Choosing a Domestic LLM for Inference Deployment: An Objective Technical Framework

1. Why Model Selection is Shifting from “Benchmarks” to “Engineering”

Over the past year, the focus in the LLM space has noticeably shifted. Early on, the primary concerns were parameter counts, benchmark scores, and one-off generation quality. Today, an increasing number of teams realize that a model’s viability for production depends on whether it offers a stable, observable, and scalable inference service architecture.

Against this backdrop, simply looking at a model’s ranking on public evaluation datasets is no longer sufficient. More practical questions include:

• Can the model be deployed within existing infrastructure, rather than requiring the team to rebuild the entire inference stack?
• Do inference latency and throughput meet the requirements of specific business scenarios, such as online chat, batch extraction, or code completion?
• Does it provide a unified interface protocol, authentication methods, and streaming output capabilities?
• Is it easy to manage permissions, track usage, audit, and allocate costs?
• When the model version is upgraded, can the business side switch smoothly without requiring massive refactoring?

Therefore, rather than asking “which LLM should we choose,” it is better to first establish an evaluation framework tailored to your team. This framework should cover five layers: model capability, inference platform, integration cost, operational complexity, and ecosystem compatibility.

2. What Problems Does an Inference Platform Solve?

Many teams initially attempt to self-host open-source models. However, as business scale grows, the issues with self-built inference services become glaring:

1. Low resource utilization: GPU resources are difficult to schedule dynamically, leading to shortages during peak hours and significant idleness during off-peak times.
2. High iteration costs: Changing a model version might require rewriting service code or adjusting inference configurations.
3. High engineering requirements: Features like streaming output, timeout control, request queuing, concurrency limits, and failure retries must be implemented from scratch.
4. Complex security and compliance: Access control, log retention, key management, and content safety filtering are hard to quickly establish.

The emergence of inference platforms essentially encapsulates these complexities within a unified API and service layer. For developers, an inference platform acts as a “model service gateway”: the underlying layer can connect to different model providers, while the upper layer exposes a standardized calling protocol.

A typical enterprise inference platform usually provides the following capabilities:

• Unified model list and version management;
• Standardized REST interfaces or SDKs;
• Support for both synchronous and streaming generation modes;
• Token-level usage statistics and cost breakdowns;
• Support for private network access, key rotation, and permission isolation;
• Configurable content filtering, sensitive word interception, and audit logs.

Understanding this, when evaluating different model integration methods, the focus should no longer be on whether the model itself is “listed,” but rather on whether the inference platform has solved the engineering problems the business truly cares about.

3. Six Technical Dimensions for Model Selection

1. Model Capability and Task Alignment

Different models can perform very differently on different tasks. A model that excels at general conversation might not be suitable for structured information extraction; a model with strong code generation capabilities might not be ideal for long-text summarization.

During evaluation, you should prepare a small evaluation set based on your own business, covering the following task types:

• Short text classification and tag generation;
• Long document summarization and key information extraction;
• Multi-turn dialogue and tool calling;
• Code generation and completion;
• JSON output under format constraints.

The evaluation set does not need to be massive; typically, 50 to 200 samples per task is sufficient. The key is that the samples must come from real business scenarios, not public benchmarks.

2. Inference Performance and Cost Structure

Inference performance directly impacts user experience and unit costs. When evaluating, pay attention to the following metrics simultaneously:

• Time to first token (TTFT): Affects the perceived response speed by the user;
• Generation speed: Tokens generated per second;
• Maximum context length: Whether it supports long document inputs;
• Concurrency capacity: The number of requests a single model instance can handle;
• Cost structure: Whether billing is per token or per API call, and whether input and output are priced separately.

For real-time interactive scenarios, TTFT is usually more important than overall generation time. For batch processing scenarios, throughput and unit token cost are more critical.

3. Inference Platform Compatibility and API Standardization

Regardless of which model you choose, it is recommended to prioritize solutions that can be accessed via unified APIs. Standardized interfaces mean:

• Business code is not locked into a specific model;
• Model upgrades or replacements are smoother;
• You can migrate between different environments, such as testing and production.

In engineering implementation, it is common practice to abstract a model invocation client at the business layer, rather than directly using a proprietary SDK from a specific model provider in the business logic. This way, even if the model is replaced in the future, only the adaptation layer needs modification.

