Open-source large language models can now match proprietary alternatives in performance and capabilities. Over the past two years, models like Llama, Mistral, and Falcon have evolved from research experiments into production systems running in banks, hospitals, and government agencies.
According to Hugging Face, downloads of open-source models surged from 1 billion in 2023 to over 10 billion in 2024. Stanford’s AI Index shows that open-source models now account for the majority of new foundation model releases.
But proprietary platforms still dominate commercial deployments.
OpenAI alone processed over 10 billion ChatGPT messages per week as of early 2024 and holds an estimated 60% share of the enterprise LLM API market.
Enterprise security teams now have a choice to make between OpenAI’s battle-tested closed-source models and more experimental open-source LLMs that promise finer data control, elimination of vendor premiums, and alignment with jurisdiction-specific requirements like the EU AI Act.
In this blog post, we are looking into the security architectures of OpenAI’s GPT models and open-source LLM deployments across four critical dimensions.
- Data flow and storage practices
- Access control mechanisms
- Compliance certifications and frameworks
- Total cost of maintaining security
What are open-source vs closed-source LLMs
Closed-source models (e.g., OpenAI)
Closed-source LLMs are proprietary large language models whose weights, training data, and internal architectures are not publicly released. They’re typically accessible only through paid APIs or licensed deployments controlled by the provider.
Most enterprises start with closed-source models for a practical reason: they work immediately. No infrastructure setup, no model hosting, no security configuration, just an API key.
The trade-off is less control over customization, data residency, and costs, but for organizations testing AI capabilities or building initial prototypes, that trade-off often makes sense.
General-purpose closed LLMs like GPT and Claude have robust guardrails against data bias, and their datasets are filtered not to contain content from unverified sources.
The practical advantage: closed-source models are already fine-tuned for general use. You can start building applications immediately without collecting training data, setting up GPU infrastructure, or running fine-tuning jobs. For organizations without dedicated ML teams, this eliminates months of preparatory work.
Closed, off-the-shelf LLMs are high quality. They’re often far more accessible to the average developer.
Eddie Aftandillan, Principal Researcher, GitHub
At the time of writing, these are the key players in the closed-source LLM market.
Major closed-source models compared
| OpenAI GPT-4.1 | OpenAI | Not public (estimated hundreds of billions) | Up to ~128K tokens (via API, varies by tier) | Fully proprietary SaaS via API and ChatGPT Pay-per-token and enterprise contracts | Yes – strong across major world languages | General-purpose enterprise AI (chat, coding, RAG, agents) where you want top-tier quality, tools, and ecosystem over full control. |
| Anthropic Claude 3.5 Sonnet | Anthropic | Not public | Up to 200K+ tokens (depending on deployment) | Proprietary API and console, with enterprise/managed-tenant options | Yes Particularly strong in English, solid global coverage | Long-context analysis (docs, codebases), research, and safer assistant use cases with strong alignment and UX. |
| Google Gemini 1.5 Pro | Not public | Up to 1M tokens (very long context) in some tiers | Proprietary via Google AI Studio, Vertex AI, and Workspace integrations; pay-per-use | Yes. Strong multilingual and multimodal support | Multimodal and ultra-long-context scenarios (whole repos, videos, docs) inside Google Cloud/Workspace ecosystems. | |
| Microsoft Copilot (M365 layer) | Microsoft (backed by OpenAI models) | Uses OpenAI foundation models; exact params not disclosed | Typically up to ~16K–32K tokens per call inside Copilot experiences | Licensed per seat (M365 Copilot SKU), deeply integrated into Microsoft 365 apps | Yes (depends on underlying model) | Knowledge work inside Microsoft 365 (email, docs, slides, Excel) where tight integration beats raw model control |
| Cohere Command R+ | Cohere | Not public | Up to ~128K tokens (long-context tuned) | Proprietary API, with on-VPC and private deployment options | Yes – good business-domain multilingual support | Enterprise RAG, search, and internal copilots where data residency, VPC hosting, and legal terms are crucial. |
| Palmyra-X / Jamba-Instruct | AI21 Labs | Not public (Mixture-of-Experts for Jamba) | 256K+ tokens (for Jamba variants) | Proprietary API and some managed/VPC options | Yes. Strong English, broader support evolving | Long-context document and code understanding, especially for enterprises wanting MoE efficiency and custom contracts. |
Open-source models
Open-source models are AI models whose weights, architecture, and training code are publicly released. This gives engineering teams a clear understanding of how the model is structured and trained.
