The SMB Guide to Private LLM Deployment: Secure AI Architecture That Actually Works
Most SMBs don't need a massive AI infrastructure budget to run a private LLM. They need the right architecture, the right model, and a clear-eyed view of what each deployment option actually costs and protects.
- A private LLM keeps your data inside a controlled environment, whether self-hosted or VPC-isolated, so it never trains a shared model or crosses a public API boundary.
- VPC-isolated deployments on AWS, Azure, or GCP are the most common SMB choice for 2026 because they balance security, cost, and operational simplicity.
- Open-weights models like Llama 3, Mistral, and Qwen 2.5 are production-ready for most SMB use cases and eliminate per-token licensing fees from frontier providers.
- Frontier model APIs, even with no-training flags enabled, carry prompt-leakage risk that most regulated industries cannot accept under HIPAA, SOC 2 Type II, or GLBA.
- Total cost of ownership for a private LLM is not just GPU compute. It includes inference serving, monitoring, fine-tuning pipelines, and the engineering hours to keep it current.
- A well-designed private LLM deployment matches model size to task complexity, not to what sounds impressive in a board deck.
What 'Private LLM' Actually Means (And What It Doesn't)
A private LLM is any large language model deployment where inference happens inside an environment you control, where your data doesn't cross a public API boundary, and where no third-party model provider can access your inputs or outputs. That definition sounds simple. In practice, it gets blurry fast, because vendors use the word 'private' to mean at least four different things.
The first meaning is network-private: the model endpoint is not publicly accessible on the internet. The second is data-private: your prompts and completions are never logged by the model provider. The third is tenancy-private: the model weights and serving infrastructure are dedicated to your organization, not shared with other customers on the same hardware. The fourth is the strictest interpretation: the model itself runs in your own cloud account or on your own physical hardware, fully air-gapped from any provider telemetry.
For SMBs in regulated industries, the distinction between these four levels is not academic. A healthcare billing company running on OpenAI's API with the 'no training' flag enabled is operating in meaning two, but not meaning three or four. That company's prompts still transit OpenAI's infrastructure. If a prompt contains a patient name, a diagnosis code, or a claim amount, it has left the organization's boundary, even if OpenAI never stores it for training. Under HIPAA, that transit constitutes a disclosure unless a Business Associate Agreement is in place and the infrastructure meets the required safeguards. OpenAI has a BAA product, but it doesn't resolve the broader network-transit concern for organizations whose legal counsel reads the regulation strictly.
When we talk about private LLM deployment in this guide, we mean the third and fourth definitions: dedicated infrastructure, either in your own cloud account or on hardware you operate, where model provider telemetry is absent from the data path. This is the architecture that lets a HIPAA-covered entity, a SOC 2 Type II-scoped fintech, or a GLBA-regulated financial advisor use AI without treating every inference call as a potential audit finding.
The other thing 'private LLM' doesn't mean is 'custom model.' A private deployment can run a completely stock Llama 3.1 70B model with zero modifications. The privacy comes from where and how inference runs, not from whether the weights were fine-tuned on your data. Fine-tuning is a separate decision we cover in the model choice section. Conflating the two is one of the most common mistakes we see in SMB AI planning, and it leads to scoping AI projects at five times the complexity they actually require.
Deployment Options: Self-Hosted, VPC-Hosted, and Dedicated Tenant Compared
There are three practical deployment patterns for SMBs who need a private LLM. Each has a distinct risk profile, operational burden, and cost structure. Choosing the wrong one at the start of a project creates debt that's expensive to unwind.
Self-hosted on-premises means you own or lease the GPU hardware, you run the inference stack, and you manage every layer from firmware to the model serving API. This gives you the strongest possible data isolation. Nothing leaves your building. For organizations with an existing data center, strict sovereignty requirements, or specific government compliance mandates, this can be the right call. But for most SMBs, it's the wrong call. GPU hardware is expensive to procure, slow to scale, and requires skilled ML infrastructure engineers to keep running. A single NVIDIA H100 SXM5 costs roughly $30,000 to $35,000 on the open market. Running a 70-billion-parameter model in production at reasonable throughput requires at least two of them. The hardware alone is one thing. The engineers to maintain it, the power and cooling, and the disaster recovery planning are what actually break SMB budgets.
