Top open-source alternatives to ChatGPT for companies: Self-hosting options

Every team I hear from has a similar story: ChatGPT changed productivity overnight, but then the valid concerns started showing up:

1. Where is our sensitive data going?
2. What happens when API costs scale with our usage?
3. How do we maintain compliance when we're sending everything to an external service?

These questions aren’t only important to large enterprises.

Startups and growing companies face similar challenges, where unpredictable API bills can strain budgets, and handing sensitive customer data to a third party isn’t always acceptable when trust and compliance are at stake.

This is why the open-source AI space has grown with enterprise-ready and startup-friendly alternatives.

I’ll walk you through the top open-source alternatives to ChatGPT (including OpenAI’s own GPT-OSS release) and show you how to self-host them on platforms like Northflank to give your business complete control over AI infrastructure.

The truth is, ChatGPT’s API-first approach creates challenges that grow as usage scales.

When you send every query through OpenAI’s servers, you’re essentially handing over your most sensitive business data to a third party with no guarantees about how it’s stored or processed.

We’re talking about your:

• Customer information
• Internal documents
• Strategic discussions
• And countless other sensitive details

Then there’s the cost reality.

What starts as a few dollars in API calls can quickly spiral to thousands as your team adopts AI across departments, or, for smaller companies, as adoption expands beyond one or two early use cases.

Now you’re paying per token with unpredictable pricing, facing rate limits during peak times, and building your entire AI strategy on infrastructure you don’t control.

If your company is serious about AI adoption, these aren’t minor inconveniences; they’re deal-breakers that demand a different approach.

I’ve tested and worked with dozens of open source alternatives, but four consistently stand out for companies that want predictable costs and full data control.

Let’s see what you need to know about each:

This is the newest option, and it’s making waves for providing powerful, high-quality models that can be run on-premises with full control.

OpenAI released two versions with a permissive Apache 2.0 license: a larger 120B parameter model that runs on an 80GB GPU, and a smaller 20B model that can run on consumer hardware with 16GB of memory.

Both deliver high reasoning performance, though they are not designed to be as capable as OpenAI’s most advanced proprietary models.

Choose GPT-OSS when you want advanced performance with the backing of OpenAI’s research, but you need the control that comes with self-hosting.

It performs well on complex reasoning tasks and maintains the familiar ChatGPT-like behavior due to its training.

However, the models are text-only and must be run on your own infrastructure; they are not accessed through OpenAI’s official API or ChatGPT interface.

As the user, you are responsible for maintaining, updating, and ensuring the safety of your deployment.

DeepSeek has built a reputation for delivering impressive reasoning capabilities while being significantly more cost-effective than many alternatives.

This is largely thanks to its use of a Mixture-of-Experts (MoE) architecture, which enables huge parameter counts while only activating a smaller, more manageable subset for any given task.

The V3 and R1 series often perform above expectations, particularly in logical reasoning, coding, and mathematical problem-solving.

DeepSeek’s R1, for example, uses a “chain-of-thought” process to break down problems. That makes it slower than the faster, more general V3, but also more reliable for handling complex queries.

If your company needs to prove ROI before fully scaling AI infrastructure, DeepSeek is a natural fit. It combines performance with efficiency in a way that keeps budgets under control without sacrificing reasoning quality.

On top of that, DeepSeek releases its models under open-weight licenses, which has encouraged a wide and active community to build fine-tuned variants and tools. This makes it even more cost-effective and flexible for your use cases.

Qwen consistently delivers high performance across benchmarks, but where it stands out is in multilingual support.

Its latest models, including Qwen3 and specialized versions like Qwen-MT, are trained on massive multilingual datasets (up to 36 trillion tokens across 119 languages).

That gives you reliable results in translation, multilingual instruction-following, and managing nuanced conversational shifts across different languages.

If your company operates globally or needs AI that can handle multiple languages natively, Qwen is often the best fit. You’ll get broad linguistic coverage alongside dependable reasoning capabilities.

Alibaba also offers a full suite of options, including dense and Mixture-of-Experts (MoE) variants, as well as multimodal models for visual understanding.

