haimaker, a San Francisco-based AI infrastructure company, has launched a unified AI API Gateway that connects developers to more than 200 AI models through a single OpenAI-compatible endpoint. The platform, announced on June 27, 2026, aims to eliminate the integration overhead associated with working across multiple AI providers by offering a drop-in replacement compatible with any existing OpenAI-compatible workflow—no code rewrites required.
The release addresses a growing challenge for developers as the number of available large language models continues to expand: managing separate API integrations, credentials, and performance benchmarks across providers such as OpenAI, Anthropic, Mistral, and a broad range of open-source models. haimaker consolidates that complexity into a single endpoint and a single API key, enabling developers to reach models from multiple providers through one standardized interface.
Because the endpoint is fully OpenAI-compatible, teams already operating within OpenAI-compatible toolchains can adopt haimaker without altering their existing code. This architecture directly targets vendor lock-in, one of the more persistent friction points in production AI development. With haimaker, switching between providers or evaluating a different model requires no structural changes to an application—the same request format, endpoint, and API key apply across the entire model catalog.
Beyond aggregation, haimaker incorporates intelligent model routing as a built-in capability. Rather than requiring developers to manually test and compare models for each use case, the platform automatically identifies the most suitable model based on three configurable priorities: cost, response speed, or output quality. This capability is particularly relevant in production environments where token costs, latency targets, and task complexity differ across various parts of an application. For example, a developer building a chatbot might prioritize speed for conversational exchanges while routing document summarization tasks to a higher-capability model—all without writing custom routing logic. haimaker handles that decision layer automatically.
The platform also supports real-time model comparison, enabling developers to evaluate outputs across providers under live conditions rather than depending on static benchmarks that may not reflect actual performance in a specific context. This feature allows for informed decisions about which model to use for a given task.
haimaker is designed with developer velocity as a primary consideration. The integration path is intentionally direct: a single API key grants access to the full model catalog, and because the endpoint mirrors the OpenAI API structure, existing SDKs and tooling continue to function as expected. Developers do not need to learn a new interface to get started. Pricing is transparent, with costs tied to underlying model usage rather than a proprietary pricing layer that obscures what developers are actually paying for. This reflects a broader design principle within the platform—to surface information and control to the developer rather than abstract it away.
The Developer AI Tools available through haimaker include real-time model comparison, automatic routing configuration, and access to both commercial and open-source models within the same unified workflow. For teams building applications that require flexibility across model providers—or that anticipate their model strategy will shift over time—the platform provides the infrastructure to accommodate that without committing to a single vendor's ecosystem.
haimaker is available now through haimaker.ai, where developers can access the full model catalog and begin integrating through the unified endpoint. The platform's launch comes at a time when the AI landscape is increasingly fragmented, and the ability to quickly adapt to new models and providers can be a significant competitive advantage. For business and technology leaders, haimaker's offering reduces the risk of vendor lock-in and simplifies the process of scaling AI applications across multiple providers, potentially lowering costs and accelerating time to market.
