We select, integrate, and productionise open-weight models that match your requirements — at a fraction of proprietary API costs. Model selection is a skill most teams don't have. We've benchmarked hundreds of model-task combinations.
Defaulting to GPT-4 for every task — paying 5–10× more than necessary for tasks open-source handles equally well
No systematic model selection process — engineers pick familiar APIs, not optimal models
No task-specific benchmarking — teams use public leaderboards that don't reflect their actual use cases
Integration complexity — each open-source model deployment is treated as a one-off engineering project
Fear of quality regression — legitimate concern without a proper evaluation framework
Six stages from use case audit to production-grade multi-model deployment.
Map every AI task in your target workflow. Different tasks have different accuracy/cost/latency trade-offs — separate them before selecting models.
Evaluate Llama 3.3, Mistral, Gemma 3, Phi-4, Qwen 2.5, and DeepSeek candidates against your task requirements and constraints.
Build task-specific evaluation sets using your actual data — not just public benchmarks that don't reflect your use case.
Compare API pricing vs managed hosting (Inference Endpoints) vs self-hosted across 12-month projections with your usage forecasts.
Design the routing layer: LiteLLM for multi-model routing, fallback policies, and OpenAI-compatible interfaces your team already knows.
Deploy with monitoring (latency, accuracy drift, cost), model versioning strategy, and fallback routing to cloud models if needed.
Your AI inference bill exceeds €5K/month and is growing, you've been told to reduce AI costs without sacrificing capability, you're building multi-model systems and need a systematic routing strategy, or you want vendor independence without sacrificing quality.
It depends on your task, hardware, and compliance requirements. For general enterprise use: Llama 3.3 70B. For EU-sovereign deployments: Mistral Nemo 12B. For coding: Qwen2.5-Coder 32B. For edge/constrained hardware: Phi-4-mini 3.8B. We benchmark your specific tasks before recommending.
For most enterprise tasks, the quality gap has closed significantly. Llama 3.3 70B matches GPT-4 on instruction following and many coding benchmarks. The gap remains in complex multi-step reasoning and world knowledge. Our task-specific benchmarking tells you exactly where the gap is — and whether it matters for your use case.
In most cases, yes. LiteLLM provides an OpenAI-compatible API that works with any existing LangChain, LlamaIndex, or direct API integration. You change the base URL and model name — your code stays the same.
We only recommend models with permissive commercial licenses. Llama 3.3 (Meta license, commercial use allowed for <700M MAU), Mistral models (Apache 2.0), Gemma 3 (Apache 2.0), Phi-4 (MIT), Qwen 2.5 (Apache 2.0), DeepSeek-R1 (MIT). We review the license for your specific use case.
Options: Hugging Face Inference Endpoints (managed, EU data residency available), your own cloud VMs (A10G/A100), or on-premise. We design the architecture based on your latency requirements, concurrency, and compliance constraints.
Let's discuss how this service can address your specific challenges and drive real results.