Langtail VS Spanly
Langtail VS Spanly 对比，Langtail 和 Spanly 有什麼區別？
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總結
Langtail 總結
Langtail 著陸頁
Spanly 總結
Soon, more agents than humans will use your product via MCP. Spanly gives you full observability on the MCP server you ship: error rates, session traces, latency, client analytics, deploy alerts. Drop-in CLI or SDK. US & EU data residency. Built for SaaS engineering teams shipping MCP in production, alongside the Datadog, Sentry, or New Relic you already run.
Spanly 著陸頁
比較詳情
Langtail 詳細信息
| 類別 | 大型語言模型 LLMs, AI測試, 無程式碼與低程式碼開發, 提示詞工程, AI 開發者工具, AI API |
| Langtail 網站 | https://langtail.com?utm_source=toolify |
| 添加時間 | 2024年4月11日 |
| Langtail 定價 | -- |
Spanly 詳細信息
| 類別 | 大型語言模型 LLMs, AI監控, AI 開發者工具 |
| Spanly 網站 | https://www.spanly.com?utm_source=toolify |
| 添加時間 | 2026年6月30日 |
| Spanly 定價 | -- |
使用對比
如何使用Langtail？
Langtail can be used to test LLM prompts with real-world data, score tests with natural language, pattern matching, or custom code, and experiment with models, parameters, and prompts. It integrates with major LLM providers and offers a TypeScript SDK for developers.
如何使用Spanly？
To use Spanly, you can integrate it into your MCP server by dropping the open-source SDK into your TypeScript or Python code, wrapping your server binary using the Spanly CLI, or deploying it as a Docker sidecar. Once configured with your API key, it automatically captures and traces all MCP-shaped traffic, allowing you to view analytics, errors, and session traces directly via the web dashboard or from your IDE using Spanly's built-in MCP server.
比較 Langtail 和 Spanly 的優點
Langtail 的核心功能
- LLM Testing
- AI Firewall
- Prompt Management
- Collaboration Tools
- Data-driven Insights
Spanly 的核心功能
- Protocol-aware tracing capturing full JSON-RPC payloads (arguments, results, errors)
- Real-time session tracking grouping requests end-to-end
- Comprehensive performance metrics broken down by p50, p95, and p99 durations
- Automated error tracking and grouping across servers and clients
- US & EU regional data residency options with GDPR-friendly architecture
- IDE integration allowing engineers to search traces and triage errors from tools like Claude Code or Cursor
比較用例
Langtail 的用例
- Testing LLM prompts before deployment
- Blocking AI attacks and unsafe outputs
- Managing and refining prompts across teams
- Gaining insights from test results and analytics
Spanly 的用例
- Monitoring production MCP server error rates and performance bottlenecks
- Debugging failing tool calls by inspecting full payload arguments and response details directly
- Tracking down bad deployments by splitting performance metrics out by server and client versions
- Correlating AI agent interactions and sessions across complex multi-turn tool calling workflows
Langtail 和 Spanly 之間的計劃不同
Langtail
$0 / month + VAT
Unlimited users, 2 prompts or assistants, 1,000 logs per month, 30 days data retention, Public sharebable apps
$99 / month + VAT
1 user, 20 prompts or assistants, Unlimited logs, 90 days data retention, Public sharebable apps
$499 / month + VAT
10 users, Unlimited prompts or assistants, Unlimited logs, 1 year data retention, Public sharebable apps, Radars & Alerts, Dedicated support
Custom
Unlimited users, Unlimited prompts or assistants, Unlimited logs, Custom data retention, Public sharebable apps, Radars & Alerts, AI Firewall, Dedicated support, Self hosting
Spanly
$0/mo
100,000 MCP requests included (soft cap with sampling). Includes 30 days data retention, 2 seats, and multi-region ingestion. Designed for teams evaluating Spanly on a single server.
$41/mo
Billed annually at $492. Includes 100,000 MCP requests (overage from $6.00/100k decreasing with volume), 90 days data retention, unlimited seats, up to 10 alert rules, and public dashboards.
