FOR DEVELOPERS

Elevence AI for Developers

One OpenAI-compatible API. 60+ models. Streaming SSE. Tools. Threads. Built for engineers who ship.

Applies to web, iOS and Android

If you build with AI, you're tired of the same problem: every provider has a slightly different API, billing surface, rate-limiting model, and quirks. Elevence AI is the single OpenAI-compatible endpoint that proxies GPT-5, Claude, Gemini, Llama, Mistral and 55 more — same request shape, one key, pay-as-you-go credits.

It's the developer surface we wanted ourselves: streaming SSE, threads, branching, function calling, multimodal inputs, real per-token cost analytics. No SDK to install — your existing OpenAI client works. No subscription minimums. No multi-vendor procurement.

What developers struggle with

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Multiple provider clients, multiple billing surfaces, multiple SDK versions to maintain.

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No good way to A/B compare model quality on the same prompt without writing a harness.

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Per-provider rate limits that fragment your throughput.

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Subscription fatigue: paying for ChatGPT Plus + Claude Pro + Gemini Advanced just to evaluate options.

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Backend logging across providers is inconsistent — token counts, costs, latency.

What you can do on Elevence AI

Drop in an OpenAI-compatible base URL

Point your existing OpenAI client at `https://api.elevence.ai/v1` and the code keeps working. Same response shape, same streaming protocol, same tool-call format. Switch the model identifier (`gpt-5` → `claude-4-sonnet`) and the response Just Works.

Branch on a model per environment

Use cheap models (Gemini Flash, Llama 4) in CI tests, premium models (GPT-5, Claude Opus) in production, all from the same code. Set the model via env var and let the API surface stay constant.

Run tool-using agents with confidence

Pre-built tools (web search, code execution, file search, knowledge bases) plus standard OpenAI function-calling. Build agents that span chat, retrieval, computation and external APIs — without rolling your own tool framework.

Get accurate per-token cost analytics

Every response includes the exact credit deduction broken down by input tokens, output tokens, and platform margin. Pipe it into your existing observability tooling.

Recommended models by task

Task

Model

Why

Code generation, refactoring

Claude Sonnet 4.6

Top code-quality benchmarks at moderate cost.

Hard reasoning, multi-step planning

GPT-5 (reasoning_effort: high)

Best frontier reasoning currently available.

High-volume extraction / classification

Gemini 3 Flash or Llama 4 70B

Excellent price-performance at scale.

Long-context analysis (1M tokens)

Gemini 3 Pro

Only frontier model with a 1M-token window.

Tool-calling agents

GPT-5 or Claude Sonnet 4.6

Most reliable structured-output and function-calling behaviour.

Example prompts

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"Refactor this Python function for readability without changing behaviour: <code>"

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"Given this 500K-token codebase, list all places where we call the auth service and explain the auth flow end-to-end."

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"Read this OpenAPI spec and generate a TypeScript client with full type safety."

Frequently asked questions

Is Elevence AI compatible with the official OpenAI SDK?

Yes. Both the Python `openai` and Node `@openai/sdk` clients work unchanged — just set `base_url` (or `baseURL`) to `https://api.elevence.ai/v1` and pass your Elevence API key. Streaming, tool calls, vision and multi-turn all behave identically.

What rate limits apply?

Default plans have generous shared rate limits. For high-throughput production workloads, Team and Enterprise plans get dedicated capacity. Per-org throttling is configurable; reach out if you need higher limits than the default.

How is data handled — is anything used for training?

No. Prompts and outputs are private to your account and are not sent to providers for training. Standard data-retention controls apply across providers.

Can I self-host or BYOK for specific models?

Today, all traffic routes through the managed API for unified billing and observability. BYOK (bring-your-own-key) is on the Enterprise roadmap. For open-weight models (Llama, Mistral), the weights are downloadable for self-hosted inference.

How are credits priced vs going direct to each provider?

Credits track the underlying provider rate plus a transparent flat platform margin. Total spend is within a few percent of going direct — and you get 60+ models in one billing surface instead of 6 separate ones.

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