VPC-hosted deployment is the dominant pattern for SMBs in 2026, and for good reason. You deploy model weights into a Virtual Private Cloud on AWS, Azure, or GCP. The cloud provider owns the physical hardware and manages the hypervisor layer, but your VPC is logically isolated. Your data doesn't leave your cloud account. No model provider telemetry touches your inference traffic. On AWS, this typically means running an endpoint on Amazon SageMaker or a self-managed EC2 cluster using instances like the p4d or p5 family. On Azure, it means Azure Machine Learning managed endpoints or Azure Kubernetes Service with GPU node pools. On GCP, it means Vertex AI custom serving or GKE with A100 nodes.
This pattern gives you 90 percent of the security benefit of on-premises at a fraction of the capital cost. You pay for compute as you use it. You scale up for batch jobs and scale down overnight. You inherit the cloud provider's physical security, network controls, and compliance certifications. For a company that's already in AWS under a Business Associate Agreement for other workloads, adding a private LLM endpoint to that existing BAA scope is a straightforward compliance extension, not a new compliance program.
Dedicated tenant deployments are offered by a growing set of AI infrastructure providers: Fireworks AI, Together AI, and Anyscale all have dedicated cluster products where your inference traffic runs on hardware that's not shared with other customers. This sits between VPC-hosted and public API in terms of control. You don't manage the infrastructure, but you're not sharing a multi-tenant GPU pool either. For SMBs that don't want infrastructure operations at all but need stronger isolation than a standard API provides, dedicated tenant is worth evaluating. The tradeoff is that you're trusting a vendor's architecture claims rather than verifying isolation yourself, which matters during a SOC 2 Type II audit when your auditor asks about subprocessor controls.
Our recommendation for most SMBs: start with VPC-hosted on whichever cloud you're already operating in. The operational lift is manageable, the compliance story is clean, and the cost scales with actual usage rather than peak capacity bets.
Model Choice: Open-Weights, Fine-Tuned, and Frontier API Each Solve Different Problems
Model selection is where most SMB AI projects make their biggest early mistake. Teams default to frontier models like GPT-4o or Claude 3.5 Sonnet because those are the models they've tested in demos, and demos are optimized for impressiveness. Production is optimized for reliability, cost, and compliance. The optimal model for a demo is often a poor choice for a private deployment.
Open-weights models have matured dramatically. Llama 3.1 70B Instruct, Mistral Large 2, and Qwen 2.5 72B are production-ready for the vast majority of SMB use cases. We've deployed these models for document summarization, internal knowledge retrieval, intake form processing, and customer communication drafting at clients in healthcare, legal, and financial services. Quality is excellent for structured tasks. For open-ended creative generation or state-of-the-art reasoning, frontier models still lead. But most SMB workflows are not open-ended creative tasks. They're structured, repeatable, and domain-specific, which is exactly the territory where a well-served 70B open-weights model competes with frontier APIs on quality while winning decisively on cost, latency, and data control.
The 'open-weights' label means the model parameters are publicly available under a license. For Llama 3.1, that's Meta's community license, which permits commercial use for companies with under 700 million monthly active users. For Mistral models, it's an Apache 2.0 license with no significant commercial restrictions. For Qwen, it's Alibaba's Tongyi Qianwen license. Read the licenses before you ship. The licenses are not identical, and some have attribution or use-case restrictions that matter in regulated contexts.
Fine-tuning on top of an open-weights base model makes sense in three scenarios. First, when the task requires terminology or formatting that the base model handles inconsistently, such as specific ICD-10 code patterns or a company's internal escalation logic. Second, when you need to reduce inference costs by distilling a task that currently requires a 70B model down to a 7B or 13B model that handles 90 percent of the volume. Third, when latency requirements are strict and a smaller, specialized model serves requests faster than a large general-purpose one. Fine-tuning is not a substitute for retrieval-augmented generation for knowledge tasks, and it's not a way to 'teach' a model your data in the way many clients initially assume. It adjusts style, format, and behavior. It doesn't reliably inject factual knowledge.
Frontier model APIs, meaning OpenAI, Anthropic, Google Gemini, and similar, remain the right choice when you genuinely need state-of-the-art reasoning, multimodal inputs, or tool-use capabilities that open-weights models don't yet match. The critical point is what happens to your data when you use them. Even with data processing agreements in place, your prompts travel over the public internet to the provider's infrastructure. For certain regulated data types, that transit alone is a compliance problem regardless of the provider's contractual commitments. We've seen legal teams at healthcare clients block OpenAI integrations not because OpenAI's security posture is poor, but because the legal interpretation of 'disclosure' under HIPAA made any off-premises transit unacceptable for protected health information. If you're operating in those constraints, frontier APIs are simply off the table for PHI-adjacent workflows, regardless of quality.