No API required, no per-token charges, no vendor controlling access.
Early open-source models like Llama appealed mainly to machine learning engineers who wanted a deeper understanding of the technology. Closed-source APIs don’t let you modify attention mechanisms, adjust training objectives, or understand why the model produces specific outputs.
When you’re doing research, you want access to the source code so you can fine-tune some of the pieces of the algorithm itself. With closed models, it’s harder to do that.
Alireza Goudarzi, Senior ML Researcher, GitHub
In 2025, open-source models are gaining traction in enterprise as well. Banks use them to keep sensitive financial data on-premises. Healthcare systems use them to meet HIPAA requirements. Government agencies use them to comply with data sovereignty rules. The common thread: these organizations can’t send their data to external APIs, even with contractual guarantees.
Open-source models require real infrastructure work. Organizations require GPU clusters for inference, MLOps pipelines for deployment, monitoring systems for performance tracking, and ML engineers who are skilled in fine-tuning models and debugging issues.
Major open-source models compared
| Llama 3.1-70B | Meta | 70B dense | 128K tokens | Llama 3.1 Community License (source-available, commercial use allowed with some limits) | Yes – 8 major languages (incl. English, Italian, Spanish, Hindi, etc.) | General-purpose chat, coding, long-context RAG where you’re OK with Meta’s custom license. |
| Mixtral 8x7B | Mistral AI | ~46.7B total, ~13B active (MoE) | 32K tokens | Apache-2.0 (very permissive) | Strong multilingual performance | High-throughput, cost-efficient inference (MoE), great for RAG and agent backends when you want a truly OSS license. |
| Qwen2-72B | Alibaba (Qwen) | 72B dense | 128K–131K tokens (official support up to 128K+) | Qwen License (source-available, commercial with conditions) | Yes – trained on 29+ languages, strong in English & Chinese | Multilingual and code-heavy workloads where Chinese and English and very long context matter. |
| Gemma 2-27B | 27B dense | 8,192 tokens | Gemma License (open weights, Google custom terms) | Primarily English (good multilingual understanding) | Smaller infra footprint vs 70B+ models, strong general performance at “mid-size” for on-prem or edge-ish deployments. | |
| DBRX | Databricks | 132B total, 36B active (MoE) | 32K tokens | Databricks Open Model License (Llama-style source-available) | Yes – multilingual text and code | High-end enterprise workloads on Databricks or Kubernetes where you want a very strong open-weight model tuned for code and reasoning. |
| DeepSeek-V2 / V2.5 | DeepSeek | 236B total, 21B active (MoE) for V2 | 128K tokens | DeepSeek License (open-weight, with “responsible use” restrictions) | Strong bilingual Chinese/English and coding | Long-context reasoning and code for teams comfortable with a Chinese open-weight stack and custom license. |
There’s been an ongoing debate among machine learning engineers as to which group of models is more reliable and secure at the enterprise level.
To offer engineering team leaders a clear decision-making framework, we will compare OpenAI’s security practices to a broader host of open-source models.
This post is based on the market state as of November 2025 and may require independent fact-checking.
Data flow and storage
OpenAI
At OpenAI, data management practices are product-level rather than model-level.
Depending on the plan GPT users choose, they will fall under different data retention policies that will apply to all models the company is currently maintaining.
At the moment, OpenAI offers three tiers.
