Building Your First AI Agent or Scaling a Production Fleet — We Design and Run the Infrastructure.
Most SMBs can't hire a specialist AI infrastructure team — and shouldn't have to. KubeNiche designs and operates your full Agentic AI stack on Kubernetes: LiteLLM gateway, MCP server orchestration, RAG pipelines, vector store management, GPU autoscaling, and token cost attribution. Whether you're deploying your first LLM-powered workflow or running a multi-agent system in production, we own the infrastructure from week one.
Discuss Your AI InfrastructureAgentic AI Infrastructure Is a Specialisation — Not a Side Project for Your DevOps Team
Agentic AI workloads have unique infrastructure demands that generic Kubernetes configurations can't handle: GPU and accelerator scheduling, high-throughput LLM inference serving, multi-model routing via the Model Context Protocol (MCP), stateful agent memory with vector store backends, semantic caching, and token cost attribution across provider APIs. SMBs starting their AI journey face a steep learning curve with no margin for expensive production incidents. Teams already running AI workloads hit the same wall when they try to scale — runaway agent loops, uncontrolled token spend, and observability blind spots. KubeNiche brings production-grade Agentic AI infrastructure expertise to both. We've already learned the hard lessons so you don't have to.
MCP Ecosystem Experience
Open Source & Managed MCP Servers We Deploy
We've deployed and operated these tools in production Kubernetes environments — not just evaluated them in a sandbox.
What's Included
Core Capabilities
Greenfield AI Infrastructure Design — Start Right
For SMBs deploying their first LLM-powered application or Agentic AI workflow, we design the infrastructure foundation from scratch — LiteLLM gateway configuration, MCP server architecture, vector store selection, K8s cluster sizing for inference workloads, and cost governance from day one. No expensive retrofits later. You get a production-grade AI infrastructure blueprint before you write your first agent.
MCP Server Design, Deployment & Operations
We design and deploy MCP servers on Kubernetes — using FastMCP, the Anthropic MCP SDK, or custom implementations — that expose your internal APIs, databases, SaaS integrations, and file systems as callable tools for AI agents. Each server is containerised, Helm-packaged, and managed via GitOps. Tool schemas are versioned, auth is enforced at the gateway layer, and observability is wired from day one. Compatible with Claude, GPT-4o, Gemini, and any MCP-capable agent runtime.
LiteLLM Gateway — Unified Multi-Provider LLM Routing
Deploy LiteLLM as your unified LLM proxy on K8s — routing across OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex AI, Mistral, and self-hosted models (Ollama, vLLM, llama.cpp). Per-team virtual keys, token budget enforcement, provider fallback chains, semantic caching, and MCP tool call proxying — all managed and monitored by KubeNiche. Critical for both first-time AI adopters and teams managing multi-provider spend.
AKS, GKE, OpenShift & ROSA — Platform Expertise
We've operated Agentic AI workloads on all four major managed K8s platforms. GPU node pools (A100, H100, L40S), KEDA-based autoscaling for inference queues, resource quotas for multi-tenant agent deployments, and platform-specific day-2 operations — including OLM on OpenShift and ROSA. For greenfield deployments, we select and configure the right platform for your cloud provider, team size, and AI workload profile.
RAG Pipeline Infrastructure — Vector Stores & Retrieval
Vector store deployment and operations — pgvector on PostgreSQL, Weaviate, Qdrant, and Chroma on K8s. Embedding pipeline design, chunking strategy, hybrid search (dense + sparse retrieval), index refresh automation, and retrieval latency tuning. For SMBs building their first RAG application, we design the full retrieval architecture. For teams with existing pipelines, we optimise retrieval quality and reduce hallucination rates.
Agent Observability, Evals & Drift Detection
Every agent deployment we operate includes distributed tracing (OpenTelemetry), LLM call logging via LiteLLM, evaluation pipelines (Ragas, DeepEval, custom evals), and answer quality drift detection. You know when retrieval degrades or model behaviour shifts before your users do. For new AI deployments, we establish eval baselines before go-live so you have a quality benchmark from day one.
Managed SRE for Agentic Workloads — 24/7 Operations
24/7 operations for your LLM inference clusters, MCP servers, vector stores, and agent pipelines — as a fully managed service. We own the on-call rotation, maintain runbooks, handle certificate rotation, and respond to incidents with a sub-15-minute SLA. Your engineers stay focused on building agents and shipping product, not firefighting infrastructure at 2am.
First AI agent or tenth — we build and run the infrastructure either way
Let's design the right K8s architecture for your LLM workloads. Whether you're starting from scratch or scaling an existing Agentic AI system, we'll scope the right managed infrastructure engagement — from LiteLLM gateway to MCP server deployment and ongoing 24/7 operations.
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