Ollama development company
We design, build and run applications on Ollama so your teams can use open-weight language models without sending prompts, documents or customer records to a third-party API, from a developer laptop prototype to a GPU server in your own cloud.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
Ollama development company for private, self-hosted LLMs
Krapton is an Ollama development company that builds production software around models you host yourself: chat assistants, retrieval-augmented search over internal documents, extraction pipelines and coding helpers that run on your hardware instead of a vendor’s. Ollama packages quantized open-weight models behind a CLI and a REST API on port 11434, and it exposes an OpenAI-compatible endpoint, so an application written against the OpenAI SDK can often switch to a local model by changing the base URL. That convenience hides real engineering choices. Quantization level, context length (num_ctx), keep-alive settings and GPU memory decide whether answers are fast and complete or slow and silently truncated. We pick the model family for the task, write Modelfiles that pin system prompts and parameters, add evaluation sets so quality is measured rather than assumed, and wrap the server with authentication, logging and rate limits, since a bare Ollama port should never be exposed to the internet. When load outgrows a single box, we plan the move to a higher-throughput server such as vLLM.
Ollama development services
Local RAG over private documents
Retrieval pipelines that chunk your PDFs, tickets and wikis, embed them with nomic-embed-text or mxbai-embed-large through Ollama, store vectors in pgvector or Qdrant, and cite sources in every answer.
Modelfile and prompt engineering
Custom Modelfiles that fix the base model, SYSTEM prompt, chat TEMPLATE and PARAMETER values such as temperature, stop tokens and num_ctx, versioned in Git so every environment runs an identical assistant.
Cloud-to-local LLM migration
Moving features built on a hosted API to Ollama’s OpenAI-compatible endpoint, with side-by-side evaluation so you see where a local model matches the hosted one and where it falls short.
Structured extraction and tool calling
Using Ollama’s JSON-schema format option and tool calling to turn invoices, emails and forms into validated records, with Pydantic or Zod checks that reject malformed output before it reaches your database.
GPU server deployment
Running the official ollama/ollama container on NVIDIA or AMD GPUs, on bare metal, a cloud VM or Kubernetes, with persistent model volumes, health checks and tuned OLLAMA_NUM_PARALLEL and OLLAMA_MAX_LOADED_MODELS.
Desktop and offline assistants
Apps for laptops, factory floors or field sites with no reliable connection, bundling a small quantized model and a local UI so staff keep working when the network does not.
Why teams choose Krapton for Ollama
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Evaluation before model choice
We build a test set from your real questions and score candidate models and quantizations against it, so the choice between a small fast model and a larger accurate one rests on evidence.
Security around an open port
Ollama listens on a plain HTTP port, so we place it behind a reverse proxy such as Caddy or NGINX with authentication, TLS, request size limits and per-client quotas.
Honest limits on throughput
Ollama suits internal tools and moderate traffic. For many concurrent users we benchmark it against vLLM or SGLang and recommend the server that fits, even when that means moving off Ollama.
License-aware model selection
Open-weight models ship under different terms, from Apache 2.0 to custom community licenses with use restrictions, so license review sits beside accuracy and speed when Krapton shortlists models.
How a Ollama project runs with Krapton
01
Use-case and data audit
We map the task, the documents involved, privacy constraints and the hardware available, then decide whether a local model can meet the quality bar at all.
02
Model bake-off
Several model families and quantization levels run against your evaluation set on target hardware, measuring answer quality, tokens per second and memory use side by side.
03
Build and harden
Application code, Modelfiles, retrieval and the proxy layer are built together, with prompt-injection tests and load tests run before any user touches the system.
04
Deploy and monitor
We ship containerized releases, track latency, GPU memory and failed generations, and re-run the evaluation set whenever a model or Modelfile changes.
The Ollama stack we build with
Ollama development: questions answered
What does an Ollama development company actually build?
It builds applications that run open-weight language models on infrastructure you control. Typical work includes internal chat assistants, document search with citations, data extraction from forms and email, and coding helpers for teams that cannot send source code to an outside API. Krapton also handles the less visible parts: model evaluation, Modelfile versioning, the authentication proxy, monitoring, and the hardware sizing that decides whether responses arrive quickly.
When is Ollama the wrong choice?
Ollama is the wrong choice when you need to serve hundreds of simultaneous users from shared GPUs with tight latency targets. Its strength is simple, reliable single-node inference. Servers built for continuous batching, such as vLLM or SGLang, usually extract more throughput from the same GPU. It is also a poor fit when quality tests show only a proprietary hosted model meets your accuracy bar.
Can our existing OpenAI-based code run against Ollama?
Often yes, with a base URL change and a model name. Ollama exposes an OpenAI-compatible endpoint under /v1, so the official OpenAI SDKs and frameworks like LangChain work against it. Differences remain: some parameters are ignored, tool-calling reliability varies by model, and smaller models follow long instructions less faithfully. We run your existing prompts through both backends and adjust prompts or Modelfiles where outputs diverge.
How do you keep an Ollama deployment fast as usage grows?
Speed comes mostly from fitting the model fully in GPU memory and keeping it loaded. We choose a quantization that fits VRAM with room for the context cache, set keep_alive so models are not reloaded on every request, and tune parallel request slots. Beyond one GPU node, we add replicas behind a load balancer or move high-traffic routes to a batching server while Ollama keeps serving development and low-volume tools.
What decides the cost of Ollama development services?
Scope and hardware decide it more than anything else. The main drivers are the number of use cases, how much document data must be cleaned and indexed, integrations with systems such as SharePoint or your CRM, the accuracy bar and the evaluation effort it needs, security review, and whether you already own suitable GPUs or need them provisioned. A short discovery phase turns these into a scoped estimate.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.