September 25, 2025
|
5 min read
On-Prem AI Gateway: Unified LLM API Access
- Connect to OpenAI, Claude, Gemini, Groq, Mistral, and 250+ LLMs through one AI Gateway API
- Use the platform to support chat, completion, embedding, and reranking model types
- Orchestrate workloads across your on-prem GPUs and approved external endpoints with smart routing and fallbacks
- Policy-based governance, enforce rate limits, quotas, RBAC, and audit logs at the gateway level
On-Prem/Hybrid LLMOps: Model Serving & Inference
- Launch any open-source LLM via pre-tuned, production-ready pipelines in your on-prem or VPC/hybrid cluster
- Leverage industry-leading model servers like vLLM and SGLang for low-latency, high-throughput inference
- Leverage industry-leading model servers like vLLM and SGLang for low-latency, high-throughput inference
- Enable GPU autoscaling, auto shutdown, and intelligent resource provisioning across your LLMOps infrastructure
Why choose TrueFoundry for hybrid cloud AI?
Deliver high-performance AI infrastructure that optimizes itself - reducing cost, complexity, and manual intervention.
Data Sovereignty & safety
- 100% of tokens, files, and traces stay inside your DC/VPC — no vendor access.
- Per-tenant controls with strict residency compliance.
- 42% of enterprise architects now view independent storage as safer than primary clouds
Agentic Workflow Toolkit
- Compose multi-step agents with tools, prompts, and policies.
- Built-in evaluation and observability for trust + repeatability.
- Rapid iteration enables scaling to complex workflows.
Unified GPU Fleet Orchestration
- On-prem models deliver up to 90% latency savings vs. cloud runs.
- Single dashboard to manage racks, clusters, and edge nodes.
- Automated scheduling, autoscaling, and real-time monitoring.
Predictable & Reduced Cost
- Enterprises report 80–90% cost reductions by shifting workloads on-prem.
- Own hardware and cut egress fees for financial control.
- Dynamic routing to lowest-cost models within SLA.
Technical challenges teams face on-prem
The most common blockers we see—and how to move past them without burning months on glue work.
Challenge
Symptom
How TrueFoundry Fixes It
Observability across edge / on-prem / lab
We can’t see which model, pod, or node is the bottleneck; MTTR is days
One pane for traces/metrics/logs + request-level LLM observability; environment health roll-ups.
Fragmented GPU pools, poor utilization
Some nodes are idle while one queue is jammed; teams hoard GPUs.
GPU partitioning/slicing, quotas, and preemption; fair-share scheduling across teams.
Data governance & residency
We must keep PII/PHI in-house but still join datasets for AI.
Residency-aware pipelines, in-place training/inference, and masked feature stores.
Performance tuning & cost visibility
Latency SLOs vs. cost are a black box; small models sometimes beat big ones but routing is manual.
Policy-based routing (by latency/cost/accuracy), per-request cost traces, autoscaling profiles.
Heterogeneous estates (VMs, K8s, legacy)
We run VMs and containers across sites; ops is inconsistent and brittle
K8s-native control with VM+container harmony, standard golden images, drift detection.
Keeping up with model/tooling churn
Every month: new runtimes, formats, and accelerators; our stack lags behind.
Pluggable runtimes (OpenAI-compatible, vLLM, NIM, etc.), versioned blueprints, upgrade windows.
Financial Services
- Customer data never leaves the bank → easier SOC 2 audits
- Sub-10 ms inference → tighter bid/ask spreads
- Ring-fenced pipelines → zero data-leak headlines
Real-time fraud scoring
Score every transaction in milliseconds and quarantine anomalies before they clear
T-1 risk back-testing
Compress VaR runs to overnight so books close with fresher stress results.
Personalised wealth bots
Compliant, on-prem advisors that remember portfolio context, without leaking customer data.
Healthcare
- PHI stays on-site → HIPAA/GDPR peace of mind
- Instant model inference → faster diagnostics
- Full audit trail → smoother FDA submissions
Radiology image triage
Score scans in milliseconds next to PACS and auto-prioritise suspected criticals.
Drug-discovery fine-tuning
Fine-tune on de-identified trial data inside your firewall; IP and PHI never leave.
Hospital-bed demand forecasting
Local EHR/ADT feeds power daily bed-need forecasts and staffing alerts, no data export.
Automotive
- Customer data never leaves the bank → easier RBI/SOC 2 audits
- Sub-10 ms inference → tighter bid/ask spreads
- Ring-fenced pipelines → zero data-leak headlines
Driver-assist testing lab
Deterministically replay edge cases on an on-prem AV/HPC cluster and sweep model versions with safety-lifecycle traceability
Predictive maintenance
Fuse telemetry and service history locally to forecast wear and schedule fixes before failures.
In-plant robotics vision
Run inspection models at the far edge (cameras/robots) to catch defects in-line, no cloud dependency.