Meta’s Llama models have become a standard in the open-weight AI space, known for their capabilities and broad accessibility.

The latest versions deliver powerful performance, with some reaching near-frontier capabilities.

If your company does significant software development or needs advanced coding assistance, Llama’s specialized and general-purpose variants give you targeted performance backed by a vast, active ecosystem.

What sets Llama apart is the massive ecosystem of tools, fine-tuned versions, and community support you can tap into.

This makes deployment much easier for your teams.

Now that you know what it takes to self-host these models, the next step is making deployment simple and repeatable.

Northflank is built to remove the complexity, so you can launch a model in minutes with a pre-built stack or scale a full deployment with orchestration, monitoring, and CI/CD.

You can get started instantly with one-click stack templates for GPT-OSS, DeepSeek, Qwen, and Llama. These come pre-configured with vLLM and Open WebUI so you can test models right away.

If you prefer a guided approach, follow our detailed self-hosting guides that cover everything from provisioning GPUs to optimizing inference for enterworkloads. Each guide is written to be reproducible in any environment.

Once your prototype is live, you can scale deployments across GPUs, regions, and clouds. Northflank handles orchestration, autoscaling, and CI/CD, so your AI setup scales smoothly into production.

This decision framework will help you select the right model to align with your business priorities. Most companies eventually adopt a “multi-LLM” approach, deploying different models for different use cases as they scale.

1. Go with GPT-OSS if you want a powerful, self-hostable model with a familiar conversational style. It performs well on reasoning and agentic tasks while giving you full control over data and costs. Note: you’ll need internal expertise and hardware, and GPT-OSS is “open-weight” (downloadable with permissive licensing) rather than fully “open-source.” It’s also text-only.

2. Choose Llama if you want proven reliability, community support, or coding assistance via Code Llama. Its ecosystem of fine-tuned variants makes deployment straightforward. Note: Meta’s license is permissive but not fully open-source and restricts usage for very large companies.

3. Pick DeepSeek if budget is your top priority but you still need competitive reasoning. Its Mixture-of-Experts design delivers excellent value for logic, math, and coding. Note: some models like R1 are slower but more precise, while V3 is faster but more general. V3.1 offers a middle ground.

4. Select Qwen if your company operates globally and needs multilingual capabilities with cultural context. Note: developed in China, Qwen includes moderation and filtering that you’ll need to assess for compliance (e.g. EU AI Act). Performance also varies across different versions and “thinking” modes.

Performance vs. resource trade-offs: Larger models (GPT-OSS 120B, Llama 70B+) deliver higher-quality results but require more GPU memory. Smaller ones (GPT-OSS 20B, Llama 8B) can run on consumer-grade hardware with trade-offs.

Most companies begin with one model to validate use cases, then scale into a mix of models for different workflows.

Now that you’ve seen GPT-OSS, DeepSeek, Qwen, and Llama, the core question is: are they company-ready?

For years, the answer was “not yet.” In 2025, OpenAI’s release of GPT-OSS signaled a shift, showing that open-weight models can now compete at a serious level.

Today, these models deliver targeted strengths:

• DeepSeek for reasoning
• Qwen for multilingual capability
• Llama for coding assistance

They’re more than “good enough” and serve as practical, high-performance tools.

That said, readiness comes with considerations:

• Most models are open-weight, not fully open-source, and some (like Llama) have license restrictions.
• Self-hosting gives you control over data and costs but requires investment in management, security, and governance.
• Many companies adopt a hybrid strategy: APIs for quick use cases, self-hosted models for sensitive or high-volume workloads.

The bottom line is open-weight models are company-ready if you pair them with the right strategy and resources.

When you self-host, your data stays in your environment.

For instance, your customer records, internal documents, and strategic discussions remain fully under your control. For regulated industries like healthcare and finance, this isn’t optional; it’s the only way to stay compliant.

Costs also become predictable.

Rather than paying for every token with bills that grow unexpectedly, you pay for infrastructure that scales with your workload.