$210/mo
Billed annually at $2,520. Includes 100,000 MCP requests (overage from $6.00/100k decreasing with volume), 12 months data retention, unlimited seats, up to 100 alert rules, public dashboards, SAML & OIDC single sign-on, audit logs, and priority support.
$125/mo
Locked for 12 months for the 2026 cohort. Includes 1,000,000 requests, all Business features, a direct Slack Connect channel with the founder, roadmap input, and early access to new features in exchange for a monthly feedback call.
Custom Pricing
For volumes above 30M requests/month. Offers a custom per-request rate below $5.00/100k, dedicated support, and custom retention configurations.
Langtail 的流量
Langtail 是月访问量為 7.5K 且平均訪問時長為 00:00:04 的工具。 Langtail 的每次訪問頁數為 1.47，跳出率為 39.46%。
最新網站流量
| 月訪問量 | 7.5K |
| 平均訪問時長 | 00:00:04 |
| 每次訪問頁數 | 1.47 |
| 跳出率 | 39.46% |
Spanly 的流量
Spanly 是月访问量為 0 且平均訪問時長為 00:00:00 的工具。 Spanly 的每次訪問頁數為 0.00，跳出率為 0.00%。
最新網站流量
| 月訪問量 | 0 |
| 平均訪問時長 | 00:00:00 |
| 每次訪問頁數 | 0.00 |
| 跳出率 | 0.00% |
地理流量
The top 5 countries/regions for Langtail are:United States 23.49%, Germany 19.66%, Czech Republic 13.42%, India 11.76%, Brazil 9.02%
Top 5 Countries/regions
 | 23.49% |
 | 19.66% |
 | 13.42% |
 | 11.76% |
 | 9.02% |
地理流量
對不起，沒有數據
網站流量來源
Langtail 的 6 個主要流量來源是：郵件 0, vs_sourcesGenAi 0, 直接 0, vs_sourcesAffiliate 0, 引薦 0, vs_sourcesDisplayAds 0, vs_sourcesSearchPaid 0, vs_sourcesSocialPaid 0, vs_sourcesSearchOrganic 0, vs_sourcesSocialOrganic 0
郵件 | 0 |
vs_sourcesGenAi | 0 |
直接 | 0 |
vs_sourcesAffiliate | 0 |
引薦 | 0 |
vs_sourcesDisplayAds | 0 |
vs_sourcesSearchPaid | 0 |
vs_sourcesSocialPaid | 0 |
vs_sourcesSearchOrganic | 0 |
vs_sourcesSocialOrganic | 0 |
網站流量來源
Spanly 的 6 個主要流量來源是：郵件 0, vs_sourcesGenAi 0, 直接 0, vs_sourcesAffiliate 0, 引薦 0, vs_sourcesDisplayAds 0, vs_sourcesSearchPaid 0, vs_sourcesSocialPaid 0, vs_sourcesSearchOrganic 0, vs_sourcesSocialOrganic 0
郵件 | 0 |
vs_sourcesGenAi | 0 |
直接 | 0 |
vs_sourcesAffiliate | 0 |
引薦 | 0 |
vs_sourcesDisplayAds | 0 |
vs_sourcesSearchPaid | 0 |
vs_sourcesSocialPaid | 0 |
vs_sourcesSearchOrganic | 0 |
vs_sourcesSocialOrganic | 0 |
Langtail 或 Spanly哪個更好？
Langtail 可能比 Spanly 更受歡迎。如您所見，Langtail 每月有 7.5K 次訪問，而 Spanly 每月有 0 次訪問。 所以更多的人選擇Langtail。 因此，人們很可能會在社交平台上更多地推薦 Langtail。
Langtail 的平均訪問持續時間為 00:00:04，而 Spanly 的平均訪問持續時間為 00:00:00。 此外，Langtail 的每次訪問頁面為 1.47，跳出率為 39.46%。 Spanly 的每次訪問頁面為 0.00，跳出率為 0.00%。
Langtail 的主要用戶是United States, Germany, Czech Republic, India, Brazil，分佈如下：23.49%, 19.66%, 13.42%, 11.76%, 9.02%。
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