Semiconductors
- Yield slips from microscopic defects → inline AI inspection boosts first-pass yield
- Lab-only pilots & siloed EDA logs → one governed platform across design, test, and fab
- Tool downtime & scrap costs → predictive maintenance and SPC reduce excursions
Wafer & mask defect detection
CV+ML flags hot spots inline
Virtual metrology & SPC
Predict out-of-spec before it hits yield
EDA/log mining for D₀ ramp
Correlate design/test/fab signals to speed yield learning
Manufacturing
- Analyze production data without cloud latency
- Keep proprietary processes and IP secure on-site
- Deploy vision models for real-time quality control
Defect heat-map overlay
Pixel-level anomaly maps on live cameras to guide inspectors in real time.
Energy-use optimisation
Learn optimal setpoints and auto-adjust drives/ovens to trim kWh without hurting throughput.
Demand-driven scheduling
Pull live ERP/WMS signals to re-sequence jobs and reduce WIP bottlenecks.
Media & Telecom
- Terabytes of raw footage stay in-house → protect IP rights
- Real-time, on-prem render & edit → slash post-production time
- First-party viewer data processed locally → privacy-compliant personalization
Auto-editing
AI stitches multi-cam footage, Auto-sync angles, assemble a first cut, and generate captions, without raw media leaving your vault
Smart recommendations
Personalize without third-party cookies, Drive recs from first-party viewing behavior stored in your own infra; no external trackers
Secure asset vault
Rights management & watermarking, Centralized access control plus forensic watermarking to trace leaks across screeners and cut
Defense
- Air-gapped training clusters → meet DoD Top-Secret / SCI mandates
- Sub-20 ms inference at the tactical edge → faster decision cycles
- Immutable audit logs → pass DevSecOps & zero-trust reviews
Tactical model training
Update vision models in-theater
Real-time targeting support
On-device detection/labeling to aid situational awareness in low-connectivity settings.
Secure audit trail
Hash-chained/append-only logs with verifiable history for investigative and compliance needs.
Frequently asked questions
How should we choose between cloud‑based and on‑prem AI governance systems?
Use data sensitivity and control as your tiebreakers. If you need data sovereignty, PHI/PII control, custom guardrails, and predictable cost, on‑prem (or hybrid) governance is typically a better fit; the cloud shines for bursty experimentation. TrueFoundry outlines the trade‑offs and supports both approaches with a common governance layer (Gateway + guardrails + audit).
How to choose between on‑prem vs cloud AI finance solutions?
While MLOps supports a wide range of ML models, LLMOps is purpose-built for GenAI and
large language models. It includes capabilities like model server orchestration, prompt
management, token-level observability, agent frameworks, and secure API access.
TrueFoundry’s LLMOps platform handles these GenAI-specific workflows natively—unlike
generic MLOps tools.
large language models. It includes capabilities like model server orchestration, prompt
management, token-level observability, agent frameworks, and secure API access.
TrueFoundry’s LLMOps platform handles these GenAI-specific workflows natively—unlike
generic MLOps tools.
Is cloud or on‑prem edge AI security in data centers better—and when?
Managing LLMs at scale is complex. TrueFoundry’s LLMOps platform offers integrated tools for
model serving, fine-tuning, RAG, agent orchestration, observability, and governance—so your
team can focus on building instead of stitching infrastructure. It also supports enterprise needs
like compliance, quota management, and VPC deployments.
model serving, fine-tuning, RAG, agent orchestration, observability, and governance—so your
team can focus on building instead of stitching infrastructure. It also supports enterprise needs
like compliance, quota management, and VPC deployments.
How do self‑hosted LLM evaluation platforms usually store & secure prompt logs?
TrueFoundry’s platform includes:
Model Serving & Inference with vLLM, SGLang, autoscaling, and right-sized infra
Finetuning Workflows using LoRA/QLoRA with automated pipelines
API Gateway for unified access, RBAC, quotas, and fallback
Prompt Management with version control and A/B testing
Tracing & Guardrails for full visibility and safety
One-Click RAG Deployment with integrated VectorDBs
Agent Support for LangChain, CrewAI, AutoGen, and more
Enterprise Features like audit logs, VPC hosting, and SOC 2 compliance
I need a self‑hosted platform to log every LLM request with metadata—options?
Yes. TrueFoundry is designed for flexibility. You can deploy the LLMOps platform on your own
cloud (AWS, GCP, Azure), in a private VPC, on-premise, or even in air-gapped
environments—ensuring data control and compliance from day one.
cloud (AWS, GCP, Azure), in a private VPC, on-premise, or even in air-gapped
environments—ensuring data control and compliance from day one.
How do AI vendors manage infrastructure diversity across air‑gapped deployments?
TrueFoundry’s LLMOps stack offers token-level tracing, latency tracking, cost attribution, and
request-level logs. You can track every prompt, response, and error in real time, making it easy
to debug and optimize your LLM applications.
request-level logs. You can track every prompt, response, and error in real time, making it easy
to debug and optimize your LLM applications.
GenAI infra- simple, faster, cheaper
Trusted by 30+ enterprises and Fortune 500 companies
