Model & Inference Layer
Model and inference engineering for frontier, open-weight and small language models, hosted or self-hosted, behind one consistent interface. The choice of model stays with you, from proof of concept to production.
What is this layer about
Every AI programme starts with a model, and enterprises rarely want just one. Different jobs call for different models: a frontier model for hard reasoning, an open-weight model for cost-sensitive volume, a small language model for a narrow task that must run close to the data.
The Model & Inference Layer is where those models are reached and run. It covers hosted APIs from providers such as OpenAI, Foundry AI and Anthropic, open-weight families such as Qwen, Kimi and DeepSeek, and open-source small models served on your own infrastructure.
Zapper Edge stays model-agnostic here. The layer hides provider differences behind a common interface, so applications are not rewritten when a model changes. It also decides where inference runs, which matters for data residency and sovereignty. The model is never the problem; missing control is.
What do we deliver
Frontier model access
Connect to hosted frontier models from OpenAI, Foundry AI and Anthropic through managed credentials, so teams use approved models without each application holding its own keys.
Open-weight model access
Use open-weight families such as Qwen, Kimi and DeepSeek, hosted by a provider or run on your own infrastructure, when openness, cost or control matters more than a hosted service.
Self-hosted inference
Serve models on your own GPUs or cloud tenancy, with model serving, quantization and GPU runtimes chosen to fit the hardware, latency needs and data boundaries of each workload.
Small model support
Run open-source small language models for focused jobs such as classification, extraction and summarisation, close to the data, where a large model would add cost and exposure without adding value.
Model abstraction
Expose different providers and runtimes behind one consistent interface and request format, so applications can change models without code changes and teams avoid lock-in to any single vendor.
Inference data residency
Place inference in the regions and environments your policies require, so prompts and outputs stay within approved boundaries, whether the model is hosted by a provider or self-hosted.
Technology stack
- Hosted model APIs
- OpenAI API
- Azure AI Foundry
- Anthropic API
- Google Gemini API
- Amazon Bedrock
- Open-weight and small models
- Qwen
- Kimi
- DeepSeek
- Llama
- Mistral
- Phi
- Model serving
- vLLM
- Ollama
- llama.cpp
- NVIDIA Triton Inference Server
- Hugging Face Text Generation Inference
- Quantization and GPU runtimes
- GGUF
- AWQ
- GPTQ
- CUDA
- ROCm
- TensorRT-LLM
- Abstraction and gateways
- OpenAI-compatible APIs
- LiteLLM
- ONNX Runtime
- LangChain
These are the kinds of technologies this layer is built from; the choice of models, providers and runtimes stays with the customer.
Business outcome
No lock-in to one model vendor. Applications talk to a common interface, so you can adopt a better or cheaper model later without rebuilding what you already run.
The right model for each job. Teams match frontier, open-weight and small models to the task, instead of using one model for everything.
Inference that respects data boundaries. You decide where inference runs, so sensitive prompts and outputs stay within the regions and environments your policies allow.
Faster, safer AI adoption. Teams reach approved models quickly through one governed path, which shortens the route from pilot to production without bypassing review.
Frequently asked questions
What does the Model & Inference Layer cover?
It covers how your teams reach and run models: hosted frontier APIs, open-weight families and open-source small models, served by a provider or on your own infrastructure, all behind one consistent interface.
Does it lock us into one model provider?
No. The layer is model-agnostic and hides provider differences behind a common interface, so you can add or swap models without rewriting applications. The choice of models and providers stays with you.
Where does inference run and where does our data go?
You decide. Inference can be placed in the regions and environments your policies require, whether a provider hosts the model or you self-host it, so prompts and outputs stay within approved boundaries.
How does this layer relate to the layers above it?
It supplies the models; the layers above add context, orchestration, tools, safety controls and observability. Model routing and policy work with it, so each request reaches an approved model under your rules.
Questions about your AI rollout?
Talk to us about how the seven layers fit your models and data.