Developers want two things from a coding model: strong suggestions and clear transparency. StarCoder and its successor, StarCoder 2, aim to deliver both. In this article, we’ll examine what StarCoder is, its key features, who’s using it today, and what makes it unique among its competitors.
What is StarCoder?
StarCoder LLM is an open-access LLM for code generation created by the BigCode community. The first version of the StarCoder model, released in 2023, is a 15.5B-parameter model trained on permissively licensed GitHub code from The Stack (an open, permissively licensed dataset of source-code files collected by the BigCode project). It supports fill-in-the-middle editing, handles long inputs with an ~8K token window, and ships under the BigCode OpenRAIL-M license, which permits commercial use under responsible AI conditions.
StarCoder2 was released in 2024 with more extended capabilities. It comes in three sizes: 3B, 7B, 15B trained on The Stack v2 (600+ languages) with 3.3–4.3T tokens, GQA attention, and a larger 16K context (with sliding-window attention for efficiency). The entire pipeline (data curation, training code, checkpoints) is public.
Key Features to Use
Fill-in-the-Middle (FIM)
StarCoder and StarCoder 2 are trained to complete code inside a snippet, enabling smarter inline refactors, function-body rewrites, or test-stub generation without touching surrounding lines.
Long-context code understanding
StarCoder-15B handles 8,192 tokens. StarCoder 2 raises that to 16,384 tokens with sliding-window Grouped-Query Attention, which is useful for multi-file prompts, long diffs, or bulky API docs.
Multi-language coverage
Training on The Stack v2’s ≈ 619 languages lets the model reason across Python, Java, TypeScript, CUDA, Verilog, and niche DSLs. This is handy for cross-language translation or mixed monorepos.
Transparent data & training
BigCode publishes every preprocessing script, opt-out policy, and training configuration, making audits and reproducible benchmarks straightforward.
Flexible deployment
Run locally via Transformers, GGUF/Ollama builds, Hugging Face Inference Endpoints, or NVIDIA NGC containers. This is useful for VPC-isolated inference or enterprise SLAs. (Community GGUF ports; HF/NGC images are official.)
Commercial-friendly open license
The OpenRAIL-M license allows commercial use, provided you follow BigCode’s responsible-AI clauses.
Who is Using StarCoder?
The StarCoder model is already more than a research checkpoint. It is turning up in real production pipelines:
- ServiceNow fine-tuned the 15B-parameter model into its Now LLM, which drives text-to-code and workflow features across the Now Platform.
- NVIDIA ships StarCoder 2 inside NIM containers and exposes it through an OpenAI-compatible REST endpoint, allowing teams to drop the model directly into GPU clusters without requiring custom serving code.
- On Hugging Face, organizations spin up one-click Inference Endpoints and tap community fine-tunes like StarChat-β, which re-targets StarCoder LLM for conversational coding tasks.
What makes StarCoder unique?
- Open by default, audited by design: Where many coding models hide data and training details, StarCoder2 publishes datasets, curation scripts, and training code. This transparency shortens security reviews and supports internal reproducibility.
- Strong small/medium checkpoints: On public coding suites (HumanEval, MBPP, MultiPL-E, DS-1000), StarCoder2 3B and 7B beat most peers their size, while 15B tops its class.
- FIM + long context = practical editing: Fill-in-the-middle lets the model rewrite a function body or insert a guard clause without touching surrounding code, and the 16,384 token window means you can pass an entire file plus stack-trace in a single prompt.
- Deployment breadth: StarCoder models can run almost anywhere. You can run StarCoder locally with a community GGUF build (ollama run starcoder2), spin it up in seconds on Hugging Face Inference Endpoints, or deploy at scale via NVIDIA NIM containers that expose an OpenAI-compatible API.
- Responsible license with commercial room: Every checkpoint ships under the BigCode OpenRAIL-M license, which allows commercial use while incorporating responsible AI guardrails.
Pricing
Hugging Face pricing is seat-based. Compute (Spaces/Inference Endpoints) is billed separately. So the cost scales with team size and the hardware you choose.
Free
- Public Hub access
- Spaces CPU Basic is free.
- Good for learning and public demos.
Pro: $9/user/month
- 10x private storage
- 20x included inference credits
- 8x ZeroGPU quota
- Spaces Dev Mode
- Private dataset viewer
- Great for individual builders shipping private work.
Team: $20/user/month
- Adds SSO/SAML
- Storage regions
- Audit logs
- Resource-group access control
- Usage analytics
- Policy defaults
- Centralized token control
- Org-wide ZeroGPU/Inference Providers perks.
Enterprise: from $50/user/month
- Everything in the Team plan
- Highest storage, bandwidth, and API rate limits
- Managed billing with annual commitments
- Legal and Compliance processes
- Personalized support
Conclusion
StarCoder combines open weights, a permissive license, and fully published training data with practical features such as fill-in-the-middle edits, 16K-token context, and strong checkpoints ranging from 3B to 15 B. You can run it anywhere from a laptop via Ollama to Hugging Face endpoints or NVIDIA NIM containers, and it’s already powering production systems at companies like ServiceNow and NVIDIA. If you need trustworthy code suggestions without vendor lock-in, StarCoder is an easy yes.
