Best Unified API Startups & Tools

GPTZZZ 是面向开发者的独立第三方 AI API 中转与多模型网关，提供 OpenAI 兼容接口和 Claude Messages 接口，帮助应用与编程工具通过对应协议接入模型。服务采用共享预付余额、按实际用量计费的方式，不代表任何模型厂商。 开发者可为所需分组创建 API Key，并在兼容客户端中配置服务地址。每个密钥对应一个分组，同一账户下的余额共用。公开价格页展示模型、分组与调用价格，具体可用范围和扣费以控制台实时信息为准。 网站提供中文接入文档、集成指南、错误码参考和公开服务状态页，便于核对配置、处理调用问题及了解运行状态。服务面向需要在应用和编码流程中调用模型的用户，与 ChatGPT 会员购买分别管理。 现行公开规则支持支付宝充值，单笔人民币十元至两千元，无充值手续费，未使用的余额可申请退款。充值金额是预付余额，不是固定月费；最低充值额也不等于单次调用价格。

Understanding Amazon's competitive landscape requires access to reliable product, keyword, and market data that most sellers can't gather manually. Ecommerce Data API addresses this gap by providing developers, Amazon sellers, and AI agents with structured access to product listings, search trends, sales estimates, customer reviews, and traffic patterns through a unified REST API and alternative integration methods. The platform organizes 45 distinct API endpoints across six functional categories, each designed to support specific business workflows. The product research suite enables ASIN lookups with detailed specifications, seller information, pricing, ratings, and competitive variant data. The keyword research set targets search volume analysis, traffic attribution, and ranking metrics. Market analysis tools quantify category size, product concentration, and demand trends. Traffic analysis reveals keyword sources and related product associations. The trademark module covers global intellectual property searches across multiple countries and registering bodies. A text recognition API rounds out the offering, handling optical character recognition for both Chinese and Latin scripts across image URLs, Base64, and file uploads. The architecture reflects thoughtful product design. API endpoints are grouped by business scenario rather than alphabetical listing, making navigation intuitive for users building specific research workflows. Request examples in the documentation include actual ASIN values and response payloads, reducing friction between signup and first API call. The platform supports not just traditional REST integration but also deployment as a remote Model Context Protocol server and as a reusable Agent Skill for AI-driven automation, broadening accessibility to non-traditional development environments. Transparency emerges as another distinguishing characteristic. The documentation explicitly acknowledges that coverage varies by operation and that sales figures represent estimates rather than confirmed data. This honesty about data quality limitations distinguishes the product from competitors that oversell accuracy. The user experience centers on self-service. Customers register independently, generate API keys through a secure dashboard, and monitor consumption metrics and error rates in real time. The platform supports key rotation for security management. For Amazon sellers building competitive intelligence workflows, cross-border sellers executing market research at scale, or developers integrating ecommerce research into larger applications, the breadth of accessible data points and flexible deployment options position this as a compelling alternative to building custom scraping infrastructure. The explicit acknowledgment of data limitations separates it from vendors making assured-accuracy claims they can't defend.

Simplifying access to multiple Model Context Protocol servers is a significant challenge for developers working with AI coding tools. Managing numerous API keys and reconciling invoices from various providers can be cumbersome. mcpmeter's solution is to offer a unified authentication and billing system, allowing users to interact with multiple MCP servers through a single proxy using one bearer key. What stands out about mcpmeter is its straightforward approach to authenticating, routing, and billing for MCP calls. The proxy sits between the agent and the publisher's MCP server, handling authentication, forwarding the JSON-RPC body, counting responses, and writing ledger rows. This pass-through design ensures that only relevant traffic is metered and billed. The proxy's performance is also noteworthy, with a P95 latency of 52ms, which is well below the target of 100ms. Key features of mcpmeter include its per-call metering and billing, with reconciliation happening nightly, and payouts made on the 1st of each month via Stripe Connect. The platform supports various AI coding tools and platforms, such as Claude, Cursor, and OpenAI. The fact that it provides a live ledger and statement, with every call recorded, adds to its transparency and auditability. mcpmeter's pricing model is based on a per-call charge, with no subscription fees, and is claimed to be 5 times cheaper than traditional API marketplaces. Publishers listing their MCP servers on the platform are charged a 10% platform fee, with payouts made monthly, subject to a $50 minimum. Overall, mcpmeter presents a compelling solution for developers and publishers looking to simplify their interactions with multiple MCP servers, offering a streamlined and cost-effective alternative to managing multiple API keys and invoices.

Building AI agents that can operate in the real world requires bridging the gap between digital systems and traditional communication channels. AgentCall solves a critical problem: enabling AI agents to interact via phone—both making outbound calls and receiving inbound communication—without the friction and failures that plague existing VoIP-based approaches. The core offering is elegant in scope. Developers provision real SIM-backed phone numbers through an API, connect their agents with a single API key, and receive all incoming calls and SMS messages through webhooks. The platform handles provisioning in seconds, supports country and capability selection, and guarantees that numbers pass strict platform verification checks that typically block VoIP alternatives. For AI agents, this means actually being able to register accounts, complete SMS-based verification flows, and operate in environments where traditional virtual numbers get rejected. What distinguishes AgentCall is how it handles the full communication stack. Voice calls aren't just passive; agents initiate outbound calls with AI-powered conversation using one of eight distinct voice options—from the neutral "Alloy" to the energetic "Shimmer"—each tuned for different contexts. The AI voice system accepts a system prompt and autonomously manages the conversation, returning a full transcript. This makes customer service outreach and verification workflows genuinely practical. On the messaging side, agents get a dedicated SMS inbox per number, send and receive messages, and automatically extract verification codes from incoming SMS, delivering them to webhook endpoints in real-time. The architecture reflects strong security thinking. Each agent gets its own isolated number, preventing compromise of one agent from cascading across others. The async, webhook-based design eliminates the need for persistent connections or complex state management. The platform supports diverse use cases: agents test SMS-based authentication on their own apps, run outbound calling campaigns with follow-up SMS, maintain two-way SMS conversations, and handle inbound calls through webhook forwarding. This breadth indicates the founders understood the landscape of agentic workflows rather than optimizing for a single scenario. The "Works with MCP" mention signals integration with the Anthropic Model Context Protocol, positioning AgentCall within the broader AI infrastructure stack. For developers building sophisticated AI agents that need reliable phone capabilities, AgentCall delivers what the market currently lacks—a practical alternative to the constraints and unreliability of virtual number services.