Choosing a Domestic LLM: From Inference Servitization to Production Deployment

As generative AI transitions into production environments, selecting a Large Language Model (LLM) is no longer just a competition of accuracy metrics. It has evolved into a systematic engineering effort involving inference costs, servitization levels, ecosystem adaptation, and business scenario alignment. Recently, several domestic model vendors have intensively released new flagship products, with some already available via mainstream cloud inference platforms as API services. This signals that the competition among domestic LLMs is shifting from a “parameter scale race” into the deep waters of “engineering deployment capabilities.”

1. Inference Servitization: A Key Turning Point in Model Distribution

In the past, the primary path for developers to acquire open-source models was downloading weights, setting up inference environments, and managing GPU resource scheduling. While flexible, this approach presents significant engineering barriers: memory management, concurrency control, cold start optimization, and security sandboxes often consume a team’s energy rather than driving core business innovation.

Today, a standardized distribution model represented by “model inference microservices” is changing this landscape. The core idea is to encapsulate models as API-callable services, with the platform side unifying resource pooling, elastic scaling, and version management. For enterprise users, this means no longer worrying about underlying infrastructure and focusing solely on business logic orchestration and performance tuning.

Notably, leading domestic models have been successively included in such inference service catalogs, supplied alongside mainstream global open-source models. This not only lowers the trial barrier but also indicates that domestic models have gained recognition from international platforms regarding engineering maturity. For technology selectors, this brings a direct benefit: the ability to quickly and cost-effectively compare the real performance of multiple models horizontally under the same evaluation toolchain.

2. Four Core Dimensions for LLM Selection

Faced with an array of models, making decisions based solely on floating-point scores on leaderboards is insufficient. Combined with developer community feedback and production practices, it is recommended to build an evaluation framework across the following four dimensions:

1. Task Alignment: Balancing Generalization and Specialization

Different models exhibit distinct design philosophies. Some flagship versions offer balanced performance in general conversation, code generation, and logical reasoning, making them suitable as enterprise-grade AI foundations. Others are deeply optimized for long-context understanding, agent tool calling, or multimodal alignment.

In practice, it is advisable to first map out internal high-frequency scenarios. For example, if the primary workload is code completion and repository-level understanding, priority should be given to the model’s performance in “multi-file context association” and “instruction-following accuracy.” If focusing on customer service scenarios, testing the model’s anthropomorphic expression of business terms and the rationality of its refusal strategies is crucial. There is no absolute best model, only the model that best matches the scenario.

2. Servitization Capabilities: Beyond API Availability

“Can be called via API” is merely the passing line. True servitization capability manifests in three layers:

• Concurrency and Latency: Under simulated production traffic, observe the fluctuation amplitude of TP999 (peak latency) rather than just looking at average response time.
• Observability: Whether the platform provides request logs, token consumption breakdowns, and error code explanations directly impacts troubleshooting efficiency.
• Ecosystem Integration: Whether the model is compatible with mainstream development frameworks (such as function calling, structured output, and vector retrieval plugins) determines subsequent integration costs.

It is recommended to establish a “Model Service Availability Checklist” containing the above metrics to complete systematic verification before selection.

3. Cost Structure: Calculating Compute and Business Costs Together

A model’s pricing model typically includes input/output token unit prices, concurrency reservation fees, and private deployment licensing fees. However, a low-cost model does not always result in the lowest total cost—if a model’s “pass rate” is low, it means more inference calls or manual corrections are needed, significantly increasing actual costs.

A more reasonable approach is to introduce the concept of “cost per effective task”: divide the total cost (including failed retries and post-processing) for the model to complete 1,000 standard business requests by the number of successful requests. This metric truly reflects the economic differences of various models within a business workflow.

4. Security and Compliance: Explainable Boundaries Matter More Than Promises

Domestic models generally have a solid foundation in Chinese content security and value alignment, but three details still require attention:

• Granularity of Refusal Strategies: Whether the model can distinguish between “absolute taboos” and contexts requiring “tactful expression.”
• Output Consistency: Whether the same question yields factual contradictions under different temperature parameters.
• Data Compliance: When using cloud inference services, it is necessary to confirm whether data is used solely for the current request processing and whether the platform supports private log isolation.

3. Selection Recommendations for Typical Scenarios

Scenario 1: Enterprise Knowledge Base Q&A

Selection Focus: Retrieval-Augmented Generation (RAG) capabilities, long-document understanding limits, citation traceability accuracy.

In this scenario, the model needs to accurately understand the semantic connection between embedded fragments and user questions, generating answers based on the given context. Recommended testing focus: when knowledge base fragments exceed the model’s context window, whether the model’s truncation strategy is reasonable; facing multi-hop questions, whether the model can actively trace relevant paragraphs rather than simply concatenating them. Some new-generation models have approached international leading levels in such tasks, but the gap is reflected in the refinement of resolving Chinese-specific coreferences.

Scenario 2: Software Development Efficiency Enhancement

Selection Focus: Code generation accuracy, cross-file refactoring capability, unit test generation quality.

R&D scenarios demand extremely high “tool calling” capabilities from the model. It requires not only writing correct function bodies but also understanding the entire project’s build configuration, dependency relationships, and coding style. During evaluation, a “commit message reverse inference” test can be used: given a git commit diff, ask the model to generate the commit description and complete missing test cases. This task effectively exposes the depth of the model’s understanding of code change intent.

Scenario 3: Marketing Content Batch Production

Selection Focus: Style consistency, multi-round rewriting capability, sensitive word avoidance.

In marketing scenarios, model “stability” is often more important than “creativity.” It is recommended to test whether the model can output multiple versions of copy for the same product selling point while maintaining the brand tone, without logical breaks or factual deviations. Additionally, attention should be paid to the model’s ability to understand platform audit rules—whether it can actively avoid extreme words and false advertising terms rather than relying on external filtering layers.

4. Establishing a Reusable Evaluation Benchmark

Regardless of the model chosen, it is recommended to build a “Minimum Viable Evaluation Set” internally, containing:

• 20 prompts from real business operations (covering instruction following, logical reasoning, knowledge Q&A, code writing, etc.).
• 5 sets of questions requiring the model to “admit it doesn’t know” to test its hallucination suppression capability.
• 3 sets of malicious inputs (such as prompt injection and data theft induction) to verify safety guardrails.

The evaluation process should be double-blind: evaluators do not know the specific source of the models and score only according to a unified scoring rubric. Only when a new model’s comprehensive score on the evaluation set exceeds that of the existing model by more than 10% is it worth initiating a migration cost assessment.

Conclusion: Rationally Viewing the “Flagship” Halo and Returning to Business Essence

The overall progress of domestic LLMs is evident, with the release frequency and capability limits of new models maintaining rapid growth. However, the ultimate goal of technology selection is not to chase the strongest parameters, but to find the “optimal solution” that can continuously and stably produce business value under specific resource constraints. It is recommended to establish a normalized mechanism of “quarterly evaluation - pilot application - gray-scale rollout,” transforming model selection from a one-time decision into a continuously optimized engineering practice. After all, models will iterate rapidly, but a solid evaluation methodology and clear requirement definitions are the anchors that keep teams decision-competent in the AI wave.