AI Startup

Building Your Own AI Coding Assistant Product

Stack for shipping a custom AI coding assistant — code completion, chat, code search, and agentic refactoring — as a standalone product or IDE plugin.

Developer tools founders and platform engineering teams building custom AI coding tools, IDE extensions, internal developer portals, or coding agents for specific languages or frameworks. $300–$3,000; LiteLLM routing to Groq for completions and Claude for chat is the most cost-effective strategy. At 1k active devs × 500 completions/day × 200 tokens = 100M tokens/month. 📦 9 tools

Building a coding assistant product (not just using Cursor for yourself) means solving hard problems: indexing large codebases accurately, providing low-latency completions, routing between models (fast cheap model for completion, powerful model for chat), and building a code-aware context retrieval system. The core loop is: parse the codebase with Tree-sitter for AST-level understanding, embed code chunks with a code-specialized model, retrieve relevant context via a vector store, route to the right LLM via LiteLLM, and evaluate outputs with code execution sandboxes. DeepSeek Coder and Codestral are excellent cost-efficient alternatives to GPT-4o for code-specific tasks.

The Stack

Anthropic

— Primary reasoning LLM

Claude 3.5/3.7 Sonnet leads benchmarks on code generation, refactoring, and multi-file edits. Its 200k context window handles full repository context without truncation for most codebases.

Alternatives: openai, deepseek-api, codestral, groq, zhipu-ai, baichuan-ai

LiteLLM

— LLM routing and cost optimization

LiteLLM proxies 100+ LLM APIs behind a single OpenAI-compatible interface — route completions to fast/cheap models (Groq + Llama) and chat to powerful models (Claude/GPT-4o) without changing application code.

Alternatives: openrouter, fireworks-ai, together-ai

ast-grep

— AST-based code search and refactoring optional

ast-grep performs structural code search and rewrite across repositories — essential for building refactoring features that understand syntax rather than just text patterns.

Qdrant

— Code vector search

Qdrant stores AST-chunked code embeddings with payload filters by file path, language, and symbol type — enables accurate semantic code search across millions of lines.

Alternatives: chroma, weaviate, pgvector, milvus

E2B

— Code execution sandbox optional

E2B runs AI-generated code in isolated sandboxes to verify correctness before presenting to users — the foundation for 'AI writes and tests the code' workflows.

Alternatives: modal-labs, replit

Instructor

— Structured LLM outputs for code edits optional

Instructor validates LLM JSON outputs against Pydantic schemas — ensures code edit payloads (file path, start/end lines, replacement text) parse correctly without custom retry logic.

Alternatives: outlines, guidance, mirascope

Langfuse

— Prompt and completion tracing optional

Every completion and chat turn is traced with prompt template version, model, token count, and user ID — lets you compare model upgrades on real user queries before rolling out.

Alternatives: langsmith, braintrust, helicone

DeepEval

— Code generation evaluation optional

DeepEval's code-specific metrics (correctness via execution, test pass rate) let you benchmark model upgrades against a golden set of coding tasks before deployment.

Semgrep

— Security scanning for generated code optional

Semgrep scans AI-generated code for known vulnerability patterns (SQL injection, hardcoded secrets, insecure deserialization) before surfacing it to users — critical for trust in coding tools.

Gotchas

⚠️ Code context windows are expensive: a 2,000-file repository fully embedded and retrieved can easily inject 50k+ tokens per chat turn at $0.75+ per message with GPT-4o.
⚠️ Completion latency requirements (<100ms first token) are incompatible with GPT-4o — use Groq (Llama 3.1 70B) or Fireworks AI for inline autocomplete and reserve powerful models for chat.
⚠️ AST-chunking is language-specific — a generic text splitter will split function bodies mid-logic. Invest in Tree-sitter-based chunking early or retrieval quality will be permanently poor.
⚠️ AI-generated code that looks plausible but introduces security vulnerabilities is worse than no suggestion — implement at minimum a Semgrep scan before surfacing suggestions to users.
⚠️ DeepSeek Coder and Codestral are 10x cheaper than GPT-4o for code tasks with comparable quality on most benchmarks — benchmark your specific language/framework before defaulting to GPT-4o.
⚠️ Multi-file edits require transactional semantics — if the LLM generates edits to 5 files and file 3 fails validation, you need rollback logic. Most off-the-shelf frameworks don't handle this.