QVeris vs Requesty
Optimize the Model, Then Choose the Action
QVeris 与 Requesty：先优化模型，再选择行动
Requesty optimizes how model requests reach providers under routing, budget, cohort, key, and observability controls. QVeris chooses which external capability should perform the next action. Treat them as sequential decisions, not competing routers.
Requesty 在路由、预算、分组、密钥与可观测性控制下优化模型请求如何到达供应商；QVeris 决定哪个外部能力执行下一步行动。二者是连续决策，而非相互竞争的路由器。
TL;DR
Use model and provider routing, failover, budgets, sticky cohorts, experimentation, BYOK, and observability to improve model cost and reliability.
Use capability discovery, inspection and governed calls to choose an API, tool, dataset, or service that fulfills the agent's intent.
Model tokens and provider markup belong to inference; API quotas, per-call charges, side effects and accepted results belong to capability execution.
Preserve model policy and cohort decisions, then append capability rank, schema, price estimate, execution ID, downstream receipt, and outcome.
通过模型/供应商路由、回退、预算、粘性分组、实验、BYOK 与可观测性改善模型成本和可靠性。
通过能力发现、检查与受治理调用，选择满足智能体意图的 API、工具、数据集或服务。
模型 Token 与供应商加价属于推理；API 配额、单次调用费用、副作用与可接受结果属于能力执行。
保留模型策略与分组决策，再附加能力排序、结构定义、价格预估、Execution ID、下游回执与结果。
Cost-aware model routing stops before action selection 成本感知模型路由止于行动选择之前
Requesty's job is model-access optimization. Its gateway positioning covers unified model access, routing and fallbacks, budgets, analytics, observability, prompt-related workflows, BYOK and traffic segmentation such as sticky routing or experiments.
Requesty 的任务是优化模型访问。其网关定位涵盖统一模型访问、路由与回退、预算、分析、可观测性、提示相关工作流、BYOK 以及粘性路由或实验等流量分组。
QVeris begins at the next question. Once a model understands the task, Discover finds candidate capabilities, Inspect compares their schemas and signals, and Call executes under a recorded session. The best model route does not determine the best business API.
QVeris 从下一个问题开始。模型理解任务后，Discover 寻找候选能力，Inspect 比较其结构定义与信号，调用在有记录的会话下执行。最佳模型路由不能决定最佳业务 API。
QVeris vs Requesty side by side QVeris 与 Requesty 并排比较
| Decision surface 决策面 | QVeris | Requesty |
|---|---|---|
| Routing target 路由目标 | External APIs, tools, data and services 外部 API、工具、数据与服务 | Models, providers and inference endpoints 模型、供应商与推理端点 |
| Optimization objective 优化目标 | Capability match, contract, policy, reliability, latency and call cost 能力匹配、契约、策略、可靠性、延迟与调用成本 | Inference price, quality, latency, availability, budget and experiment 推理价格、质量、延迟、可用性、预算与实验 |
| State 状态 | Search, execution and workflow session Search、Execution 与工作流会话 | User/cohort stickiness, routing policy and gateway request 用户/分组粘性、路由策略与网关请求 |
| Keys 密钥 | Governed credentials for downstream capabilities 下游能力的受治理凭证 | Provider keys or managed model access 供应商密钥或托管模型访问 |
| Failure boundary 故障边界 | Schema, authorization, downstream state, idempotency and result 结构定义、授权、下游状态、幂等性与结果 | Provider error, rate limit, timeout, model quality and usage 供应商错误、限流、超时、模型质量与用量 |
| Cost evidence 成本证据 | Estimated and billed capability execution 能力执行预估与实付 | Model/provider usage and gateway attribution 模型/供应商用量与网关归因 |
Route two decisions with two explicit objectives 用两个明确目标路由两个决策
Your immediate bottleneck is model spend, provider resilience, cohort consistency, experimentation, BYOK, or inference observability.
The agent needs to find and invoke live external capabilities whose schema, policy, reliability, region, and cost vary.
The workflow must optimize inference spend and still prove why a particular external action was selected and executed.
当前瓶颈是模型支出、供应商韧性、分组一致性、实验、BYOK 或推理可观测性。
智能体需要发现并调用结构定义、策略、可靠性、区域与成本各不相同的实时外部能力。
工作流既要优化推理支出，也要证明为何选择并执行某个外部行动。
Keep model cohorts separate from action idempotency 模型分组与行动幂等性必须分离
Requesty may keep a user or experiment cohort on a consistent model route. Pass that route evidence into the agent context, but let QVeris independently rank capability candidates. For calls that change state, use action-specific idempotency keys and approval policy; never reuse model request retries as permission to repeat an external action.
Requesty 可以让用户或实验分组保持一致模型路由，并把该路由证据传入智能体上下文；但能力候选应由 QVeris 独立排序。对于会改变状态的调用，使用动作特定的幂等键与审批策略；不要把模型请求重试当作重复外部行动的许可。
Architecture rule: a cheap repeated model call is an efficiency issue; a repeated refund, order, or message can be a business incident.
