Laava builds cloud-agnostic and model-agnostic AI architectures, so you are not locked to one vendor. Swap models with a config change, move between Azure, AWS, GCP, or on-premise without rebuilding, and keep your business logic independent of any provider's pricing or roadmap.
One workflow to production. One platform to build on what works.
Positioning
Most AI implementations hardwire one cloud and one model into every layer, so when pricing changes, a better model ships, or the rules shift, you are stuck, and rebuilding is slow and risky.
Laava keeps your business logic separate from any single vendor, so you can move.
Context, tools, and control stay swappable, independent of the model or cloud underneath.
Outcomes
Future-proof AI: adopt new models on day one - change one config line
Cost optimization: route each task to the best price-performance model automatically
Regulatory compliance: deploy in any region, any cloud, or fully on-premise
Negotiation leverage: no single vendor can dictate your terms
Portable infrastructure: move workloads between clouds without downtime or rework
Resilience: if one provider has an outage, fail over to another
Two dimensions of independence, one unified architecture
Your AI agents work with any LLM - Claude, Llama, Mistral, or whatever comes next. We keep the model behind an abstraction, so switching models is a configuration change, not a rewrite. Adaptive routing sends complex queries to powerful models and simple tasks to fast, cost-effective ones.
Everything runs on Kubernetes - containerized, portable, and cloud-independent. We use Terraform and OpenTofu for reproducible infrastructure, ArgoCD for GitOps deployment, and standard cloud-native patterns that work identically on Azure, AWS, GCP, or bare metal.
Choose where your data lives and which models process it. Need EU-only hosting? On-premise for classified data? A specific cloud for regulatory reasons? The architecture supports it all without compromise on functionality.
Not every query needs the most expensive model. Our intelligent routing layer analyzes complexity and sends each request to the optimal model - balancing quality, speed, and cost. Simple lookups go to fast models. Complex reasoning goes to powerful ones. You save money without sacrificing quality.
Approach
A Platform Launch delivers a working agnostic architecture - not a slide deck
Step 01
We map your current AI stack, identify lock-in points, and design a target architecture that decouples your business logic from specific vendors. You see exactly which models and clouds will work for your use case.
Step 02
We build the model abstraction and infrastructure-as-code foundation. Your AI agents get a unified interface that works with any LLM provider. Kubernetes manifests and Terraform modules make the infrastructure portable.
Step 03
We connect multiple model providers and configure adaptive routing. Your system now intelligently selects the best model per task. We set up GitOps pipelines so deployment to any cloud is automated and repeatable.
Step 04
We prove the architecture works - live demo of model switching, cloud portability, and cost optimization. Full documentation, runbooks, and knowledge transfer to your team. You own everything.
Principles
Principle 01
Models change every quarter. Architecture endures. We build the scaffolding that makes any model work - so you benefit from every advancement without the rewrite.
Principle 02
Switching from one model to another should be a config change, not a sprint. We design every integration point to be swappable by default.
Principle 03
If it can't move between clouds, it's not infrastructure - it's a liability. Every component we build runs on standard Kubernetes with no cloud-specific dependencies.
Principle 04
Context, tools, and actions stay independent layers. Change your vector database without touching your model. Change your model without touching your integrations.
FAQ
The most practical questions that usually come up before a first application actually lands in the operation.
View all questionsIt means your AI system isn't hardwired to a specific LLM provider. We build an abstraction layer so you can switch between Claude, Llama, Mistral, or any future model by changing a configuration file - no code changes, no redeployment of business logic. When a better model launches tomorrow, you can adopt it the same day.
No. Our abstraction layer is lightweight and adds negligible latency (typically under 5ms). What it does add is the ability to route tasks intelligently - complex reasoning goes to powerful models, simple tasks to fast ones. Most clients actually see better overall performance because they're no longer using expensive models for trivial tasks.
Absolutely. Even if you stay on Azure, a cloud-agnostic architecture gives you negotiation leverage, failover options, and the ability to use specific services from other clouds where they're superior. It also means your AI investment survives any future infrastructure decisions.
Not every query needs a frontier model. A simple FAQ lookup might cost €0.001 on a lightweight model versus €0.03 on a powerful reasoning model. Multiply that by thousands of daily queries and the savings are significant - often 40-60% reduction in model costs without any loss in quality for end users.
First step
Let's map your current AI stack and design an architecture that gives you true independence. No lock-in, no compromises.
Response time
We typically respond within 24 hours