- Individual use: ChatGPT Free and Plus
- SMB and mid-market tier: ChatGPT Team and Business
- Enterprise-grade stacks: ChatGPT Enterprise and Edu
Each tier handles data differently:
| Individual: Free/Plus | - User prompts, files, and model outputs go to OpenAI’s consumer ChatGPT stack. - If a user uses GPT agents, some data is sent to external sites or APIs under their own privacy policies - User records are retained indefinitely | - User chats are used to train models (after de-identification and filtering) unless a user opts out - Temporary chats are never used for training and are deleted from platform logs within 30 days |
| SMB and mid-market: Team and Business | Same infrastructure as ChatGPT, but in a dedicated workspace. Users can enable internal or third-party connectors and set up app-level permissions and network lockdown for those connectors | By default, OpenAI does not train models on Business and Team data (inputs or outputs) |
| Enterprise | The data flow is similar to Business, offers more granular access control, analytics, Compliance API, and data residency options. | There’s no training on internal data unless users explicitly opt in |
Data flow via the OpenAI API
When a user sends API calls to OpenAI’s platform, all inputs and outputs are encrypted in transit. Normally, OpenAI stores this data for up to 30 days to support service delivery and detect abuse.
To prevent data retention, enterprise teams can request Zero Data Retention (ZDR) for eligible endpoints – this will prevent OpenAI from storing user data at rest.
For EU-region API projects, zero data retention is enabled by default with in-region processing.
OpenAI gives teams full ownership of training data and fine-tuned custom GPT models.
They’re never shared with other customers or used to train other models, and files are kept only until you delete them. Importantly,
OpenAI does not use API business data for training unless teams explicitly opt in through dashboard feedback.
Open-source models
Open-source deployments give you complete control over data flow, but that control comes with infrastructure responsibility.
Unlike OpenAI’s managed tiers, you decide where data lives, how long it’s retained, and whether it’s used for any purpose beyond inference. The data handling specifics depend entirely on the deployment architecture.
Self-hosted in a user’s environment
Deploy models on your own hardware using inference frameworks like vLLM, TGI, or Ollama.
Data flow: Prompts never leave your infrastructure. There is control of the entire stack: application, GPU inference, and storage. Configure retention policies as needed: 90 days, one year, forever, or immediate deletion.
Use case: Regulated industries (healthcare, finance, defense) maintaining data sovereignty. Meta recommends self-hosting Llama when external APIs violate compliance requirements.
Trade-off: You’re responsible for security hardening, access controls, encryption, backups, and incident response. Requires dedicated infrastructure and security teams.
Managed open-source model services
Cloud providers like AWS Bedrock, Google Vertex AI, and Azure AI Foundry, along with independent platforms like Hugging Face and Together AI, offer open-source model hosting as a service.
Using managed services gives enterprise teams the flexibility of open-source models without the stress of managing the infrastructure.
A downside to consider is that your team’s data will run through the vendor’s platform and is tied to the provider’s security controls. Data retention policies will also depend on the infrastructure provider.
On-device or edge open-source models
Smaller versions of models like Llama, Mistral, Phi, or Gemma run directly on laptops or mobile devices.
This is useful for internal tools or field scenarios where a team needs AI capabilities without internet connectivity, like predictive maintenance in remote oil rigs.
When to choose GPT
Choose GPT platform when you need enterprise-grade security with minimal infrastructure overhead and can accept data flowing through OpenAI’s managed stack.
ChatGPT Enterprise and API endpoints with Zero Data Retention (ZDR) ensure prompts and outputs don’t persist at rest and offer configurable data residency across 10+ regions and no training on customer data by default.
When to choose open-source models
Choose open-source models when your data governance demands complete control over data flow and you cannot accept external data transit. Self-hosted deployments using vLLM, TGI, or Ollama keep all prompts and outputs entirely within your security perimeter.
Access controls
Enterprise teams want granular control over how employees access their LLMs and the types of permissions teams have.
- Identity and authentication: Methods for verifying user identity and platform access, ranging from email/password, multi-factor authentication, to more sophisticated tools like single sign-on (SSO), and domain verification.
- Role-based access controls (RBAC): Controls for defining an organizational structure and permission levels that determine what different types of users can access and manage within the platform.
- Audit and admin APIs: Tools and programmatic interfaces for monitoring user activity, managing the organization, and exporting compliance data.
Here’s how OpenAI and open-source models perform in these dimensions.
OpenAI
OpenAI’s access control system offers several enterprise-grade benefits that make it practical for large organizations.
The RBAC (Role-Based Access Control) goes beyond administrative settings and governs end-user capabilities: running agents, apps, connectors, web search, and code execution. When combined with system cross-domain identity management (SCIM) groups, it scales effectively across different departments with varying security profiles.
