Originally published on CoreProse KB-incidents
From demo to production: the real enterprise LLM problem
The main issue is no longer whether to use LLMs, but how to turn demos into governed, resilient systems. By 2026, most large French enterprises and CAC 40 companies run at least one LLM in production, but under a third have a formal AI strategy and governance framework.[4][6]
The gap shows up as:[2][5][6][7]
- Unstable apps and surprise invoices
- Sensitive data flowing through third‑party APIs without DPAs
- Conflicts between innovation teams and CISOs / regulators
Common “demo gone wrong” patterns include:[2][7]
- Loops that trigger thousands of LLM calls and large, unexpected bills
- Provider outages or rate limits with no fallback model
- No logging of prompts/contexts, making failures hard to debug
- Shadow AI tools adopted by business teams without security review
LLMOps emerged to address these issues. It adds prompt and context management, model routing, cost control, and human‑in‑the‑loop feedback to classic MLOps deployment and monitoring.[3] LLMs also bring constraints (context windows, tool‑using agents, multi‑model portfolios) that legacy stacks do not handle well.[3]
Why enterprise LLM partners matter
Specialized LLM development companies are typically hired to deliver:[2][3][4][6][7]
- Reference architectures (public API vs sovereign vs on‑prem vs custom models)
- A shared gateway / LLMOps layer for routing, observability, and rollback
- Governance and compliance frameworks aligned with GDPR, EU AI Act, NIS2
The rest of this article covers architecture choices, LLMOps and gateways, security and governance, the people and roles involved, and how partners help scale from one use case to a portfolio.
Architecture choices: API, on‑prem, or custom enterprise LLMs
Choosing between API providers, on‑prem, and custom models
Most enterprises start with LLM APIs. Native providers that train and serve their own models usually offer:[9]
- Strong model quality and tooling
- Mature SDKs and integrations
- Fast path from idea to first app
Routing platforms and cloud marketplaces then expose multiple models and providers so enterprises can balance cost, latency, and reliability.[9]
For regulated sectors (healthcare, finance, defense), sending raw data to external APIs is often unacceptable.[5][10] On‑prem and sovereign platforms now allow models like Llama or Mistral to run inside corporate or in‑region infrastructure, with optimized latency and throughput suitable for interactive assistants.[10]
Architecture spectrum
- Public API LLMs – quickest start, limited control over data residency[9]
- Sovereign / private cloud – in‑region hosting, stronger data and access controls[4][6]
- On‑prem LLMs – full control over network boundaries and security posture[10]
- Custom models – adapted or pre‑trained on proprietary data under strict governance[1]
Custom, domain‑specific models
For high‑risk, high‑value use cases (credit, medical decisions, industrial control), enterprises co‑develop domain-specific models with partners such as Mistral.[1] These projects:[1][10]
- Fine‑tune or pre‑train on proprietary corpora
- Enforce strict data isolation and auditability
- Deploy on‑prem, sovereign cloud, or even on‑device depending on regulation and latency
Customization options exist on a continuum:[1][3]
- Prompting only – system prompts + few‑shot examples over general models
- Instruction tuning / adapters (LoRA, QLoRA) – light behavior adaptation
- Task‑specific fine‑tuning – domain corpora (e.g., contracts, clinical notes)
- Full pre‑training – rare; for deeply specialized or sovereign needs
Enterprise LLM companies usually start with prompting and RAG, and only escalate to fine‑tuning when metrics or compliance requirements justify the added complexity.[1][3]
Regulatory and sovereignty drivers
In Europe, regulation and sovereignty decisively shape architecture. The EU AI Act classifies many LLM‑powered systems in finance, healthcare, and critical infrastructure as high‑risk, requiring controls and conformity assessments.[4][6] GDPR and NIS2 add obligations around data residency, access, and incident response.[5][7]
That leads to patterns such as:[4][5][6][7][10]
- EU‑only or national hosting for logs, embeddings, and training data
- Detailed audit trails for data provenance and inference behavior
- Preference for sovereign or on‑prem deployments in heavily regulated sectors
Reference multi‑model architecture
To reconcile flexibility, sovereignty, and cost, partners often implement a central gateway that routes to:[2][10]
- External APIs for low‑sensitivity tasks (generic summarization, code gen)
- On‑prem / sovereign models for HR, finance, and regulated workloads
- Fine‑tuned domain models for high‑value use cases (e.g., underwriting)[1][10]
High‑level routing pseudocode:
def route_request(req: LLMRequest):
meta = classify_request(req) # sensitivity, domain, latency_slo
if meta.sensitivity == "high":
model = "onprem-secure-llm"
elif meta.domain in ["risk", "medical"]:
model = "custom-domain-llm"
else:
model = "public-api-llm"
price = pricing_table[model]
if estimated_cost(req, price) > meta.budget:
model = fallback_cheaper_model(model)
return call_model(model, req)
This gateway‑centric design centralizes logging, policy enforcement, routing, and cost control while satisfying sovereignty constraints.[2][4][6]
LLMOps and AI gateways: making LLMs operable at scale
What is LLMOps in practice?
