The Missing Data & Model Layer for Your
AI Platform
Stop fighting GPU orchestration and vector index scaling. Solanica automates the deployment, management, and observability of your AI stack on Kubernetes — keeping your models, embeddings, and prompts inside your own infrastructure.
Why AI Platforms Stall in Production
Standing up a notebook with Llama is easy. Running it for paying customers is where the trouble starts.
The GPU Money Pit
Standard Kubernetes schedules CPUs fine but leaves GPUs underutilized. You end up with zombie nodes — idle hardware still billing 100% of the rate.
The Vector Scaling Wall
Running Milvus or Qdrant without specialized orchestration leads to lost indices and sluggish Day 2 retrieval. Your AI is only as fast as its storage.
Operational Blindspots
When an inference call hangs, you can't see why. Model OOM? Vector search timing out? Scheduler failing? Without correlated tracing, every incident is a manual hunt.
The Compliance Trap
Sending IP, customer data, or PII 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 Platform sits between your apps and your accelerators, automating the lifecycle of the stateful pieces every AI stack needs — vector indices, model serving, and GPU pools.
Built for the AI Lifecycle
Stop building "toy AI." These are the infrastructure patterns you need to run production workloads — on your own hardware, on your own terms.
Production RAG
Your retrieval layer is your bottleneck. Solanica automates the Day 2 operations of Milvus and Qdrant — sharding, backups, upgrades — so your vector search is as reliable as Postgres.
- Auto-sharded vector indices
- Scheduled snapshots & PITR
- Rolling upgrades with zero downtime
Private LLM Hosting
Stop leaking IP to public APIs. Deploy open-weight models (Llama 3, Mistral, DeepSeek) directly on your Kubernetes nodes — data never leaves your VPC and you control the versioning.
- OpenAI-compatible endpoints
- vLLM, TGI, or Triton serving
- Version pinning & canary rollouts
GPU Cost Control
Inference endpoints are expensive to keep idle. We enforce strict TTLs and scale-to-zero policies — if no one is querying the model, the GPUs shouldn't be burning cash.
- Scale-to-zero on idle endpoints
- GPU slicing (MIG) for shared loads
- Per-team quotas & cost attribution
The Cloud AI Trap vs The Sovereign Stack
Hosted AI APIs are great for prototypes. They become a problem the moment your usage — or your compliance team — gets serious.
The Cloud AI Trap
Vendor Lock-in
Proprietary model versions, embeddings, and fine-tuning APIs. Switching means rebuilding the pipeline.
Black Box Pricing
Per-token billing balloons unpredictably as adoption grows. One viral feature can blow the quarterly budget.
Data Egress Tax
Your prompts, your customer data, and your embeddings leave your perimeter on every call.
The Sovereign Stack
True Portability
Open-weight models on standard Kubernetes. Move from AWS to Azure to bare metal without rewriting your stack.
Transparent Cost
You pay for GPU hours, not magical tokens. Scale-to-zero, GPU slicing, and per-team quotas keep spend predictable.
Open Source Core
Built on OpenEverest (CNCF Sandbox, Apache 2.0). Inspect it, fork it, run it — even if Solanica disappears tomorrow.
Why Partner with Solanica for AI?
The Solanica team has been operating stateful workloads on Kubernetes since the early days. We bring that same discipline to GPUs, vectors, and models.
Data Sovereignty by Default
Your prompts, embeddings, model weights, and fine-tuning data stay inside your VPC or data center. No third-party API calls, no surprise audits, no embedding leaks.
NRE for the Hard Parts
Need a Triton plugin, a custom scheduler policy, or integration with an in-house feature store? Our NRE (Non-Recurring Engineering) team builds it — and contributes it upstream when it makes sense.
Engineer-to-Engineer Support
No Tier-1 scripts. You get a direct Slack channel with the OpenEverest maintainers and platform engineers who actually built the orchestration layer.
Stop Building Infrastructure.
Start Shipping Intelligence.
Your data science team is waiting on resources. Give them a platform that just works — on your own hardware, behind your own firewall. Some features on this page are landing soon: now is the right moment to shape them as an early partner.