Connectors and company knowledge base security: permissions can be disabled for specific user groups or require explicit admin approval.
On the API side, projects offer clear isolation boundaries with service accounts and per-endpoint key permissions that enable least-privilege access patterns.
The system integrates well with existing security infrastructure via compliance APIs and support for tools like Purview and CrowdStrike. These connections let organizations incorporate ChatGPT activity into their established security information and event management (SIEM) and data governance workflows instead of building new monitoring systems.
The table below summarizes access control features for all ChatGPT tiers.
| Identity and authentication | - Email / OAuth login - Optional MFA - No SSO - No domain verification | - Domain verification - SSO (SAML/OIDC) - Users tied to a business workspace; no SCIM | - Domain verification - SSO - SCIM for automated provisioning - IP allowlisting |
| Roles and workspaces | Single personal workspace No org roles | Workspace roles Owner / Admin / Member; control billing and workspace settings No fine-grained tool RBAC | Workspace and member RBAC: Owner/Admin/Member Custom roles: organizations can limit access to apps, connectors, agents, web search, and tools per group |
| Audit and Admin APIs | No admin console, no audit export | - Basic admin UI for user management and billing - No Compliance API - No security-grade audit feed | - Full admin console and analytics dashboard - Compliance API for exporting conversations and GPT activity to SIEM/DLP - Richer admin APIs for org/project management (API side) |
Open-source models
For open-source models, access control is independent of the model provider and depends on the infrastructure where the team hosts the LLM.
The inference engines like vLLM, Text Generation Inference (TGI), and Ollama focus exclusively on model serving and optimization and deliberately omit authentication and authorization features to maintain simplicity and flexibility.
Instead, production architectures rely on enterprise-grade infrastructure components.
- API gateways (Nginx, Traefik) sit between the LLM application and external APIs to enforce policies like rate limiting, authentication, request routing, and logging for all API traffic.
- Service meshes (Istio, Linkerd) provide a dedicated infrastructure layer that manages service-to-service communication within the microservices architecture, handles traffic management, security (mTLS), and observability.
- Reverse proxies receive client requests and forward them to backend servers. They enable load balancing, SSL termination, caching, and an extra security layer that hides the backend infrastructure.
This separation of concerns allows organizations to integrate their existing identity providers (Okta, Azure Entra ID, Keycloak) and security policies uniformly across all services, while the inference engines remain stateless and focused on maximizing throughput and minimizing latency.
Security considerations for hosting architecture
| Identity and authentication | - Leverages the company’s existing identity provider(Entra ID, Okta, etc.) in front of an internal API - Can be mutual TLS, OAuth, mTLS gateways, API gateways. - Allows to keep all access behind your SSO | - Tied to cloud identity and access controls (AWS IAM, Google IAM, Azure Entra) - Often integrates with enterprise SSO via federated login. |
| Role-based access controls | - Fully owned by the host - Teams can design granular permission connector controls - Highly flexible but requires careful design and setup | Most cloud providers offer robust RBAC and project scoping. Teams can grant fine-grained permissions like “team A can invoke this endpoint only” and separate prod vs dev by project. |
| Audit and admin APIs | - Teams can send calls via API gateways - Ability to define the audit schema and integrate with existing compliance tooling - Teams ned to build custom dashboards and alert systems. | Cloud providers like Amazon AWS, Google Cloud, and Microsoft Azure offer cloud audit logs for every API call Easy to plug into existing enterprise logging and compliance workflows, but there’s the risk of vendor lock-in. |
When to choose GPT
Choose OpenAI’s GPT platform when you need production-ready access controls without building infrastructure from scratch.
ChatGPT Enterprise provides SSO/SCIM integration, custom RBAC for limiting apps/connectors/agents per group, IP allowlisting, and Compliance APIs that export activity directly to your existing SIEM/DLP systems.
This eliminates the need to architect your own authentication layer, audit pipelines, or monitoring dashboards.
When to choose open source models
Choose open-source models when you need complete flexibility to design access controls around your existing security architecture or have complex requirements that vendor platforms don’t support.