If usage is steady and high, this can reduce costs over time. Keep in mind that hardware, energy, and skilled staff are part of the equation, so for lighter workloads, APIs may still be more affordable.

Self-hosting also removes external dependencies.

Rate limits are set by your infrastructure; outages at a provider do not impact your workflows, and sudden pricing or policy changes do not dictate your roadmap.

Self-hosting is not hands-off though.

You take on responsibility for deployment, monitoring, security, and performance tuning, which requires a capable team.

For many companies, the most effective path is hybrid: APIs for fast experimentation and general tasks, with self-hosted models for sensitive, high-volume, or regulated workloads.

Self-hosting might sound complex, but it comes down to three key considerations:

Each model has different GPU needs.

Smaller variants like Llama 8B and GPT-OSS 20B can run on a single high-end consumer GPU, such as an NVIDIA RTX 4090 with 24GB of VRAM. This becomes more practical when you use memory optimization techniques like quantization.

Larger models such as GPT-OSS 120B and Qwen 235B, on the other hand, require multi-GPU clusters with high-bandwidth interconnects.

The rule of thumb is simple: match the model size to your available GPU memory, and scale out as usage grows. Keep in mind that longer context windows or fine-tuning will increase VRAM requirements.

You can start simple by running a model on a single GPU for prototyping, and scale up to multi-node clusters for production workloads. With frameworks like vLLM, inference optimization becomes plug-and-play, and scaling across multiple machines is straightforward. Companies often begin with one model for testing, then expand to a dedicated AI cluster as adoption spreads.

This is where platforms like Northflank make a difference. By packaging your models in containers and deploying with Kubernetes, you get autoscaling, monitoring, and high availability out of the box. It takes your AI infrastructure from “fragile experiment” to “production-ready system” with the reliability your teams expect.

Self-hosting is about more than control; it’s also about economics.

At small scale, API-based AI may seem affordable.

However, as adoption spreads across teams, the per-token pricing model becomes increasingly unpredictable.

Running your own models flips the equation: you invest in infrastructure once, then scale usage without runaway costs.

For most companies, self-hosting becomes cost-effective once AI is no longer a side project and starts powering daily workflows.

ROI grows as more employees rely on the same infrastructure, distributing fixed costs over larger usage.

And the benefits extend beyond savings: full data ownership, built-in compliance, and the ability to customize models to your exact needs.

Think of self-hosting as the foundation for long-term AI adoption.

By owning the infrastructure, you avoid vendor lock-in, rate limits, and policy shifts that can disrupt your plans.

It also helps you future-proof your AI stack. Today’s top-performing model may be replaced tomorrow, but when you control the platform, swapping or adding models is a choice you make, not a dependency on a vendor’s roadmap.

The best way to begin is to choose a model that aligns with your immediate business goal, such as reasoning, multilingual support, or coding assistance, and deploy it as a prototype. This gives your team hands-on experience without overcommitting.

From there, you can build on Northflank’s deployment guides and one-click stacks to move from testing to production.

With Northflank, scaling into company-grade infrastructure is straightforward, allowing you to focus on producing value rather than infrastructure complexity.

If you’d like to learn more about the technical and strategic side of self-hosting, these resources will help:

• An engineer’s guide to open source AI models – A practical introduction to the leading models and how they compare.
• Self-hosting AI models: The complete guide – A step-by-step walkthrough of the self-hosting process.
• Open-source LLMs: The complete developer’s guide to deployment – Covers everything from infrastructure to scaling.
• Why smart enterprises are insisting on BYOC for AI tools – Explains the growing trend of “bring your own cloud.”
• 7 best AI cloud providers – A breakdown of the top infrastructure options.
• Top GPU hosting platforms for AI – An overview of where to run compute-heavy models.
• AI infrastructure: What it really takes – An in-depth look at the hardware and orchestration layer.
• Claude rate limits, pricing, and costs explained – A useful comparison for teams weighing proprietary APIs vs. open-source.
• Claude Code vs Cursor – An evaluation of AI coding assistants that highlights trade-offs with open-source options.