Github Copilot and ChatGPT alternatives
There are a growing number of AI coding tools that are alternatives to Copilot. A list of other popular, promising options. Originally published 16 May 2023.
There are plenty of tools to choose from aside from Copilot and ChatGPT. Here are the most promising ones worth checking out, with an emphasis on those with self-hosting as an option. Date of launch is in brackets:
- Tabnine (2019)
- GitHub Copilot (2021)
- Replit Ghostwriter (2022)
- Amazon CodeWhisperer (2022)
- Codeium (2022)
- SourceGraph Cody (2023)
- CodeComplete (2023)
- FauxPilot (2023)
- Tabby (2023)
One important thing to note about ChatGPT is that, by default, it uses your input via the web interface to train its model. This is how Samsung employees leaked confidential data by asking ChatGPT to generate meeting notes. ChatGPT retains user data, even that of paying users. If you don’t consent to this, you need to opt out.
Below are a couple of ChatGPT alternatives which do not “leak” data like it does, as in making user-entered data part of a training set which can later be accessed by all customers:
- OpenAI APIs. Curiously, ChatGPT uses data entered via its web interface for training, but not when using its APIs. So an obvious workaround is to use the APIs with a wrapper, like the open source Chatbot UI.
- Azure OpenAI Service. Fine-tune custom AI models with your company data and hyperparameters.
- MosaicML. Train large AI models with your company data in a secure environment. Point to an AWS S3 bucket, and that’s it!
- Glean. AI-powered workplace search across the company’s apps, powered by deep learning-based large language models (LLM.)
- Aleph Alpha. A company emphasizing that it’s a European AI technology company, which has open sourced its code codebase and doesn’t use customer data to train models.
- Cohere. A set of LLMs to generate text, summarize it, classify and retrieve it.
- Writer. A generative AI platform that trains on the company’s data.
Building your own company model instead of using a centralized LLM provider is another alternative, and this approach could be prudent for businesses conscious about not passing sensitive and proprietary data to vendors. Databricks created Dolly, a cheap-to-build LLM which works pretty decently compared to ChatGPT, although the model is more dated. They also open sourced 15,000 records of training data. Read more about Dolly.
A very relevant question in the coming age of LLMs will be “buy, build, or self host?” This is because the usefulness of LLMs depends on two things: the model, and the additional training data. Companies will want to train LLMs using their in-house data to make them most useful for staff. However, trusting a vendor with in-house data is more risky than building and operating an LLM in-house, or self hosting one. But there’s considerable cost in both self-hosting, and especially in building one. Still, it’s early days, so perhaps experimentation like this makes sense?
It feels to me that ChatGPT alternatives are an incredibly hot topic and we’ve likely only skimmed the surface of startups working in this area. For example, there are more than 50 LLMs with 1B+ parameters that can be accessed via open-source checkpoints or proprietary APIs. Here’s a list of them, collated by software engineer Matt Rickard.
This article is an excerpt from The productivity impact of AI coding tools. The full article additionally covers:
- The survey. An overview of questions and the profiles of respondents.
- Comparable productivity gains. The gains which GitHub Copilot and ChatGPT offer are enticing, but not totally unprecedented within tech. Experienced engineers with decades in the business share examples of previous comparable productivity improvements.
- GitHub Copilot. Its most common use cases, where the biggest gains are to be found, and when this tool isn’t so useful.
- ChatGPT. Most common use cases and when to proceed with caution.
- Copilot vs ChatGPT. How the tools compare. When is one better than the other?
- The present and future of AI coding tools. Common observations and interesting predictions from survey respondents.
- "Are AI coding tools going to take my job?” A very common question and source of concern for some engineers.
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