Self-hosted deployments let you leverage your current identity provider (Okta, Entra ID) and implement custom RBAC through API gateways and service meshes, but the engineering team will have to build and maintain this entire layer.
Managed open-source services (AWS Bedrock, Azure AI Foundry, Google Vertex AI) offer a middle ground with cloud-native IAM, federated SSO, and built-in audit logs. However, this creates dependency on the provider’s specific RBAC model and logging formats.
Compliance controls
OpenAI
ChatGPT (Free, Plus, and Pro tiers) operates under consumer-grade privacy policies and terms of use rather than enterprise compliance frameworks. They lack the SOC 2 certification, Data Processing Agreements (DPAs), Business Associate Agreements (BAAs), and compliance APIs that enterprises require for auditing and governance.
OpenAI’s Business, Enterprise, and Education plans share common compliance foundations designed to meet regulatory requirements across industries and regions. All three tiers are covered by OpenAI Business Terms and a Data Processing Agreement (DPA) available upon request, with no customer data for training by default.
User data is encrypted both at rest using a highly secure AES-256 encryption standard and in transit via TLS 1.2, a cryptographic protocol that provides secure communication over a network.
Enterprise products hold SOC 2 Type 2 certification along with CSA STAR and multiple ISO/IEC standards (27001, 27017, 27018, 27701). OpenAI is positioning itself as a processor that helps customers meet their own GDPR, CCPA, and global privacy obligations.
Organizations with data residency requirements can store customer content at rest in specific regions, including the US, EU, UK, Japan, Canada, South Korea, Singapore, Australia, India, and the UAE.
| Free | Not covered by business SOC 2 / ISO scope (consumer service) | Consumer Terms and Privacy Policy only (no DPA, no BAA) | Yes, by default (unless user opts out or uses Temporary Chats) | Standard global infra, no enterprise residency or configurable retention | None. No admin console, no Compliance API, no audit export |
| Plus | Same as Free (consumer) | Same as Free (no DPA/BAA) | Same as Free (opt-out possible in personal settings) | Same as Free | Same as Free |
| Pro | Same as Plus (still a personal/consumer-style tier) | Same as Plus | Same as Plus | Same as Plus | Same as Plus |
| Business (ex-Team) | Covered by OpenAI business certifications (SOC 2, ISO 27k family, CSA STAR) | Business Terms and DPA available; no BAA | No training on business data by default | Data encrypted at rest/in transit. Eligible customers can choose region for data at rest, but limited knobs vs Enterprise; 30-day log norm | Basic admin UI and usage views only, no Compliance API, no Purview/CrowdStrike integration |
| Enterprise | Same business certs (SOC 2 Type 2, ISO 27001/17/18/27701, CSA STAR) | Business Terms and on-demand DPA; sector use can layer extra contract terms | No training on business data by default (opt-in only) | Data residency in multiple regions, admin-configurable retention for workspace data, encryption and EKM support | Compliance API, User Analytics, integrations with Microsoft Purview, CrowdStrike, etc. – full audit/export for eDiscovery, DLP, SIEM |
Open-source models
Compliance for open-source LLM deployments works differently because organizations control the infrastructure. The regulatory profile depends on three layers.
Layer #1. Model and license
Open-source model licenses like Apache, MIT, or custom community licenses govern how teams can use and redistribute the model itself, but do not cover privacy and data protection requirements.
Under the EU AI Act, providers of general-purpose foundation models must maintain technical documentation and training data summaries, with partial exemptions available for open-source models unless they pose systemic risk.
Whereas the teams behind closed-source models take responsibility for having such documentation, engineering teams using open-source models will have to keep regulator-facing records internally.
Layer #2. Deployment environment
The compliance landscape for open-source models depends on where and how you deploy them.
Self-hosted: on own infrastructure, control the entire data flow, including storage, regional processing, logging, and retention policies end-to-end.
Managed hosting (e.g., Bedrock, Vertex, Azure, Hugging Face): compliance resembles any other SaaS product.
The engineering team will rely on the provider’s SOC/ISO certifications and their Data Processing Agreement rather than controlling everything internally.
