LangChain setup
Connect LangChain through an OpenAI-compatible endpoint.
TackleKey
RAG API cost
RAG cost comes from context size, retries, and hidden failure paths
A RAG app may call embeddings, rerankers, and chat models in one user flow. Use TackleKey project keys and logs to see which step actually costs money.
Execution order
| # | Action | Why it matters |
|---|---|---|
| 1 | Separate embedding and chat keys | Use project keys to isolate retrieval, generation, and evaluation traffic. |
| 2 | Measure context size first | Send a small query with a realistic context window and check token usage before a large corpus test. |
| 3 | Compare models by task | The cheapest chat model may not be the cheapest successful answer if it needs retries or longer prompts. |
| 4 | Inspect failed requests | Invalid model, base URL, and rate-limit errors often look like app bugs until you check logs. |
Related resources
Model directory
Check model pricing and endpoint support.
API troubleshooting
Debug base URL, key, model, and billing errors.
This is a scenario checklist, not a lowest-price or availability guarantee. Confirm live pricing, logs, and service boundaries before scaling.
Try TackleKey with one OpenAI-compatible change
Create an account, generate a project API key, then replace your client base URL with the TackleKey endpoint. Keep keys server-side and verify live pricing before scaling traffic.