架构规则：重复的廉价模型调用是效率问题；重复退款、下单或发消息可能是业务事故。
A cost and side-effect proof 成本与副作用验证
- Run fixed and variable model routes across cost, latency, quality, privacy, cohort and failover policies.
- Execute read-only and state-changing capabilities with explicit approvals, idempotency keys and rollback paths.
- Force a model retry immediately before a capability timeout and prove the downstream action occurs at most once.
- Join inference cost, capability estimate, downstream bill, accepted result, and human-review cost in one record.
- Verify sticky cohorts do not override region, policy, schema, or capability eligibility constraints.
- 按成本、延迟、质量、隐私、分组与回退策略运行固定和可变模型路由。
- 用明确审批、幂等键和回滚路径执行只读与改变状态的能力。
- 在能力超时前强制模型重试，并证明下游动作最多发生一次。
- 在同一记录中连接推理成本、能力预估、下游账单、可接受结果与人工审核成本。
- 验证粘性分组不会覆盖区域、策略、结构定义或能力资格约束。
Migrate policy and evidence independently 独立迁移策略与证据
Export model routes, cohort keys, provider credentials, budgets, usage and traces separately from capability schemas, credentials, search policies and execution records. Define a shared workflow envelope, dual-write both ledgers, then canary inference and action paths independently.
将模型路由、分组键、供应商凭证、预算、用量与调用链，同能力结构定义、凭证、搜索策略与执行记录分开导出。定义共享工作流封装，双写两本账，再分别灰度推理与行动路径。
Capability routing completes cost-aware inference 能力路由补全成本感知推理
Requesty can lower or stabilize the cost of reaching a useful model response. QVeris carries the workflow into governed external action. Optimize both, but evaluate total cost per accepted business outcome rather than tokens or API calls alone.
Requesty 可以降低或稳定获得有用模型响应的成本；QVeris 把工作流推进到受治理的外部行动。两层都应优化，但应按可接受业务结果的总成本评估，而不是只看 Token 或 API 调用。
A Production Decision Scorecard for QVeris vs RequestyQVeris 与 Requesty的生产决策评分卡
For QVeris vs Requesty, the useful question is not which product has more checkmarks. It is which design gives the team the right boundary, evidence, operating model, and exit path for a defined workload.
针对“QVeris 与 Requesty”，真正有价值的问题不是哪款产品拥有更多勾选项，而是哪种设计能为明确工作负载提供正确边界、证据、运营模式和退出路径。
Map capability routing for actions and live data versus cost- and policy-aware routing of model inference across providers. Decide which component owns each decision, where policy is enforced, and whether the products are substitutes, complements, or overlapping layers.
梳理面向行动与实时数据的能力路由和跨供应商、成本及策略感知的模型推理路由。明确每项决策由哪个组件负责、策略在哪里执行，以及两者究竟是替代、互补还是部分重叠。
To validate QVeris vs Requesty, replay simple, long-context, streaming, structured-output, tool-calling, high-concurrency, and failure cases. Measure accepted-result quality, completion, p50 and tail latency, retries, trace completeness, and effective cost.
验证“QVeris 与 Requesty”时，重放简单、长上下文、流式、结构化输出、工具调用、高并发和失败案例，衡量合格结果质量、完成率、常规与长尾延迟、重试、追踪完整性和实际成本。
When evaluating QVeris vs Requesty, include hosting, regional capacity, data retention, identity integration, policy maintenance, upgrades, incident response, support, compliance evidence, and the custom adapters the team must keep current.
评估“QVeris 与 Requesty”时，纳入托管、区域容量、数据留存、身份集成、策略维护、升级、事故响应、支持、合规证据，以及团队必须持续维护的自定义适配器。
Before rolling out QVeris vs Requesty, version a neutral request and evidence envelope, shadow traffic, classify semantic differences, preserve trace identity, and prove a staged rollback. Prefer the option that keeps policy and workload contracts portable.
上线“QVeris 与 Requesty”前，版本化中立请求与证据封装，运行影子流量，分类语义差异，保留追踪身份，并证明可分阶段回滚。优先选择能让策略和工作负载契约保持可迁移的方案。
FAQ
No. It discovers, inspects, and calls external capabilities such as APIs, tools, services, and live data. A model gateway governs inference traffic.
Yes. Keep model selection in the gateway and capability selection in QVeris, then propagate trace, session, policy, and cost evidence across both.
Implement the bottleneck that blocks production, but define the shared evidence envelope before either layer becomes hard to replace.
Model route, provider, policy version, usage, QVeris search and execution IDs, chosen capability, schema version, estimated cost, result, and errors.
不是。QVeris 负责发现、检查并调用 API、工具、服务和实时数据等外部能力；模型网关负责治理推理流量。
可以。模型选择留在网关，能力选择交给 QVeris，并在两层之间传递调用链、会话、策略与成本证据。
先解决阻塞生产的瓶颈，但应在任一层难以替换之前定义共享证据封装。
模型路由、供应商、策略版本、用量、QVeris Search/Execution ID、所选能力、结构定义版本、预估成本、结果与错误。