Layer #3. AI governance framework
Enterprises are increasingly mapping their AI controls to established frameworks like NIST’s AI Risk Management Framework (AI RMF), explicitly designed to be model- and provider-agnostic.
These standards apply equally to proprietary or open-source models.
The international standard ISO/IEC 42001:2023 defines requirements for an AI Management System (AIMS) that any organization, including those deploying open-source models, can adopt to manage AI risks, ethics, and regulatory obligations across their entire AI portfolio.
| Data location and residency | You choose where to run the model (on-prem, specific cloud region, air-gapped, etc.). |
| Security controls (ISO 27001 / SOC 2 context) | Security comes from your infra (IAM, network, encryption, patching), not the model. |
| Privacy and GDPR | You are the primary controller; you design “privacy by design” around the LLM stack. |
| EU AI Act and other AI-specific rules | Obligations fall on both model providers (if you release models) and deployers (you). |
| Documentation and governance | Model cards and repo docs are a starting point; the rest is your internal AI governance. |
| Contracts (DPA / BAA, etc.) | Self-host: mainly DPAs with cloud/infra providers. Hosted OSS APIs: DPAs with each host. |
| Auditability and logging | You decide what to log and send to SIEM; there’s no opaque vendor telemetry. |
When to choose GPT
Choose OpenAI’s GPT platform when you need immediate compliance coverage through vendor certifications and minimal internal governance overhead.
ChatGPT Business and Enterprise come with SOC 2 Type 2, ISO 27001, CSA STAR certifications, pre-negotiated DPAs, configurable data residency across 10+ regions, and Compliance APIs that integrate with third-party systems for audit exports.
GPT is an optimal choice for teams that can rely on OpenAI as a data processor under GDPR/CCPA and prefer offloading certification burden to the vendor rather than auditing their infrastructure.
When to choose open-source models
Choose open-source models when you need complete control over compliance or have to satisfy requirements that vendor platforms cannot meet.
Self-hosted deployments let you own the entire data flow, choose exact geographic locations, design privacy-by-design architectures, and align with vendor-neutral frameworks like NIST AI RMF and ISO 42001 across your entire AI portfolio.
However, you become responsible for maintaining certifications (SOC 2, ISO 27001) for your own environment, producing EU AI Act documentation if you release models, and building internal governance systems.
Costs of maintaining security
OpenAI
The true security TCO goes beyond ChatGPT enterprise subscription costs since teams have to pay extra for infrastructure.
Even with built-in SSO and RBAC, organizations must budget for their identity and access management stack, which typically includes the following types of tools:
- IdP licenses: Okta, Entra, Google Workspace
- SCIM provisioning tiers
- MFA solutions
- Conditional access policies that enforce context-aware authentication.
- VPN infrastructure
- Private endpoints
These additional security upgrades carry incremental per-user license costs and ongoing security engineering effort to design, implement, and maintain access policies.
GPT security TCO
| GPT plan | ChatGPT Business / Enterprise seats, OpenAI or Azure OpenAI API usage | Per-user (Business/Enterprise) ns per-token (API). Enterprise tier usually required to unlock serious security/compliance controls. |
| Identity and access | - IdP / SSO / SCIM (Okta, Entra, Google) - MFA, conditional access - IP allowlists | - Per-user SaaS licences - Security engineer time to design and maintain policies. |
| Network security | - VPN / ZTNA / private endpoints - Egress controls - Firewall rules around GPT endpoints | - Mix of per-user (ZTNA) and infra costs - Ongoing ops to keep routes, rules, and private links safe. |
| DLP / CASB / AI posture | - Data loss prevention - SaaS security brokers - AI/SPM tools watching GPT traffic and connectors | Per-user or per-GB licences; |
| Logging and SIEM | - Ingesting GPT/Compliance API - Logs into SIEM (Splunk, Datadog, Elastic) - Alerting - Incident response | - Charged by data volume - Analyst time to tune rules and handle incidents. |
| Governance and compliance | - DPIAs - Policy work - Legal review of DPAs/BAAs - Mapping to GDPR/AI Act - Internal AI risk committees | Primarily internal legal/compliance/security headcount and external counsel as needed. |
Open-source models
From an enterprise perspective, open-source LLM deployments eliminate the vendor security premium inherent in commercial platforms. Instead of paying per-seat or per-token uplifts to unlock compliance features, organizations allocate budget directly to infrastructure and controls.
