Pick a workload and see the Pod stack it usually needs, which deployment fits, and which model categories sit inside it.
These architecture guides describe application responsibilities, model roles and evaluation criteria. They are reference patterns; your data, traffic and integration requirements determine the final configuration.
A chat model handles dialogue while your application manages session history, authentication and tool permissions. Limit retained history and define escalation paths. Evaluate task completion, first-token latency and behaviour when information is missing.
Connect an approved knowledge base and pass only the customer information needed for the task. Ticket creation and account changes require application-side authorization. Measure correct answers, human handoffs and policy adherence, not just conversation volume.
Your application chunks documents and maintains retrieval permissions and a vector database. Embeddings retrieve candidates; optional reranking improves selection before the LLM produces an answer. Evaluate source citations, retrieval recall and unsupported claims.
Extract text with OCR or a suitable document parser before requesting structured fields from an LLM. Validate output against a schema and retain source references. Review critical contractual or financial fields with a qualified person.
Select a code-capable model and control which repository files enter the prompt. Keep secrets out of context and execute generated code in a controlled environment. Compare test pass rates, edit quality and response time on your own codebase.
Define labels and a JSON schema before processing a batch. Use representative examples and an explicit unknown category. Track precision, recall, schema-valid outputs and cost per record; route low-confidence results for review.
A speech-to-text model converts audio; a separate language model can summarize or extract actions. Confirm audio format, consent and retention before ingestion. Evaluate word error rate, Turkish accents and speaker handling on real recordings.
The LLM proposes tool calls; your application validates arguments and executes allowed actions. Set permission boundaries, retry limits and audit logs. Evaluate successful completion, unintended actions and recovery from tool failures.
Latency, tokens, GPU load and cost, sliced by region, model and Pod. Illustrative figures are marked as demonstration data.
The console speaks the website's language: the same Pods, status dots, region codes and telemetry. Open the full console on its own artboard.
Inference billing should be as inspectable as the request itself. Tokens in, tokens out, GPU time, and the meter that follows.
Illustrative rates and limits, not a current offer. Final model availability, price, capacity, retention and SLA are defined in your deployment agreement.