Deploy large language models the way that works best for your business — on your own private infrastructure, or on leading managed cloud AI platforms like AWS Bedrock, Azure AI Foundry, and Google Vertex AI.
Spundan handles the full deployment lifecycle end-to-end: model selection, fine-tuning, integration, and ongoing management — so your team gets AI that is powerful, secure, and production-ready from day one.
Not sure which route to take? Here's the simple picture. Either way, Spundan guides you from start to production.
You own the model. You own the data. Everything runs inside your own servers or private cloud — no external API calls, no shared infrastructure.
Leverage enterprise-grade AI infrastructure from AWS, Azure, or Google — with managed models, built-in scaling, and cloud-native integrations handled for you.
From on-premise servers to managed cloud platforms — we cover every deployment model so you can choose what fits your business, budget, and data requirements.
On-Premise LLM Deployment
Deploy open-source LLMs like Llama 3, Mistral, Phi, or Gemma directly on your own servers — keeping all data, inference, and model weights fully within your controlled environment. No cloud dependency, no third-party access.
Managed Cloud AI Platforms
We set up and manage LLM deployments on AWS Bedrock, Azure AI Foundry, and Google Vertex AI — giving you enterprise-grade AI with cloud-native scaling, security, and access to frontier models.
Domain-Specific Fine-Tuning
Fine-tune any model — open-source or managed — on your proprietary data using LoRA, QLoRA, or platform-native fine-tuning, creating an LLM that deeply understands your industry, terminology, and business context.
Model Optimization & Cost Tuning
We optimize models for your hardware and budget — using quantization, pruning, and inference acceleration (vLLM, Ollama, TensorRT) for private deployments, and right-sizing model tiers on cloud platforms to minimize token costs.
API & Application Integration
We expose your deployed LLM via a clean, OpenAI-compatible API or platform-native endpoint — and integrate it directly into your existing products, internal tools, and workflows with minimal code changes.
RAG & Knowledge Base Integration
Connect your LLM to internal documents, databases, and knowledge sources via Retrieval-Augmented Generation — delivering accurate, grounded responses without retraining, across both private and cloud-managed deployments.
Not ready to manage your own hardware? We set up, configure, and integrate enterprise-grade managed AI platforms from AWS, Microsoft, and Google — so you get the power of LLMs with cloud-native reliability and scaling.
A fully managed service giving you access to top foundation models — Claude, Llama, Mistral, Titan, and more — via a single API, with enterprise security baked in.
What Spundan does for you
Microsoft's unified AI platform for building, evaluating, and deploying generative AI apps — with access to GPT-4o, Phi, Llama, Mistral, and custom models within Azure's enterprise security envelope.
What Spundan does for you
Google's fully managed ML platform — with access to Gemini, Gemma, Llama, Claude, and Mistral, plus built-in MLOps tooling, Agent Builder, and enterprise-grade VPC controls.
What Spundan does for you
Whether private or cloud-managed, our process is the same — structured, transparent, and built around your goals.
Whether you choose private, AWS, Azure, or Google — we bring deep hands-on experience across all four paths and help you get the most out of whichever you pick.
For private deployments, your data never leaves your environment. For cloud deployments, we configure VPC isolation and data residency controls to ensure compliance.
We right-size every deployment — recommending private infra when usage is high, or managed cloud when you're just starting. No wasted GPU, no surprise API bills.
Built for regulated industries — healthcare, finance, legal, and government. Our deployments are designed to meet HIPAA, GDPR, SOC 2, and other compliance frameworks.
From model selection and fine-tuning to deployment, RAG, API integration, and monitoring — we handle the entire LLM stack so your team focuses on building products.
Not sure which deployment path is right? We offer a free 30-minute consultation to understand your business, budget, and data needs — and give you an honest recommendation, no sales pitch.
A Private LLM runs entirely on your own servers or private cloud — you control the hardware, the model, and the data. A Managed Cloud AI platform (like AWS Bedrock, Azure AI Foundry, or Google Vertex AI) is a service where the cloud provider manages the infrastructure and you access models via API within your cloud account. Both keep your data within your environment, but private LLMs give more control and lower long-term costs, while managed platforms are faster to set up.
It depends on your usage volume. Managed cloud platforms (Bedrock, Vertex, Foundry) charge per token — great for low or variable usage. Private LLMs have a higher upfront setup cost but flat infrastructure spend after that — much cheaper at high volumes. We'll help you model the cost for your specific workload before you commit.
Most likely yes — if you're already on AWS, Bedrock integrates natively with your existing IAM, VPC, S3, and Lambda setup. It's the fastest path to production for AWS-first teams. We can evaluate your current architecture and confirm if Bedrock is the right fit.
Yes — we deploy fully on your on-premise servers, including air-gapped environments. No cloud dependency, no third-party access. Your model, your hardware, your control.
Yes. With public APIs (ChatGPT, Claude.ai), your data is sent to external servers with shared infrastructure. With our deployments — private or via managed platforms in your cloud account — everything runs within your own environment. Your data never touches shared public infrastructure.
Ready to deploy your first LLM? Let's find the right path together.
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