When Free Becomes the Default: The Technical Logic Behind Large Language Models as No-Cost Infrastructure

In the past couple of years, the large language model industry has shown a curious pattern: some frontier models with parameter counts in the trillions are now being incorporated into mainstream development frameworks and agent platforms at zero or near-zero cost, becoming the default “workhorse” models for developers.

Many observers interpret this as a marketing tactic or a price war. But a closer look at the technical economics tells a different story. The confidence to offer such models for free comes from a fundamental shift in the cost structure of inference. This article breaks down that shift from three angles: model architecture, cost modeling, and engineering practice.

1. MoE Architecture: How Trillion Parameters Coexist with Cheap Inference

1.1 The Ceiling of Dense Models

In a traditional dense model, every token’s inference requires traversing the entire set of model parameters. Compute consumption scales roughly linearly with parameter count. That means any capability gain comes with a proportional increase in inference cost: multiply parameters by ten, and per-token compute grows by roughly the same factor.

As models scale toward hundreds of billions or even trillions of parameters, the inference cost of dense architectures becomes difficult to sustain. This is the root reason why early large language models carried high per-token pricing.

1.2 The Basic Principle of Sparse Activation

The Mixture of Experts (MoE) architecture offers an alternative path. The model’s intermediate layers — typically the feed-forward network (FFN) layers — are split into multiple expert sub-networks, with a lightweight router in front of them. For each incoming token, the router selects only a small number of the most relevant experts to perform computation; the rest remain inactive.

The process can be described in pseudocode:

def moe_layer(x, experts, router, top_k=2):
    # 1. The router scores each token and selects the top_k experts
    gate_scores = router(x)                    # [num_tokens, num_experts]
    topk_idx    = topk(gate_scores, k=top_k)
    topk_w      = softmax(gather(gate_scores, topk_idx))
    # 2. Only activated experts compute; outputs are weighted and summed
    y = zeros_like(x)
    for i, expert_id in enumerate(topk_idx):
        y += topk_w[i] * experts[expert_id](x)  # Non-selected experts stay idle
    return y

1.3 Total Parameters vs. Active Parameters: Two Numbers That Must Be Separated

This gives MoE models a distinctive dual-parameter structure:

• Total parameters define the upper bound of the model’s knowledge capacity. Frontier MoE models with total parameter counts in the trillions have already emerged.
• Active parameters determine the actual compute required for each inference pass. Such models may activate only around thirty-plus billion parameters per forward pass.

In other words, the model can “remember more knowledge” without proportionally increasing the cost of “answering one question.” Capacity and compute are decoupled. This is the first cornerstone of falling usage costs.

1.4 Direct Impact on Latency: Time to First Token (TTFT)

Online inference services typically return results as a stream. The most sensitive metric for user experience is Time to First Token (TTFT) — the interval between sending a request and receiving the first token. TTFT is driven primarily by the prefill phase, where the model processes the input prompt.

MoE sparse activation directly compresses prefill computation. Fewer parameters need to be computed, so the first token arrives sooner. In multi-turn agent scenarios, a low TTFT means each tool call and each conversational exchange feels nearly lag-free, approaching the responsiveness of local software. For interactive applications that aim for immersion, this metric matters as much as answer quality.

It is worth noting that MoE is not free of trade-offs. Although active parameters are few, all expert weights still need to reside in memory, so memory footprint scales with total parameter count. MoE also introduces engineering challenges such as load balancing across experts, expert parallelism across multiple accelerators, and communication overhead. These challenges mean that efficient MoE inference depends heavily on mature inference infrastructure — a key differentiator among engineering teams.

2. Long Context and the Caching Dividend: The Economics of Persistent Memory for Agents

2.1 Why Agents Naturally Need Persistent Memory

A typical agent application sends a large block of stable, unchanged content with every request: system instructions (role, boundaries, output specifications), tool and skill descriptions (function signatures, calling conventions), user profiles and preferences, and the goals and constraints of long-running tasks.

This content can easily occupy tens of thousands of tokens and remains identical across hundreds or thousands of calls. If every call had to repeat the prefill computation for this content and pay full price each time, the business model for agents would be nearly impossible. This has been one of the largest hidden costs preventing agent applications from scaling.

2.2 How Context Caching Works

Transformer autoregressive inference has two phases: prefill (processing the full input in parallel and generating the KV cache) and decode (generating tokens one by one). Context caching keeps the KV cache computed from a stable prefix in server-side memory. When a subsequent request shares that prefix, the cached result is reused directly, and only the newly added content is computed incrementally.

Frontier long-context models have widely expanded their context windows to the 256K-token level and natively support — or are compatible with — this caching mechanism. The combination of the two creates the complete “memory infrastructure”:

• The 256K window determines how much memory can be carried at once.
• Context caching determines how much it costs to carry that memory.

2.3 A Simple Cost Illustration

A simplified example shows the value of caching. The numbers are illustrative assumptions and do not represent any specific service’s billing rules.

Assume an agent application uses a stable prefix of 80,000 tokens and handles one million calls per day:

• Without caching: the system repeats prefill computation for roughly 80 billion tokens of stable prefix every day.
• With caching: prefill only happens when the cache is invalidated. Cached hits are typically billed at a rate significantly lower than standard input pricing, with marginal cost approaching zero.

In agent scenarios that rely heavily on long prefixes, estimates based on publicly available pricing suggest that total usage costs can drop to roughly one-fifth of equivalent flagship closed-source options. The higher the cache hit rate, the larger the gap.