Once the architecture is in place, the challenge becomes scale and reliability. LLMOps extends MLOps to include:[3]
- Versioning of prompts, agents, and tools as first‑class artifacts
- Context assembly (RAG, tools, metadata) and context‑window budgeting
- Portfolio‑level inference management (cost, latency, rate limits)
- Continuous eval on business tasks and safety criteria
It preserves collaboration between data science, engineering, and IT, but centers LLM‑specific assets and workflows.[3]
AI gateways as the control plane
An AI gateway mediates between applications and LLM providers, acting as a control plane for:[2]
- Routing and load‑balancing across models and vendors
- Security, auth, and data redaction
- Observability and FinOps
Unlike generic API gateways, AI gateways understand tokens, context windows, and LLM‑specific failure modes.[2] Modern gateways and on‑prem platforms offer high throughput with low latency and detailed metrics, suitable for multi‑use‑case internal platforms.[2][10]
Core gateway capabilities[2][3]
- Centralized model routing and dynamic fallback
- Rate limiting and exponential backoff
- Prompt/response logging with PII and secret redaction
- Real‑time cost estimates for dashboards and alerts
- Feature flags and A/B testing for models and prompts
Observability and evaluation
Enterprise partners typically add a logging and monitoring layer that:[2][3][5][7]
- Captures prompts, context sources, model versions, and metadata
- Applies data classification and redaction policies
- Tracks latency, token usage, and error types per route and tenant
These logs feed monitoring and offline evaluation pipelines. Candidate models and prompts are scored on curated datasets before promotion, which is vital given non‑determinism and regulatory expectations on traceability.[3][6][7]
Example gateway skeleton:
def handle_request(http_req):
norm = normalize(http_req)
enforce_authz(norm.user, norm.scope)
# Safety filters
norm.prompt = redact_pii(norm.prompt)
if is_disallowed(norm.prompt):
return error_response("policy_violation")
# Model selection & retries
model = select_model(norm) # latency, cost, sensitivity
for attempt in range(3):
try:
resp = call_provider(model, norm)
break
except RateLimitError:
model = fallback_model(model)
backoff(attempt)
log_event(norm, resp, model)
return postprocess(resp)
LLMOps then wraps this with CI/CD, environment management, and rollback:[3]
LLMOps lifecycle checklist[3]
- Dev/stage/prod environments seeded with synthetic or masked data
- Git‑backed prompts, agents, and RAG pipelines with automated tests
- Canary deployments and safe rollback procedures
- Continuous offline evals on domain datasets and safety test suites
Security, governance, and compliance as first-class design constraints
LLM security as an end‑to‑end discipline
Security and governance span the full LLM stack: models, data, infra, and UX.[7] Classic controls (network segmentation, IAM, encryption) are necessary but do not fully address prompt injection, data poisoning, or model exfiltration.[7][8]
OWASP’s Top 10 for LLMs highlights risks such as:[7][8]
- Prompt injection and jailbreaks via user or retrieved content
- Training data poisoning in fine‑tuning or RAG sources
- Model or data exfiltration via misconfigured APIs or side channels
- Supply chain compromise in model weights, libraries, and vector DBs
Security fundamentals for enterprise LLMs
CISOs should first map where LLMs are used, what data they touch, and who accesses them.[5] This means:[5][7]
- End‑to‑end AI data‑flow diagrams (collection → storage → inference → logs)
- Reassessing authentication, authorization, and encryption at each step
For sensitive domains (finance, HR, medical), organizations must enforce:[5][6]