Companies can cut security TCO by leveraging existing investments in IAM, SIEM, DLP, and private networking across the AI stack. This results in fine-grained control over data residency and risk posture, zero-log inference for sensitive workloads, jurisdiction-specific data segmentation, and differentiated security tiers between development and production environments.
However, this control comes with upfront engineering and operational costs that organizations often underestimate.
Standing up a secure, resilient LLM platform requires extra investment in infrastructure provisioning, access control, observability tooling, and governance frameworks.
Organizations must also shoulder their own certification. Achieving SOC 2, ISO 27001, or ISO 42001 coverage for self-hosted AI infrastructure requires auditing internal environments instead of relying on vendor attestation reports.
Besides, the flexibility of open-source deployments paradoxically increases compliance risk for teams that under-engineer their implementations. Without vendor-imposed guardrails, it becomes easier to over-log sensitive prompts, maintain unencrypted backups across multiple locations, or accidentally expose internal endpoints and risk GDPR and EU AI Act penalties.
Key security TCO considerations for open-source LLMs
| Compute and infra | - GPU/CPU clusters - Storage - Networking - Model-serving stack | Biggest hard cost: node hours, storage, HA Extra costs: ops time for patching and hardening. |
| Platform and access controls | - API gateway/mesh - AuthN/Z - RBAC, secrets/KMS - TLS/mTLS | Engineering time to design and maintain policies as code Reuses existing IAM but needs extra customization. |
| Network and perimeter | VPC design, segmentation, private endpoints, firewalls, WAF | Infra and ops costs to keep LLM endpoints isolated and safely exposed to apps only. |
| Logging, SIEM, and monitoring | - Designing logs - Pushing to SIEM - Detections for misuse/exfil | SIEM ingestion fees Engineer time to build AI-specific rules and dashboards. |
| DLP and data governance | - Classifying data - DLP on prompts/RAG - Model/data catalogs | - Licences for DLP/governance tools (if used) - Integration and ongoing tuning effort |
| Model lifecycle and supply chain | - Model registry - Fine-tune governance - Vulnerability scanning | - Tooling (can be OSS or commercial) - Process overhead for approvals, reviews, promotion. |
| Compliance and governance | - DPIAs, NIST AI RMF / ISO 42001 alignment - AI Act readiness | - Internal legal/compliance/security time - Possible external audits/certifications |
When to choose GPT
Choose OpenAI’s GPT platform when you want to minimize upfront engineering costs and leverage vendor-provided security infrastructure.
While you’ll still pay for identity stack components, network security, and DLP/SIEM integration, the core compliance controls come bundled in Enterprise subscriptions with vendor-maintained certifications.
When to choose open-source models
Choose open-source models when you have existing security infrastructure investments to leverage and want to avoid vendor premiums, but be prepared for significant upfront and ongoing costs. This makes economic sense when you have strong security and compliance teams already in place.
Bottom line
The choice between OpenAI’s GPT and open-source models fundamentally depends on your organization’s security maturity, resource capacity, and control requirements.
Choose GPT when you need enterprise-grade security with minimal engineering overhead. The combination of vendor certifications, pre-built compliance controls, and managed infrastructure enables fast deployment while relying on OpenAI’s attestations and DPAs to satisfy regulatory requirements.
Choose open-source models when you require complete control over data flow, have existing security infrastructure to leverage, or face compliance constraints that vendor platforms cannot accommodate.
The trade-off is responsibility for the full lifecycle of platform engineering, internal audits, and ongoing operational costs that organizations frequently underestimate.
Before committing to either approach, conduct an honest assessment of your security posture, engineering capacity, and compliance obligations. Evaluate whether your team has the expertise to architect secure LLM infrastructure, maintain certifications, and design governance frameworks, or whether vendor-provided controls better align with your capabilities and risk tolerance.
The “right” choice will be the one that matches your organization’s security needs, available resources, and strategic priorities while avoiding both vendor lock-in risks and the compliance failures that come from under-engineered self-hosted deployments.
