprise inference deployment space includes several alternatives to NIM, each with different tradeoffs.
| Platform | Deployment model | Hardware requirement | API compatibility | License |
|---|---|---|---|---|
| NVIDIA NIM | Self-hosted container or hosted API | NVIDIA GPUs only | OpenAI-compatible | NVIDIA AI Enterprise (production) |
| Hugging Face Inference Endpoints | Managed cloud | Multi-vendor GPUs | OpenAI-compatible (partial) | Per-hour billing |
| vLLM | Self-hosted, open source | NVIDIA + AMD GPUs | OpenAI-compatible | Apache 2.0 |
| Modal (platform) | Serverless cloud | NVIDIA GPUs | Custom API | Per-second billing |
| NVIDIA Triton Inference Server | Self-hosted, open source | Multi-hardware | gRPC / HTTP | Apache 2.0 |
| Replicate | Managed cloud | NVIDIA GPUs | Custom API | Per-second billing |
| Together AI | Managed cloud / self-hosted | NVIDIA GPUs | OpenAI-compatible | Per-token billing |
NIM vs. vLLM: vLLM is the closest open-source analog to NIM. Both use continuous batching, paged attention, and OpenAI-compatible APIs. vLLM supports AMD GPUs and Intel GPUs in addition to NVIDIA, and it is fully open source under the Apache 2.0 license. NIM wraps vLLM as one of its backends and adds pre-compiled TensorRT-LLM profiles (unavailable in plain vLLM), multi-LoRA dynamic serving, enterprise support contracts, and a curated model catalog vetted by NVIDIA. For teams already comfortable with vLLM who do not need TensorRT-LLM profiles or enterprise SLAs, NIM adds overhead without clear benefit. For teams that want a supported, pre-optimized stack, NIM reduces the configuration burden.
NIM vs. Hugging Face Inference Endpoints: Hugging Face Inference Endpoints provides a managed service for deploying models from the Hugging Face Hub. The service supports multiple GPU providers and does not require an NVIDIA-specific license. It runs TGI or vLLM under the hood and charges per hour of GPU time. NIM offers more GPU-specific optimization (TensorRT-LLM profiles) and a richer model catalog outside the Hub (domain models, speech models, molecular biology models), but requires an NVIDIA AI Enterprise contract for production self-hosting. In June 2024, Hugging Face and NVIDIA announced a collaboration to offer NVIDIA-optimized NIM endpoints through Hugging Face's Serverless Inference API, blending the Hub's model discovery with NVIDIA's inference stack.
NIM vs. Modal: Modal (platform) offers serverless GPU compute billed per second, with containers that scale to zero when idle. It does not provide a curated model catalog; operators bring their own code and containers. NIM requires persistent infrastructure (or cloud instances) that are always running, which adds cost for sporadic workloads but reduces latency for steady-state production traffic. Modal's flexibility suits research and variable workloads; NIM targets steady-state enterprise production traffic.
Enterprise deployments of NIM cluster around several patterns.
Customer-facing chat and copilots: Organizations integrate NIM-served LLMs into internal helpdesks, customer service interfaces, and productivity copilots. ServiceNow, SAP, and Box were among the enterprise software vendors announced at GTC 2024 as early NIM adopters, integrating NIM-powered inference into their platforms.
Retrieval-augmented generation pipelines: NIM provides both embedding models (for indexing and query encoding) and LLMs (for answer generation) within the same container ecosystem, making it straightforward to build RAG pipelines where both components are served through the same API format. The NeMo Retriever NIM microservices are specifically tuned for retrieval tasks.
Code generation and software development: Code-specific models like Code Llama 70B are available as NIMs. Enterprises deploy these behind internal developer tools or IDEs, serving code completions and explanations within their security perimeter rather than routing code to third-party APIs.
Healthcare and life sciences: Pharmaceutical companies use BioNeMo NIMs for in silico drug screening, where molecular generation models propose candidate compounds and structure prediction models (ESMFold, DiffDock) filter them. More than 200 organizations were integrating BioNeMo NIMs into their workflows within a year of the platform's launch.
Edge and workstation inference: NIM supports deployment on workstations with RTX 4080 and RTX 4090 GPUs, enabling inference at the edge for applications where sending data to a cloud endpoint is not acceptable due to latency, cost, or data privacy constraints. NVIDIA RTX AI PCs are an explicit supported platform.