Data Residency and Compliance Framework Alignment: What the Regulations Actually Require
Compliance requirements for AI systems are not yet fully codified in most frameworks, but enough guidance exists to make defensible architectural decisions. The mistake is waiting for perfect regulatory clarity. By the time regulations are explicit, you've either shipped insecure systems that need to be rebuilt, or you've delayed long enough that competitors have moved.
Under HIPAA, the Privacy Rule and Security Rule apply to any system that creates, receives, maintains, or transmits electronic protected health information. An LLM that processes clinical notes, insurance claims, appointment details, or any data element that could identify a patient is within HIPAA scope. The required safeguards include access controls, audit logging, encryption at rest and in transit, and a signed BAA with any business associate who touches the data. For a VPC-hosted LLM, your cloud provider BAA covers the infrastructure layer. Your inference serving software must implement access controls and audit logging. The model itself doesn't care about HIPAA. Your architecture does.
SOC 2 Type II compliance for AI systems is more complex because the framework is principles-based rather than prescriptive. The trust service criteria around availability, confidentiality, and security all touch AI systems. During a SOC 2 audit, your auditor will ask how you control access to the model endpoint, how you log inference requests, how you detect and respond to prompt injection attacks, and what subprocessor agreements govern any third-party AI dependencies. A private VPC deployment where you control the endpoint, the logs, and the subprocessor list is dramatically easier to defend in an audit than a patchwork of public APIs with different data processing terms.
GLBA, which governs financial institutions, requires safeguards for customer financial data. The FTC's updated Safeguards Rule explicitly requires covered entities to assess the risks posed by each third-party service provider and ensure adequate contractual protections. An LLM processing loan applications, account statements, or customer financial profiles is a safeguards program concern. The same logic applies: private deployment with explicit data handling controls is cleaner than a shared API with a data processing agreement.
Data residency adds another layer. The EU's GDPR and increasingly state-level US laws like the CCPA and state AI acts impose requirements about where personal data can be processed and stored. If your VPC is in us-east-1 and your customers are EU citizens, you need to assess whether processing their data in that region creates a GDPR transfer problem. Most cloud providers now offer region-specific deployment options. Choosing your VPC region deliberately, rather than defaulting to whichever region has the cheapest GPU availability, is part of the architecture decision.
Practically, what this means for deployment design: log every inference request with a truncated or hashed version of the input, not the full prompt, unless you have specific audit reasons to retain full prompts and the data handling agreements to support it. Implement role-based access controls on the model endpoint so that only authorized services can call it. Use encryption in transit with TLS 1.2 or higher. Store model weights in encrypted cloud storage. Run regular access reviews. None of these controls are novel. They're the same controls you apply to any sensitive data system, applied to the LLM endpoint.
Cost Modeling: What a Private LLM Actually Costs to Run at SMB Scale
The most common mistake in SMB AI budgeting is comparing the cost of a private LLM to the cost of a public API call and concluding that the API is cheaper. It often is cheaper at low volume. The comparison breaks down when you account for compliance overhead, data handling risk, and the cost of an incident that occurs because sensitive data transited a public API without adequate controls.
For a VPC-hosted deployment, the primary compute cost is the GPU instance running inference. On AWS, a p4d.24xlarge with eight A100 40GB GPUs runs approximately $32 per hour on-demand. A p3.8xlarge with four V100 GPUs runs around $12 per hour. For a 70B parameter model in FP16 precision, you need roughly 140GB of GPU memory, which means two A100 80GB GPUs or four A100 40GBs. A reserved instance for that capacity at one-year commitment drops the cost to roughly $15 to $20 per hour depending on region and reservation type. Running that instance 24 hours a day, 365 days a year comes to roughly $130,000 to $175,000 per year at full utilization.
Most SMBs do not need 24/7 GPU availability at full capacity. Batch workloads can run on spot instances at 60 to 70 percent discount. Interactive workloads can use auto-scaling that spins up capacity on demand and scales to zero at night. A realistic cost for an SMB running a 70B model for internal document processing, maybe 10,000 to 50,000 requests per day, is closer to $3,000 to $8,000 per month in GPU compute with sensible auto-scaling. That's comparable to what you'd pay for frontier API access at equivalent volume, without the data handling risk.
