OpenLIT VS Spanly
OpenLIT VS Spanly 对比，OpenLIT 和 Spanly 有什麼區別？
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總結
OpenLIT 總結
OpenLIT is an open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics in a single application 🔥 🖥 Open source GenAI and LLM Application Performance Monitoring (APM) & Observability tool
OpenLIT 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
OpenLIT 詳細信息
| 類別 | 大型語言模型 LLMs, AI 開發者工具, AI監控, 開源AI模型 |
| OpenLIT 網站 | https://openlit.io?utm_source=toolify |
| 添加時間 | 2024年7月11日 |
| OpenLIT 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OpenLIT？
Integrate OpenLIT into your LLM application by adding `openlit.init()` to start collecting data. You can also run `docker-compose up -d` to quickly start using the open-source tool.
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 OpenLIT 和 Spanly 的優點
OpenLIT 的核心功能
- Application and Request Tracing
- Detailed Span Tracking
- Cost Tracking
- Exceptions Monitoring
- Centralized Prompt Repository
- Secure Secrets Management (Vault Hub)
- OpenTelemetry Native Integration
- Granular Usage Insights
- Real-Time Data Streaming
- Low Latency
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
OpenLIT 的用例
- Visualize traces for application and request tracing to improve performance visibility.
- Monitor exceptions and troubleshoot issues with detailed stacktraces.
- Test and compare different LLMs side-by-side based on performance and cost.
- Manage prompts with versioning and variable substitution.
- Securely store and manage sensitive application secrets.
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
OpenLIT 和 Spanly 之間的計劃不同
OpenLIT
對不起，沒有數據
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.
OpenLIT 的流量
OpenLIT 是月访问量為 9.2K 且平均訪問時長為 00:00:09 的工具。 OpenLIT 的每次訪問頁數為 1.83，跳出率為 41.88%。
最新網站流量
| 月訪問量 | 9.2K |
| 平均訪問時長 | 00:00:09 |
| 每次訪問頁數 | 1.83 |
| 跳出率 | 41.88% |
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 OpenLIT are:India 31.13%, United States 29.97%, Russia 13.80%, Vietnam 10.56%, Germany 6.84%
Top 5 Countries/regions
 | 31.13% |
 | 29.97% |
 | 13.80% |
 | 10.56% |
 | 6.84% |
地理流量
對不起，沒有數據
網站流量來源
OpenLIT 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 |
OpenLIT 或 Spanly哪個更好？
OpenLIT 可能比 Spanly 更受歡迎。如您所見，OpenLIT 每月有 9.2K 次訪問，而 Spanly 每月有 0 次訪問。 所以更多的人選擇OpenLIT。 因此，人們很可能會在社交平台上更多地推薦 OpenLIT。
OpenLIT 的平均訪問持續時間為 00:00:09，而 Spanly 的平均訪問持續時間為 00:00:00。 此外，OpenLIT 的每次訪問頁面為 1.83，跳出率為 41.88%。 Spanly 的每次訪問頁面為 0.00，跳出率為 0.00%。
OpenLIT 的主要用戶是India, United States, Russia, Vietnam, Germany，分佈如下：31.13%, 29.97%, 13.80%, 10.56%, 6.84%。
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