4. Security, Compliance, and Data Pipeline

Enterprise-grade applications typically have higher data security requirements than individual developers. You need to verify whether the inference platform supports:

• Private network access;
• Data transmission encryption;
• Fine-grained key permission management;
• Request log retention and auditing;
• Content safety filtering and sensitive information identification;
• Data residency or deployment in specific regions.

These capabilities are often more decisive than a model’s benchmark score in determining whether a solution can pass internal enterprise reviews.

5. Model Updates and Long-Term Maintenance

LLMs iterate rapidly. A healthy integration strategy should allow teams to follow platform updates to new model versions without altering business code.

This point is highly correlated with the aforementioned API standardization. If the inference platform can provide a stable API contract, upgrades at the model level become transparent to the business side. The team only needs to focus on whether the output quality meets expectations, without worrying about changes in the underlying implementation.

6. Ecosystem and Troubleshooting Capabilities

Although LLM technology evolves rapidly, the community ecosystem and troubleshooting experience remain important. A widely used inference platform or integration method generally means:

• It is easier to find similar cases when encountering issues;
• There are more ready-made integration solutions and sample code;
• Bug fixes and feature updates for the platform itself are more timely.

This is why many teams prefer a slightly more expensive but more mature service over frequently troubleshooting infrastructure issues.

4. A Reusable Selection Process

Synthesizing the dimensions above, we can outline a selection process suitable for small to medium teams and enterprise developers:

Step 1: Define Business Scenarios and Constraints

List core tasks, latency requirements, concurrency scale, data compliance requirements, and budget limits.

Step 2: Prepare an Internal Evaluation Set

Extract samples from real business data covering the main task types. The sample size need not be huge, but it must be representative.

Step 3: Conduct Horizontal Validation via Unified APIs

If the inference platform already provides a unified interface, you can directly call different models under the same codebase, recording quality and performance metrics. Sample code is as follows:

def evaluate_model(client, dataset):
    results = []
    for sample in dataset:
        start = time.time()
        response = client.chat(
            messages=sample["messages"],
            temperature=0.2,
            max_tokens=512,
        )
        latency = time.time() - start
        results.append({
            "output": response["choices"][0]["message"]["content"],
            "latency": latency,
            "expected": sample["expected"],
        })
    return results

This approach avoids evaluation biases caused by SDK differences or inconsistent interfaces.

Step 4: Observe Stability and Exception Handling

Before finalizing production selection, it is recommended to conduct stress and exception testing for a period, such as:

• Stability with long text inputs;
• Whether timeouts or service degradation occur under high concurrency;
• Whether streaming output can recover from interruptions;
• Whether error codes and prompt messages are clear.

These details will directly impact subsequent troubleshooting efficiency.

Step 5: Confirm Operations and Cost Management Capabilities

Finally, check if the platform provides sufficient operational support, such as usage reports, quota management, and alert notifications. These capabilities allow the team to maintain visibility into costs and stability during usage.

5. Common Pitfalls

There are several pitfalls to be wary of during the model selection process:

Pitfall 1: Relying solely on benchmark rankings

Public evaluation sets only reflect a model’s performance on certain tasks and do not represent its actual effectiveness in your business scenario. Internal evaluation sets are often more valuable references.

Pitfall 2: Ignoring time to first token (TTFT)

Many teams focus only on generation speed but ignore the time from when a user sends a request to when they see the first character. For interactive products, this metric greatly impacts user experience.

Pitfall 3: Prematurely binding to a single model provider

If business code heavily utilizes a provider’s proprietary interface features, the subsequent switching costs will be very high. It is recommended to maintain a layer of abstraction at the business level.

Pitfall 4: Underestimating long-term operational costs

Self-hosting an inference service might seem to save on API call fees, but it requires continuous investment in human resources, resource scheduling, fault handling, and security compliance. A comprehensive assessment is necessary.

6. Conclusion

Returning to the original question: which LLM should you choose? A more appropriate phrasing might be: what inference capabilities does your business require, and at what engineering cost do you wish to obtain them?

When multiple LLMs already possess solid foundational capabilities, the decision focus should shift from “choosing a model” to “choosing an integration method.” A mature, stable inference platform with unified APIs is often more decisive for long-term development efficiency than a model’s short-term benchmark score.

For developers, the most pragmatic approach is to first define business constraints, establish an internal evaluation mechanism, conduct horizontal validation through standardized interfaces, and finally unify both model and inference platform selection into a single engineering assessment. The conclusions drawn this way will be much closer to the actual needs of a production environment.