Custom & Private Models

Use the right AI model, locally or in the cloud.

Standard hosted models do not fit every product or data environment. We deploy private and self-hosted models where teams need tighter control or models adapted to product-specific data and workflows.

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Trusted by 100+ Enterprise clients

Why deploy with Callstack

Better model output. More control.

We evaluate where hosted models fall short, then deploy private, self-hosted, regional, or hosted models around your data, latency, security, and workload needs.

  • Keep sensitive data off third-party systems.

    Self-hosted models reduce external data exposure.

  • Get better output from the model.

    Fine-tuned or specialized models perform better on your codebase, domain, and tasks.

  • Choose where the model runs.

    Use self-hosted, on-device, regional, or hosted models based on your security, latency, and workload needs.

Services

Choose where and how models run.

  • Use Cases

  • On-Premise LLM Deployment

    Stand up self-hosted models so sensitive code and data never leave your infrastructure.

  • LLM Fine-Tuning

    Fine-tune models on your codebase, conventions, and domain so outputs match how your teams actually build.

  • Model Evaluation

    Benchmark candidate models against your real engineering tasks to pick the right one and prove it works.

Custom & Private Models

Start with a model evaluation.

Tell us where hosted models fall short for your team today. We'll come back with the right approach for your data and workload, and what the rollout path looks like.

Plan my model strategy

Scope → Build → Scale

Deploy models where the workload fits.

  • Scope

    We select a product workflow, define where agents act, and agree the controls and success measures.

    • Workflow and owners defined
    • Baseline and controls agreed
    • Token budgets confirmed

  • Build

    We connect context, agents, and verification to your tools, then run the workflow on your product backlog.

    • Running on your product backlog
    • Speed and quality measured
    • Business impact validated

  • Scale

    We document the workflow, transfer ownership, and extend it across teams and products based on results.

    • Your team owns the workflow
    • Expand based on results
    • Full knowledge handover
30/31
Next.js tasks as pixel canary
85%
Lower inference cost vs Opus 5.5
Industry runs on our tooling
50M+
OSS downloads / month
100+
Enterprise projects delivered
React Foundation Founding Member
300+ commits to React Native
ISO 27001 certified
32
Models tested against React Native

10 years of React Native → Now in your AI stack

Start with a model evaluation.

Tell us where hosted models fall short for your team today. We'll come back with the right approach for your data and workload, and what the rollout path looks like.

Plan my model strategy

Open Source

Build with our open-source tools.

We open-source the tools behind our delivery model.
Use them, fork them, or let us run them for you.

Check All Tools

React Native AI

On-device LLM execution in React Native with Vercel AI SDK compatibility

227.7K downloads/mo

React Native Evals

A benchmark suite for evaluating how coding models solve real React Native tasks

111 stars

Skill Gym

Prove your agent skills work before you ship them

1.6K downloads/mo

Insights

Worth your time, by engineers.

Explore Insights
Quantization Impact on LLM Quality: A Case Study of Apex — cover

Nov 4Talk

Quantization Impact on LLM Quality: A Case Study of Apex

Artur Morys-Magiera and Lech Kalinowski share findings from 344 benchmark runs with Apex, our Gemma-based LLM, examining the trade-offs between model size, decode speed, and quality.

Conversational Banking: From Customer Questions to Authorized Actions — cover

Nov 2Article

Conversational Banking: From Customer Questions to Authorized Actions

How does a banking assistant turn a customer’s question into an authorized action? Follow an unfamiliar payment from transaction lookup to card freeze and specialist handoff, exploring how models, banking services, and human teams work together and what the bank needs to control at each step.

Oct 1Article

Apex Is Now Generally Available: Agentic Coding Model for Mobile and Web, Specialized in React

Apex is now publicly available. Built by Callstack, it brings React Native expertise to feature development, code reviews, debugging, and testing, with strong performance on Next.js tasks too. See the benchmark results, compare API pricing, explore self-hosting options, and get started with Apex in your own project.

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Oct 1Live Stream

Can Jev Make AI Agent Decisions Faster?

Explore Jev with Kewin Wereszczyński and Szymon Chmal: how it works, its approach to faster agent decisions, and possible local alternatives.

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Sep 15Talk

No Framework, No Server: Making AI Agents Collaborate in One Terminal

Lech Kalinowski will show how Agents Commander lets multiple AI coding agents collaborate directly in one terminal without a central server or heavyweight framework.

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Aug 13Live Stream

What Apex shows about React Native AI models

See how Apex, Callstack's React Native model, compares with frontier models on navigation, animations, async state, and performance-heavy UI work.

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Jun 8Article

On-device AI After WWDC 2026: What's New?

Apple's 2026 AI updates move Apple Intelligence beyond a narrow text model. Foundation Models gets image input and server-backed calls, high-end devices get a stronger on-device model, and Core AI gives custom local models a native path. We're mapping these APIs into the React Native AI stack and working with early users on real app workflows.

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Jun 5Article

Optimizing Self-Hosted Gemma for Production Inference

We optimized self-hosted Gemma 4 31B for Apex by tuning vLLM server configuration, GPU memory use, KV cache capacity, context length, multimodal limits, tensor parallelism, and MTP speculative decoding. The article covers what changed, the performance gains we measured, the latency tradeoffs, and the production setup we recommend for faster Gemma serving.