For smaller models, the economics improve significantly. A Mistral 7B or Llama 3.2 11B model serving simpler classification or extraction tasks can run on a single A10G GPU. AWS g5.xlarge instances with an A10G cost roughly $1 per hour. That's under $800 per month for a continuously running endpoint. For workflows where a smaller model is genuinely sufficient, private deployment can be cheaper than a public API.
Beyond compute, budget for: inference serving software, either vLLM, TGI (Text Generation Inference from Hugging Face), or Triton Inference Server, which are open-source but require engineering time to configure and maintain. Budget for monitoring, using something like Prometheus and Grafana, or a dedicated LLM observability tool like Langfuse or Helicone. Budget for model updates, because open-weights models release new versions every few months and staying current requires a testing and deployment pipeline. Budget for the engineering hours to build and maintain the integration between your applications and the model endpoint.
A rough total cost of ownership for a well-run private LLM deployment at an SMB with 50 to 500 employees: $5,000 to $15,000 per month in cloud compute and tooling, plus 0.25 to 0.5 full-time engineering equivalent for ongoing maintenance. That's the honest number. It's not trivial, but for an organization processing regulated data at meaningful volume, it's often less than the cost of a single compliance incident.
Latency and Quality Tradeoffs You Need to Understand Before You Deploy
Latency is where private deployments most often disappoint teams that built their expectations on frontier API demos. Understanding why requires a quick look at how inference actually works.
Language model inference is memory-bandwidth-bound, not compute-bound. The bottleneck is moving model weights from GPU memory into the GPU's compute cores, not the matrix multiplications themselves. A 70B parameter model in FP16 occupies 140GB of GPU memory. Every generated token requires reading a meaningful portion of those weights. On two A100 80GB GPUs, time to first token for a prompt of 1,000 tokens is typically 500 to 1,500 milliseconds, with generation speed of roughly 20 to 40 tokens per second depending on batch size and serving configuration.
For comparison, OpenAI's GPT-4o API returns first tokens in roughly 300 to 600 milliseconds under normal load, with generation speed of 50 to 100 tokens per second. The frontier API is faster in most scenarios, because OpenAI is running thousands of high-end GPUs optimized for throughput with dedicated infrastructure teams. That's a real quality-of-experience difference for interactive use cases.
The gap narrows considerably with smaller models. A Mistral 7B or Llama 3.2 3B model on a single A10G GPU returns first tokens in under 200 milliseconds and generates at 80 to 150 tokens per second. For high-frequency, short-output tasks like classification, entity extraction, or binary decision-making, smaller private models can actually be faster than frontier APIs, especially when API latency includes network round-trip time from your infrastructure to the provider's data center.
Quality tradeoffs are task-dependent. On standard reasoning benchmarks, Llama 3.1 70B scores within a few percentage points of GPT-4o on most structured tasks. On complex multi-step reasoning, code generation for novel architectures, and tasks requiring broad world knowledge updated after the model's training cutoff, frontier models lead. For the workflows we deploy in SMB contexts, such as document summarization, form processing, internal Q&A over a knowledge base, and communication drafting, a well-prompted 70B model is difficult to distinguish from a frontier model in blind evaluation.
The practical approach: identify your highest-volume, most latency-sensitive workflows and test a smaller open-weights model first. If quality meets your threshold, deploy it. Reserve the more expensive, higher-latency 70B model for complex reasoning tasks. Don't use a 70B model for tasks a 7B model handles correctly. The cost and latency savings compound quickly at SMB scale.
One more variable worth understanding: quantization. Running a 70B model in 4-bit quantization (GPTQ or AWQ format) cuts GPU memory requirements roughly in half, meaning you can serve it on hardware that would otherwise require a larger cluster. Quality loss from 4-bit quantization on most tasks is marginal, often below the threshold of human detection in A/B testing. For a cost-constrained SMB deployment, quantized models are not a compromise. They're a sensible engineering choice.
Reference Architecture for a Private LLM at SMB Scale
What follows is the architecture we use for most SMB private LLM deployments. It's not the only valid architecture, but it's the one that balances security, operational simplicity, and cost for organizations with one to three ML engineers and a regulated data environment.