- Data classification and least‑privilege access
- Encryption in transit and at rest for prompts, embeddings, and logs
- Governance over employee AI usage (allowlisted use cases, rules for external APIs)
On the governance side, GDPR, the EU AI Act, and NIS2 require:[4][6][7]
- Traceability of outputs to models, prompts, and data sources
- Documentation of training data, fine‑tuning, and evaluations
- Incident response and resilience for critical sectors
Governance pillars for LLMs[4][6]
- Traceability – fine‑grained logs linking inputs, models, and outputs
- Auditability – evidence of datasets, tuning procedures, and test results
- Responsible use – policies on human oversight, fairness, and explanation
AI‑SPM and enterprise patterns
AI Security Posture Management (AI‑SPM) tools now:[7][8]
- Inventory AI assets (models, gateways, vector stores, agents)
- Detect misconfigurations and risky data flows
- Monitor for prompt injection, abuse patterns, and anomalous usage
Enterprise LLM companies embed security and governance via:[5][7][10]
- Segregated environments (dev/stage/prod, separate network zones)
- On‑prem or sovereign deployments for high‑risk workloads[4][10]
- Detailed, immutable audit logs of prompts, data sources, and decisions[6][7]
- AI‑specific incident response runbooks and playbooks
The outcome is an AI system that is both secure in practice and defensible to auditors and regulators.
People and collaboration: LLM developers, platform teams, and partners
The rise of the LLM developer
Delivering such systems requires specialized roles. An LLM developer is a software engineer focused on integrating LLMs into products beyond simple chat APIs.[11] They combine:[11]
- Backend engineering and orchestration
- Prompt and agent design
- RAG, chunking, and vector search strategies
- Tool integration with internal APIs and workflows
- Evaluation, guardrails, and performance optimization
They usually operate within LLMOps or platform teams alongside data scientists, DevOps, and IT.[3][11]
Anecdote: internal LLM platform team
A European bank set up a central LLM platform squad: two LLM developers, one data engineer, one security engineer, and a product owner. Within six months they delivered:[1][4][11]
- A secure AI gateway
- Three domain‑specific RAG assistants
- Internal evaluation tooling
They partnered with an external vendor for custom model work and training on regulatory topics.
Working with enterprise LLM partners
External LLM development companies complement internal teams by bringing:[1][4]
- Deep domain modeling expertise (risk, healthcare, manufacturing)
- Hardened playbooks for gateways, observability, and FinOps[2][3]
- Training and support to build internal AI Centers of Excellence (CoEs)[1][4]
To avoid friction, engineering, security, and compliance should agree early on shared principles so that speed does not undermine data protection or governance.[5][6]
Recommended organizational structures
Many enterprises formalize an AI CoE that:[1][4]
- Owns standards for model selection, RAG, and evaluation
- Maintains reference architectures and shared gateway APIs
- Coordinates with security and legal on regulatory updates
A simple RACI for LLM operations might be:
- Model updates – Responsible: LLM platform team; Accountable: Head of AI
- New tool approvals – Responsible: Security; Accountable: CISO
- Security incident monitoring – Responsible: SOC; Consulted: AI CoE
- Use case onboarding – Responsible: Product; Consulted: AI CoE & Legal
Clear ownership lets internal and external teams move quickly while maintaining security, compliance, and cost control.
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