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The Missing Data & Model Layer for Your AI Platform

Stop fighting with GPU orchestration and vector index scaling. Solanica automates the deployment, management, and visibility of your AI stack on Kubernetes—keeping your models and data in your own infrastructure.
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Why AI Platforms stall in production?
  • The GPU Money Pit
    Standard K8s handles CPUs fine but leaves GPUs underutilized. You end up with "Zombie" nodes - idle hardware that still costs 100% of the bill.
  • The Vector Scaling Wall
    Running Milvus or Qdrant without specialized orchestration leads to lost indices and slow "Day 2" retrieval. Your AI is only as fast as its storage.
  • Operational Blindspots
    When an inference call hangs, you can't see why. Is the model OOM, the vector search timing out, or the K8s scheduler failing?
  • The Compliance Trap
    Sending data to public LLM APIs is a security nightmare. Building a private alternative is so complex that most projects die in "Pilot Purgatory."

The AI Orchestration Layer

Solanica automates the complex lifecycle of stateful AI resources on Kubernetes, from vector indices to private model serving.

Vector Store

Auto-Scaling Indices.
Managed Milvus, Qdrant, or Weaviate with automated backups and rebalancing.

Private Inference

Zero-Trust Serving.
Host Llama 3, Mistral, or deepseek locally. No data leaves your VPC.

AI Observability

Full Stack Tracing.
Spot bottlenecks across the model, the vector search, and the infrastructure.

VECTOR DB ORCHESTRATOR GPU NODES

Solanica Platform Core

GPU Slicing • Model Registry • Cost Governance
Supported: NVIDIA GPUs • AWS Trainium • Google TPUs • Metal

Built for the AI Lifecycle

Stop building "Toy AI." These are the infrastructure patterns you need to run production workloads.

Production RAG

Your retrieval layer is your bottleneck. Solanica automates the "Day 2" operations of Milvus and Qdrant—handling sharding, backups, and upgrades so your vector search is as reliable as Postgres.

Private LLM Hosting

Stop leaking IP to public APIs. Deploy open-weight models (Llama 3, Mistral) directly on your Kubernetes nodes. Data never leaves your VPC, and you control the versioning.

GPU Cost Control

Inference endpoints are expensive to keep idle. We enforce strict TTLs (Time-to-Live) and scale-to-zero policies. If no one is querying the model, the GPUs shouldn't be burning cash.

The Cloud AI Trap
Convenient at first, fatal at scale.
  • ✕
    Vendor Lock-in
    Your embeddings are trapped in proprietary DBs. Moving them out is slow and expensive.
  • ✕
    Black Box Pricing
    Opaque token costs that explode as you scale. You pay for the API markup, not the compute.
  • ✕
    Data Egress Tax
    Training on one cloud and serving on another? Prepare to pay massive fees to move your data.
The Sovereign Stack
You own the plumbing. We keep it running.
  • ✓
    True Portability
    Run on AWS today, bare metal tomorrow. The stack (K8s + OpenEverest) stays exactly the same.
  • ✓
    Transparent Cost
    Pay for the GPU nodes you actually use. No hidden markups on inference tokens.
  • ✓
    Open Source Core
    Built on OpenEverest. If we disappear tomorrow, your platform keeps running. You own the code.
Data Sovereignty
We orchestrate the containers; the weights, embeddings, and prompts stay strictly within your VPC.
NRE Services
Need a custom plugin for a niche vector index or hardware accelerator? We build it for you.
Engineer-to-Engineer
No Tier-1 support scripts. You get direct Slack access to the engineers who wrote the code.

Stop building infrastructure.
Start shipping intelligence.

Your data science team is waiting for resources. Give them a platform that works today.
The features on this page are coming soon, but it is a great opportunity to partner early.
Talk to the team Try OpenEverest
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