The entry point is an API gateway layer. We use AWS API Gateway or an Nginx reverse proxy running in a private subnet, depending on whether the client is using AWS or a self-managed VPC. This layer handles authentication, rate limiting, and request logging. No request reaches the model endpoint without passing through this layer. The gateway logs request metadata: timestamp, authenticated identity, input token count, output token count, and a hashed or truncated version of the request ID. Full prompts are not logged at the gateway layer unless a specific audit requirement demands it.
Behind the gateway is the inference serving layer. We run vLLM as the inference server in most deployments because of its PagedAttention implementation, which dramatically improves throughput for concurrent requests compared to naive implementations. vLLM exposes an OpenAI-compatible API endpoint, which means most application code written against the OpenAI SDK works against a private vLLM endpoint with a one-line configuration change. That compatibility matters enormously for teams migrating from a public API to a private deployment.
The model weights live in an encrypted S3 bucket or Azure Blob Storage container. At startup, the inference server pulls weights from storage into GPU memory. This means instance startup time is longer than a stateless web service. A 70B model in FP16 takes five to ten minutes to load from S3 into GPU memory, which is why auto-scaling cold starts require planning. We typically keep one warm instance running continuously and auto-scale additional capacity for burst traffic.
For retrieval-augmented generation (RAG) workflows, we add a vector database layer. We use pgvector running in RDS PostgreSQL for most SMB deployments because the client already has a PostgreSQL database, and adding an extension is simpler than operating a dedicated vector store. For higher-throughput use cases, Qdrant or Weaviate running in the same VPC is the upgrade path. Documents are chunked and embedded using a separate, lighter embedding model, typically a quantized version of BGE-M3 or Nomic Embed Text, running on CPU or a small GPU instance. This keeps embedding costs separate from generation costs.
Observability runs through a Prometheus and Grafana stack or, for clients who want less operational burden, Langfuse deployed in the same VPC. Langfuse captures trace data, including prompt templates, retrieved context chunks, and model outputs, in a format that makes debugging and quality evaluation practical. For regulated industries, the Langfuse database is encrypted and access-controlled the same way as any other sensitive data store.
Network controls follow a minimal-exposure model. The inference endpoint is not accessible from the public internet. Only application services in designated security groups can call it. Egress from the inference instance is restricted to the model weights storage bucket and the monitoring endpoints. The model itself has no outbound internet access. This isn't just security theater: it prevents a class of prompt injection attacks where a malicious prompt instructs the model to exfiltrate data via an outbound HTTP call.
How SMBs Are Running This in Production
A regional home health agency with 200 clinicians was processing clinical visit notes using a third-party transcription service that fed summaries to an EHR system built on Epic. The transcription vendor had a BAA, but the agency's compliance officer wanted the summarization step, where the most sensitive clinical language was distilled, to happen inside the agency's own infrastructure. We deployed a quantized Llama 3.1 70B model in their existing AWS environment, which was already under a BAA with Amazon. The model receives transcribed text from the transcription vendor's output, generates a structured SOAP note summary, and posts it to Epic via the FHIR API. No PHI leaves the agency's AWS account during summarization. The agency reduced clinician documentation time by roughly 35 minutes per day per clinician, and the compliance officer signed off on the architecture without requiring external legal review.
A 60-person independent insurance agency processing commercial lines renewals was using a mix of ChatGPT and manual spreadsheet workflows to draft renewal summaries for account managers. The managing partner was uncomfortable with policy details and client financial information going through a public API, even with a data processing agreement. We deployed a Mistral Large 2 model on a dedicated VPC endpoint and built a lightweight intake tool that account managers use to paste coverage schedules and generate structured renewal narratives. The model runs on two A10G GPU instances with auto-scaling. Total infrastructure cost is under $4,000 per month. The agency recouped that cost in the first six weeks through reduced account manager hours on routine renewal prep.
A fintech company providing embedded lending infrastructure to credit unions was preparing for its first SOC 2 Type II audit. Their engineering team had built a customer-facing FAQ assistant using OpenAI's API with the zero-data-retention option enabled. During pre-audit assessment, the auditor flagged the OpenAI dependency as a subprocessor that required vendor security review documentation. Rather than build the documentation package for OpenAI, the team migrated the FAQ assistant to a Qwen 2.5 7B model running in their existing GCP project. The smaller model handled the FAQ use case with no measurable quality drop. The migration took three weeks of engineering time, and the model is now documented as an internal component in their SOC 2 vendor inventory rather than a third-party subprocessor, which simplified the audit considerably.
