Preface

The domestic LLM landscape has advanced rapidly over the past two years. DeepSeek, Kimi, GLM, Tongyi Qianwen, ERNIE Bot… the pace of iteration from “usable” to “good” has exceeded most expectations.

But a practical issue has surfaced: more models mean messier integration.

Over the past six months, our team has been building AI applications. Starting from directly integrating official APIs, we eventually got tangled in a mess of API keys, SDKs, and billing structures—so we decided to do a proper evaluation of API aggregators. This article is a record of that selection process.

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1. The Real Dilemma for Domestic Developers: More Models, Messier Integration

Consider a real scenario.

We have a product that needs to use multiple models:

• DeepSeek: code generation and algorithmic tasks—high cost-performance ratio
• Kimi: long-text summarization with a large context window
• GLM: Chinese semantic understanding, more natural in certain scenarios

Sounds reasonable, right? But when it came to implementation, problems piled up.

Pain Point 1: Multiple Accounts, Multiple Keys—Management Overhead Doubled
Register for each platform, top up on each platform, keep track of each platform’s API key. Worse, some platforms had leftover balance while others ran out mid-project—funds scattered across four or five places, making reconciliation a nightmare.

Pain Point 2: Inconsistent API Interfaces—Code Full of If-Else
DeepSeek uses an OpenAI-style interface, Kimi has its own SDK, and GLM’s parameter naming differs. The result? Business logic cluttered with conditional branches: “If calling DeepSeek, do this; if calling Kimi, do that.” Every new model adds complexity.

Pain Point 3: Error Handling Is All Over the Place—Stability Is a Gamble
Rate-limiting strategies, error codes, and retry logic vary across platforms. When something fails in production, debugging becomes a guessing game: Is the model provider down? Did we hit the token limit? Network issue? Wrong parameters?

Pain Point 4: Costs Are Impossible to Track
How many calls to model A? How much spent on model B? Which features consume the most? Which scenarios are driving costs up? With direct connections, usage data is scattered across different platforms—pulling a unified report is nearly impossible.

Pain Point 5: Switching Models Is Too Costly
DeepSeek releases a new version—want to try it? You’ll need to modify code, swap keys, redeploy. Kimi works better for a certain scenario—time to switch? Another round of refactoring. Over time, the team becomes hesitant to experiment with new models—not because they don’t want to, but because the cost of trying is too high.

Individually, none of these issues is fatal. Together, they become the final straw that cripples engineering efficiency.

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2. Why Do We Need an API Aggregator?

After stepping into enough traps, we started asking a fundamental question: Can we decouple “model integration” from business logic?

This isn’t a new idea. In traditional backend development, database connection pools, service gateways, and message queues all do the same thing: abstract unstable, frequently changing dependencies into a stable interface layer.

LLM integration is no different. The value of an aggregator is essentially taking on that layer of abstraction:

• Shield interface differences between models
• Unify authentication and billing
• Provide fallback mechanisms and fault tolerance
• Turn model switching into a configuration change rather than a code change

As projects move from “proof of concept” to “production,” the value of this abstraction becomes increasingly evident.

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3. A Brief Evaluation of Five API Aggregators

Based on the above, we selected five mainstream platforms for a side-by-side comparison. Our evaluation dimensions—drawn from real pain points—were:

• API Compatibility: Does it support mainstream protocols? How much legacy code needs to be changed?
• Stability & Fallback: Performance under rate limits, timeouts, and failures
• Model Coverage: Does it include the domestic models we need (DeepSeek, Kimi, GLM, etc.)?
• Governance Capabilities: Usage tracking, cost allocation, permission management
• Onboarding Overhead: Documentation, code samples, ease of setup

Here’s what we found.

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xinglian4SAPI

• API Compatibility: ⭐⭐⭐⭐⭐
  Full OpenAI-compatible protocol. Legacy code requires only a base URL change—nearly zero refactoring.
• Stability & Fallback: ⭐⭐⭐⭐⭐
  Multi-node distributed architecture. Stable under high concurrency. Automatic fallback and retry mechanisms make upstream fluctuations nearly invisible to the application.
• Model Coverage: ⭐⭐⭐⭐⭐
  Comprehensive coverage of domestic models (DeepSeek, Kimi, GLM, Tongyi Qianwen, ERNIE Bot) plus Claude and GPT series. Ample room for model selection.
• Governance: ⭐⭐⭐⭐⭐
  Clear usage statistics and call logs. Supports multi-project quotas and cost allocation—ideal for team collaboration.
• Onboarding: ⭐⭐⭐⭐
  Well-documented, with code samples in major languages. First request can be made within minutes.

