Developers

Developer-first, from the first request.

SchneeAI gives you a single REST interface for every model — plus prompt operations and observability primitives that work the same way across services. Ship AI features with the integration patterns you already use.

Design-partner phase. The public api.schneeai.com endpoint is activated at general availability. The examples below use https://api.schneeai.com as a placeholder — design partners receive a partner-specific endpoint and API key from us at onboarding. Replace the hostname in any snippet with your partner endpoint.

Quickstart

Send your first request through the AI Gateway.

curl https://api.schneeai.com/v1/chat/completions \
  -H "Authorization: Bearer $SCHNEEAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      { "role": "user", "content": "Summarize this report." }
    ]
  }'

The Gateway authenticates, applies your routing and limit policies, calls the selected provider, records structured usage, and returns the response.

New to SchneeAI? The Tutorial walks through streaming, named prompts, error handling, and cost tracking — copy-runnable code in curl, Python, and TypeScript.

REST API

The AI Gateway exposes an OpenAI-compatible chat completions endpoint, plus endpoints for prompt resolution, usage queries, and control-plane operations.

AreaEndpointPurpose
ChatPOST /v1/chat/completionsRoute a request to a model
PromptsGET /v1/prompts/{name}Resolve the active prompt version
UsageGET /v1/usageQuery structured usage records
BudgetsGET /v1/budgetsInspect budget consumption
AuditGET /v1/auditRead audit events

Full request and response shapes will live in the API Reference once the public surface stabilizes — currently in preparation.

Authentication

Requests use a bearer token issued by SchneeAI. Tokens carry a service, tenant, and user identity — so limits, logs, and access controls always reflect the caller. Never expose tokens in client-side code; route AI calls through your backend.

Examples

  • Chat endpoint — minimal request with model: "gpt-4o-mini" and a single user message. ("auto" — where the router picks per routing policy — is on the roadmap; see Failover design.)
  • Named prompt — call the Gateway with a registered prompt name and variables.
  • Budget-aware call — pass a feature tag so spend lands on the right budget.

SDKs (coming soon)

Go and TypeScript SDKs are on the roadmap. Today, integrate with any HTTP client — the REST API is the source of truth and SDKs will follow its shape.

Cost Calculator

Estimate per-request and monthly costs across major LLM providers with the token cost calculator. To scope the token count first, paste your prompt into the Token Counter & PII Scanner — it also flags any secrets or PII before the request leaves your boundary.

Developer tools

  • Model Directory — every model the Gateway routes to, with effective pricing and capability filters.
  • Prompt Template Library — production-ready prompts for summarization, RAG, code review, extraction, and PII masking. Copy and run across models.
  • Cost Calculator — per-token price comparison plus an interactive build-vs-buy TCO widget.
  • LLM Pricing Benchmark — indicative per-token prices across OpenAI, Anthropic, Google, Mistral, Cohere, DeepSeek, Meta, and xAI. Updated monthly.
  • Token & PII Scanner — tokenize prompts client-side and detect PII patterns before the request leaves your boundary.
  • Error Handling — HTTP status reference, SchneeAI-specific error codes, retry strategy, and idempotency for the chat completions endpoint.
  • API Request Builder — interactive builder that emits ready-to-run curl, Python, TypeScript, and Go snippets from the same form.
  • Service Status — live status of Gateway, Console, Vault, and upstream providers.

Stay in the loop

New endpoints, breaking changes, and release notes land on the changelog first. Email [email protected] with “Subscribe” in the subject and we’ll add you to the announcements list.


Developers

Prompt Template Library

Production-ready LLM prompt templates curated by the SchneeAI team. Summarization, classification, RAG, code review, structured extraction, and PII masking. Filter by category, copy, and try across models.

Read more →

API Reference

The SchneeAI API Reference is being prepared. OpenAI-compatible chat completions, prompt resolution, usage, budgets, and audit endpoints will be documented here once the public surface stabilizes.

Read more →

Model Directory

Filterable, sortable table of every model SchneeAI routes to. Compare context windows, latency, capabilities, and price (provider list, 1× passthrough during design-partner phase).

Read more →

Tutorial: Your First SchneeAI Request

Step-by-step tutorial: send your first chat completion, stream tokens, use a named prompt, handle errors, and track cost. Code in curl, Python, and TypeScript.

Read more →

Token Counter & PII Scanner

Paste text to estimate token count and detect PII / secret patterns (email, phone, credit card, API keys, JWT). All detection runs in your browser — nothing is sent anywhere.

Read more →

Token Cost Calculator

Estimate LLM API costs across Gemini, GPT-4o, Claude, and DeepSeek. During the design-partner phase, SchneeAI passes provider cost through at 1× — the platform fee is under adjustment.

Read more →

Error Handling

How SchneeAI reports errors: HTTP status codes, error response shape, retry strategy, rate-limit behavior, and idempotency for the chat completions endpoint.

Read more →

API Request Builder

Build SchneeAI chat completion requests interactively — pick a model, write system and user messages, set temperature and max tokens — and copy ready-to-run curl, Python, TypeScript, and Go code. No SDK install required.

Read more →

LLM Pricing Benchmark

Indicative per-token pricing across OpenAI, Anthropic, Google, Mistral, Cohere, DeepSeek, Meta, and xAI — with context windows and capability notes. Updated monthly. Verify against provider docs before SLA commitments.

Read more →