What we see in real deployments
We deployed a quantized Llama 3.1 70B model in the agency's existing AWS BAA environment to summarize clinical visit notes before posting structured SOAP summaries to Epic via FHIR. No PHI leaves the agency's AWS account during the summarization step. The compliance officer approved the architecture without external legal review.
We replaced a ChatGPT-based workflow with a Mistral Large 2 model on a private VPC endpoint, processing policy renewal data that the managing partner was unwilling to send through a public API. The auto-scaling deployment runs under $4,000 per month and cut account manager hours on routine renewal prep substantially.
A pre-audit flag on the team's OpenAI API dependency prompted a migration to Qwen 2.5 7B running in their GCP project. The migration took three engineering weeks, quality on the FAQ assistant use case was unchanged, and the model is now an internal component rather than a third-party subprocessor in their SOC 2 documentation.
Frequently asked questions
Can I run a private LLM on AWS without managing my own GPU servers?
Yes. Amazon SageMaker managed endpoints let you deploy open-weights models like Llama or Mistral on GPU instances that AWS manages at the hardware level. You define the model, the instance type, and the scaling policy. AWS handles the underlying hardware. Your data stays in your AWS account and never crosses to a model provider's infrastructure.
Is a private LLM required for HIPAA compliance?
HIPAA doesn't explicitly require a private LLM, but it does require that any system handling PHI operates under appropriate safeguards and that business associates have signed BAAs. Using a frontier API like OpenAI or Anthropic for PHI-adjacent workflows is permissible if you have a BAA and the architecture meets the Security Rule's technical safeguard requirements. Many legal teams at covered entities interpret the transit and access controls required by the Security Rule as effectively mandating private deployment for PHI-containing prompts.
What's the minimum GPU memory needed to run a 70B parameter model privately?
A 70B model in FP16 precision requires approximately 140GB of GPU memory. That means at least two NVIDIA A100 80GB GPUs running in tensor-parallel configuration. Running the same model in 4-bit quantization (AWQ or GPTQ format) cuts the requirement to roughly 40-45GB, meaning a single A100 80GB or two A100 40GBs. Quality loss from 4-bit quantization is minimal for most structured SMB tasks.
How does a private LLM deployment affect SOC 2 Type II audits?
A private VPC deployment typically simplifies SOC 2 audits by keeping the LLM as an internal component rather than a third-party subprocessor. You control access logs, audit trails, and vendor relationships. The tradeoff is that you must document your own security controls for the model endpoint, including access management, encryption, and incident response, rather than relying on a provider's SOC 2 report.
Are open-weights models like Llama and Mistral good enough for production SMB use cases?
For most structured SMB workflows including document summarization, form processing, internal knowledge retrieval, and communication drafting, yes. Llama 3.1 70B and Mistral Large 2 perform within a few percentage points of frontier models on these tasks in blind evaluations. They fall behind frontier models on complex multi-step reasoning and tasks requiring knowledge beyond their training cutoff.
What does it cost to run a private LLM for a 100-person company?
For a VPC-hosted deployment with sensible auto-scaling, budget $3,000 to $8,000 per month in GPU compute for a 70B model handling 10,000 to 50,000 requests per day. For smaller models or lower volumes, $800 to $2,500 per month is achievable. Add roughly 0.25 to 0.5 full-time engineering equivalent for ongoing maintenance. Total cost varies significantly based on request volume, model size, and whether you use spot or reserved instances.
What's the difference between a dedicated tenant LLM and a VPC-hosted LLM?
A VPC-hosted LLM runs in your own cloud account, giving you direct control over access policies, logs, and the full infrastructure stack. A dedicated tenant deployment, offered by providers like Fireworks AI or Together AI, runs on hardware dedicated to your organization but managed by the provider. VPC-hosted gives stronger control and audit clarity. Dedicated tenant reduces operational burden but requires you to trust and verify the provider's isolation claims.
Can a private LLM be used for customer-facing applications or just internal tools?
Private LLMs work for both. Customer-facing deployments require more attention to rate limiting, abuse prevention, and latency at scale, since you don't control the request volume as precisely as you do for internal tools. The compliance and security benefits of private deployment apply equally to customer-facing use cases, often more so, since customer data is usually the most sensitive data in the request stream.
Ready to Design Your Private LLM Architecture?
We've shipped private LLM deployments for SMBs in healthcare, financial services, and insurance, all under active compliance programs. Tell us about your use case and we'll scope an architecture that fits your data controls and your budget.