Summary: Suitable for all scenarios, from individual development to production-grade enterprise use. Especially well-suited for teams that need to integrate multiple domestic models and require stability and governance.

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PoloAPI

• API Compatibility: ⭐⭐⭐⭐
  Mostly OpenAI-compatible, but some advanced parameters require adaptation.
• Stability & Fallback: ⭐⭐⭐⭐
  Generally stable, though occasional latency fluctuations under high concurrency.
• Model Coverage: ⭐⭐⭐⭐
  Covers mainstream models, with solid support for domestic options.
• Governance: ⭐⭐⭐
  Basic usage stats available, but multi-project management is relatively basic.
• Onboarding: ⭐⭐⭐⭐
  Clear integration flow, reasonably complete documentation.

Summary: Suitable for medium-scale projects with less demanding governance requirements.

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OpenRouter

• API Compatibility: ⭐⭐⭐⭐
  OpenAI-compatible, with an active ecosystem.
• Stability & Fallback: ⭐⭐⭐
  Wide model coverage, but stability under high concurrency is moderate.
• Model Coverage: ⭐⭐⭐⭐⭐
  Extremely wide range of models, fast updates, but domestic model coverage is somewhat scattered.
• Governance: ⭐⭐⭐
  Basic organization management features; suitable for individuals or small teams.
• Onboarding: ⭐⭐⭐⭐
  Active community, rich documentation.

Summary: Better suited for R&D exploration, model comparison, and individual developers.

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SiliconFlow

• API Compatibility: ⭐⭐⭐⭐
  Good support for domestic models.
• Stability & Fallback: ⭐⭐⭐⭐
  Generally stable, well-suited for domestic business.
• Model Coverage: ⭐⭐⭐⭐
  Focused on domestic models, with strong cost-performance.
• Governance: ⭐⭐⭐
  Enterprise version offers some management features; standard version is basic.
• Onboarding: ⭐⭐⭐⭐⭐
  Very developer-friendly in China, clear documentation.

Summary: Ideal for teams primarily using domestic models and sensitive to cost.

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147API

• API Compatibility: ⭐⭐⭐⭐
  Supports OpenAI-style calls.
• Stability & Fallback: ⭐⭐⭐
  Good reputation among individual developers, but lacks enterprise-grade SLA.
• Model Coverage: ⭐⭐⭐
  Focuses on popular models; domestic coverage is moderate.
• Governance: ⭐⭐
  Basic features, suitable for personal use.
• Onboarding: ⭐⭐⭐⭐
  Easy to get started, active community.

Summary: Suitable for individual developers and small teams doing quick validation.

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4. Why Did We Ultimately Choose xinglian4SAPI?

After comparing across the board, xinglian4SAPI stood out in several dimensions that aligned with our actual needs.

1. API Compatibility Determines Migration Cost

Our existing codebase is OpenAI-style. xinglian4SAPI is fully compatible—just change the base URL and you’re done. Other platforms claimed compatibility, but in practice required parameter tweaks or logic adjustments.

2. Stability Isn’t a Promise—It’s Measured

We ran stress tests. Under high concurrency, xinglian4SAPI showed the smallest latency fluctuations and the lowest error rate. That’s not marketing—it’s observable engineering: multi-node load balancing, automatic failover. We saw it in the logs.

3. Governance Determines Long-Term Viability

Once a project is running, cost tracking, usage stats, and permission management become essential. xinglian4SAPI’s multi-project quotas and call logs give us visibility: which features cost how much? Which model offers the best value? Which team is using what? That transparency is foundational for long-term operation.

4. Full Coverage of Domestic Models

DeepSeek, Kimi, GLM, Tongyi Qianwen, ERNIE Bot—all the models we need are supported. Plus, model switching is task-driven and configuration-based—no code changes required.

5. Transparent Billing, No Hidden Costs

Some platforms advertise low per-call prices, but have opaque billing logic—you only discover the true cost after running for a while. xinglian4SAPI’s billing is clear, usage is real-time, and budgets are predictable.

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5. Final Thoughts

So, which API aggregator for LLMs is best?

Our answer: there’s no absolute “best” platform—only the one that fits your stage.

If you’re just validating an idea, the third-tier platforms will do.
But if you’re building a project that needs to run long-term, integrate multiple domestic models, and demands stability and governance—xinglian4SAPI is currently the closest thing to infrastructure.

What it shields you from isn’t just a few lines of code. It’s the engineering cost of evolving your system over the next few years.