2.4 Engineering Practice: Designing Layered Memory for Agents

Long context windows and caching alone are not enough. In practice, memory needs to be explicitly designed in layers:

class LayeredMemory:
    def __init__(self):
        self.working   = []                # Working memory: current conversation window
        self.session   = None              # Session memory: system prompt + user profile, kept in cache
        self.long_term = VectorStore()      # Long-term memory: external store, retrieved on demand
    def build_prompt(self, query):
        fixed_prefix = self.session_prefix()          # Stable prefix, cache-friendly
        recalled     = self.long_term.search(query, top_k=5)
        return fixed_prefix + recalled + self.recent_turns() + query

With this layering, the “stable part” is kept as large as possible, while the “changing part” is pushed toward the end of the prompt. This significantly improves cache hit rates. It is the new meaning of prompt engineering in the agent era: not just writing clearly, but writing in a cache-friendly way.

3. Open Ecosystems: Developers Vote with Call Volume

3.1 The Role of Model Aggregation Platforms

In recent years, model aggregation and routing platforms have emerged to unify multiple models behind a single compatible API. Switching models now costs developers little more than changing a model name in one line of code.

The public call-volume statistics from these platforms are, in a sense, the developer community voting with real usage — more revealing than benchmark leaderboards when it comes to engineering usability. A widely observed trend is that newly released high-quality open models can enter the top ranks of global call volume within days of launch, competing directly with established closed-source models. A few years ago this would have been unthinkable, when the experience gap between open and closed models left developers with almost no practical choice.

3.2 The Business Logic of the Free Default Model

When an agent framework announces a high-quality open model as its free default option, it may look like a loss-making move. In reality, each party gains something: the model provider receives real-world traffic and feedback, accelerating iteration; the platform gains low-cost, high-quality capability supply and lowers the barrier to entry for users; developers get a near-zero-cost testing ground.

In this context, “free” is not the destination but the starting point of ecosystem positioning. Once user habits form within a given ecosystem, downstream commercialization paths — value-added features, higher quotas, enterprise support — open up naturally.

4. What This Means for the Industry

4.1 For Developers: Innovation Costs Approach Zero

In the past, validating an agent idea could be enough to scare off independent developers with API bills alone. Today, experimentation costs are nearly negligible, and the innovation cycle is significantly compressed. Developers should redirect the saved costs toward differentiation: context engineering, tool ecosystems, and domain knowledge, rather than agonizing over model selection itself.

4.2 For Enterprises: Recalculating the Build-vs-Buy Boundary

The emergence of free and low-cost models has shaken the assumption that enterprises must build their own large models. The evaluation framework for technology selection should be updated around three questions: Do data compliance requirements allow using public APIs? Is call volume large enough that marginal costs would exceed the cost of self-hosting? Is the need for portability worth the additional engineering cost of multi-cloud adaptation?

For organizations with stricter compliance and control requirements, using model hosting and inference capabilities from cloud providers — deployed in private or dedicated cloud environments — has become a mainstream middle path between “pure API calls” and “pure self-hosting.” Baidu AI Cloud, for example, provides such model hosting and inference capabilities in China.

4.3 For the Industry: The Competitive Center of Gravity Is Shifting

As the capability gap between models narrows and acquisition costs converge, the center of competition will shift away from “the model itself” toward three areas: inference infrastructure (throughput, latency, cache hit rate, scheduling efficiency), context engineering (memory design, retrieval quality, cache friendliness), and ecosystem and service (toolchain completeness, stability guarantees, enterprise support).

In one sentence: the moat around model capability is getting shallower, while the moat around engineering capability is getting deeper.

5. Evaluation Guidance for Technical Teams

Instead of debating which model is stronger, build your own evaluation process. It should cover at least five dimensions, tested under realistic load:

1. TTFT and throughput: measure P50/P99 first-token latency with streaming requests.
2. Context and caching: confirm the target service’s window length, caching mechanism, and billing rules.
3. Unit cost: normalize to “cost per million tokens,” and always measure using your own real-world prefix length rather than official examples.
4. Tool-calling capability: the success rate and stability of structured output and function calling are hard requirements for agent scenarios.
5. Stability and SLA: rate limiting policies, peak-load behavior, and compensation clauses.

Here is a ready-to-use snippet for measuring TTFT:

import time
import requests
def measure_ttft(api_url, headers, payload, n=20):
    """Run n streaming requests and collect first-token latency distribution"""
    ttfts = []
    for _ in range(n):
        start = time.perf_counter()
        with requests.post(api_url, headers=headers,
                           json=payload, stream=True) as resp:
            for line in resp.iter_lines():
                if line:  # Record time when the first data chunk arrives
                    ttfts.append(time.perf_counter() - start)
                    break
    ttfts.sort()
    return {"p50": ttfts[len(ttfts)//2], "p99": ttfts[-1]}

Two things matter during load testing. First, use the same prompt structure as production, especially long prefixes. Second, distinguish between “cold start” (cache miss) and “warm request” (cache hit) — their TTFT and cost can differ by an order of magnitude.

Conclusion

The shift of large language models from “scarce per-token compute” to “free infrastructure” looks like a business strategy on the surface. Underneath, it is the inevitable result of maturing technologies: MoE sparse activation, long context windows, and context caching.

For practitioners, the real question is no longer “Can we afford large models?” but “Can we get context engineering, memory design, and inference optimization right?” The next round of competition will be decided in these unglamorous engineering details.