Financial services and security: CrowdStrike was among the GTC 2024 launch partners, integrating NIM into its Falcon platform for security analytics. Financial institutions use NIM-hosted LLMs for document analysis, compliance review, and fraud detection, running inference within their own data centers to satisfy regulatory requirements around data residency.
Manufacturing and digital twins: Siemens, one of the named NIM adopters, explored integrating NIM with its industrial AI toolchain for predictive maintenance and process optimization. NVIDIA's Omniverse platform, which is used for industrial simulation and digital twins, integrates with NIM for language and reasoning tasks within simulation workflows.
Document processing at scale: The multimodal PDF extraction Blueprint lets organizations process large archives of PDFs, extracting text, tables, charts, and figures into structured formats suitable for downstream analysis or RAG pipelines. This use case targets legal, financial, and compliance document workflows where structured extraction from unstructured documents is a persistent bottleneck.
At the GTC 2024 announcement, NVIDIA named Adobe, Cadence, CrowdStrike, Getty Images, SAP, ServiceNow, and Shutterstock as early NIM adopters. By August 2024, when NIM Agent Blueprints launched, the named enterprise customer list had grown to include Lowe's, Siemens, Cohesity, Dropbox, NetApp, and Glean.
Cloud providers integrated NIM into their AI model catalogs: AWS SageMaker, Google Cloud Vertex AI, and Microsoft Azure AI all offered NIM containers through their platforms by mid-2024. Oracle Cloud added NIM to Oracle Cloud Infrastructure in a more limited capacity.
Hardware partners including Dell, HPE, Lenovo, and Supermicro validated NIM on their NVIDIA-Certified server lines and began offering NIM as a pre-installed option on AI factory deployments. Cisco validated NIM on its UCS server platforms.
Red Hat OpenShift published guidance for running NIM on OpenShift AI in May 2025, reflecting growing interest in running NIMs within Kubernetes environments that use Red Hat's enterprise Linux distribution.
NIM 1.4, released in December 2024, achieved 2.4x faster inference than the 1.0 release across several benchmark configurations. DeepSeek-R1 was added as a preview NIM in January 2025 following the model's public release.
Cloudera reported 36x performance improvements after integrating NIM into its data platform, compared to previous inference configurations. This figure reflects both the TensorRT-LLM optimization and the removal of per-request engine compilation overhead that had characterized their earlier setup.
NVIDIA announced that as of late 2024, hundreds of enterprises had deployed NIM in production, with the catalog expanding from the initial 20+ models at GTC 2024 to over 100 models by the end of the year. The build.nvidia.com platform accumulated millions of API calls from developers using the free hosted endpoints for evaluation.
The NIM ecosystem also expanded through NVIDIA's AI Foundry service, a managed offering that combines NIM with NeMo fine-tuning and model management. AI Foundry targets organizations that want to customize a foundation model with proprietary data and deploy it as a NIM, without managing the fine-tuning infrastructure themselves.
NIM has several meaningful constraints for organizations evaluating it.
NVIDIA hardware exclusivity. NIM containers only run on NVIDIA GPUs. Organizations with AMD or Intel GPU investments, or those looking to hedge against hardware vendor concentration, cannot use NIM on non-NVIDIA hardware. vLLM and Triton Inference Server both support AMD ROCm GPUs.
Production licensing cost. The $4,500 per GPU per year subscription cost is substantial for small organizations or projects with limited GPU counts. For a small four-GPU inference server, the annual license fee alone is $18,000, before accounting for hardware and cloud costs. Open-source alternatives like vLLM have no licensing cost.
Limited to NVIDIA's model catalog. While the catalog covers most popular open-weight models, it does not include every model available on Hugging Face. Less popular or recently released models may not yet have a NIM, requiring teams to fall back to other serving frameworks for those models.
Closed optimization profiles. The pre-compiled TensorRT-LLM profiles are compiled artifacts distributed by NVIDIA; they are not open source. Users cannot inspect or modify the compilation process, and the profiles are only available for GPU architectures and models that NVIDIA has chosen to optimize.
Dependency on NVIDIA infrastructure. Pulling container images requires authenticating to NVIDIA's NGC registry. If NGC has downtime, or if NVIDIA changes access terms, pulling updated images or deploying to new infrastructure becomes harder. Organizations with strict supply-chain requirements may find this dependency difficult to accept.