The model access layer for AI teams shipping to production.
One API. Every model. Production ready. EvoLink helps AI teams access, compare, and operate model APIs across LLM, image, video, and audio workloads — without rebuilding their stack every time the model market changes.
Why EvoLink Exists
Adding an AI model to a real product should be a deliberate choice — not a guess.
But today, the model layer is fragmented. Pricing is opaque across providers. APIs differ in shape, retry behavior, and async patterns. Providers go down on their own schedule. Bills don't match logs. What's supposed to be infrastructure ends up as a project that every team rebuilds from scratch.
EvoLink exists to give that layer back its shape. One API for every model. Clear docs and pricing before you integrate. Flexible enough to swap models as your needs change. Built for teams that have moved past "play with the model" and are now answering harder questions: how do I keep this stable, what does it actually cost per request, and how do I adopt the next model without a rewrite.
What We Believe
Clear model access.
See supported models, capabilities, and docs before you start integrating. No mystery wrappers. No surprise behavior. Every model has a documented surface — pricing, parameters, return shape, error codes — and you can read all of it before you ever create an API key. Browse supported models
Flexible model choices.
Move between GPT, Claude, Gemini, and image or video families without rewriting your stack. New frontier models ship every month. The whole point of EvoLink is that you can adopt them by changing one parameter — not by re-architecting. Compare model families
Built for real use.
We don't optimize for the demo. Async patterns, retries, callbacks, status, and provider fallback are first-class — because the difference between a prototype and a product is what happens on call number 10,000. Read the docs
What We're Building For
EvoLink is built for three jobs. Each one needs the same things — reliable access, clear costs, flexible model choices — but from a different angle.
For developers shipping production apps
You need predictable behavior, clear pricing, and a model layer that doesn't break when a provider does. One key, one SDK, every model behind it.
Read the API docs →For teams scaling AI features
You need to compare models without rewriting code, route traffic by quality or cost, and trust that what worked yesterday still works today.
Browse models →For media generation workflows
Production-ready async APIs for Veo 3.1, Sora 2, Kling, Seedance, Wan, and more. Task IDs, polling, webhook callbacks, retry handling, and clear cost-per-output.
Read the docs →How We Earn Trust
Trust isn't a tagline. Here's how we operate, and what we measure ourselves on.
Coverage
120+ models from 20+ providers — LLM, image, video, and audio — accessible through one EvoLink API. Drop-in compatibility with OpenAI, Anthropic, and Google SDK formats, so most existing code works with a base URL and key change. Browse models
Reliability
99.9% observed uptime across recent months, with provider monitoring and automatic failover when an upstream provider degrades. Routing overhead stays in the low tens of milliseconds based on our internal observations.
Cost transparency
Usage-based pricing on every model. Per-model prices are visible before you call. A real-time dashboard shows usage and costs as they happen — so the pricing page, the dashboard, and the bill all describe the same thing.
Data handling
EvoLink acts as a secure proxy. Prompts and responses are not stored. All traffic is encrypted in transit with TLS 1.3. Audit logs cover compliance metadata, not request content.
API keys
One EvoLink key replaces the individual provider keys you'd otherwise need to issue, rotate, and secure. Per-key usage tracking and revocation.
Who We Are
EvoLink is built by a team focused on AI model infrastructure, developer tooling, and production API operations. We work with teams that need reliable access to fast-moving AI models without rebuilding their stack every time the model market changes.
Our goal isn't to be the cheapest gateway. It's to be the model layer teams can keep building on as the frontier shifts.
Common Use Cases We See
These aren't customer quotes — they're patterns we see across teams using EvoLink in production.
AI video teams use EvoLink to run long-running generation jobs through async APIs with webhook callbacks, instead of holding polling connections open against multiple providers.
Agent and CLI teams use EvoLink as a configurable model backend, so they can switch between Claude, GPT, and other models for different workloads with a config change instead of a refactor.
SaaS engineering teams use EvoLink to consolidate per-model usage tracking, unified billing, and provider failover into one integration instead of three.
Teams migrating off direct provider APIs or other gateways adopt EvoLink for the same OpenAI-compatible interface they already have, with broader model coverage and a single dashboard.
We'll publish named customer stories as teams go public with their setups.
Where to go next
If you want to go deeper before deciding:
Join the community
We're building EvoLink in the open. Follow our progress, share what you're building, and tell us what's broken.