# Middleware vs RunLLM Middleware records 35,967 monthly visits with -12.3% month-over-month growth and Freemium, Contact for Pricing pricing. RunLLM records 182 monthly visits with -90.6% growth and Free Trial, Contact for Pricing pricing. | Metric | [Middleware](https://openaitools.app/tool/middleware) | [RunLLM](https://openaitools.app/tool/runllm) | | :--- | :--- | :--- | | Website URL | https://middleware.io | https://www.runllm.com | | Categories | AI Monitor, Log Management, Large Language Models (LLMs) | AI Agent, AI Customer Service, AI Assistant, AI Code Assistant | | Pricing Model | Freemium, Contact for Pricing | Free Trial, Contact for Pricing | | Monthly Visits | 35,967 | 182 | | MoM Growth Rate | -12.3% | -90.6% | | Primary Market | India | Global | | Direct Visits | 8,884 | 71 | | Review Score | 4.3 | 4.4 | | Saved Count | 1 | 0 | | Supported Platforms | Website | Website | ## Middleware Middleware consolidates the observability signals that usually live in separate tools. Infrastructure monitoring covers servers, containers, serverless functions and managed cloud services across AWS, Azure, GCP and Kubernetes. APM tracks application performance and traces slow requests down to the code path, while log monitoring with AI-assisted search makes it practical to debug high-volume log streams. Real user monitoring and synthetic monitoring add the outside-in view of how actual users and test probes experience the product. What differentiates the platform is OpsAI, an AI SRE agent that not only detects anomalies but correlates them across signals and proposes remediation. The vendor cites customers reducing debugging and resolution time dramatically, and positions the agent as helping fix problems in production rather than simply paging an engineer. LLM observability extends the same idea to AI applications, letting teams monitor and evaluate model calls in production. Operationally, Middleware provides a unified dashboard, alerting, a query language, notebooks, session replay, error tracking and continuous profiling, plus SLO monitoring. It integrates with the common cloud and Kubernetes environments and is presented as an alternative to Datadog, New Relic, Dynatrace, Grafana and Coralogix. A free tier makes it easy to start, with paid and enterprise plans for larger data volumes and support needs. **Pricing & Plans:** Free tier to get started, with paid plans and enterprise pricing available on request. - **OpsAI SRE Agent:** An AI agent that detects, investigates, and helps auto-fix production issues across infrastructure, APM and RUM signals. - **Unified infrastructure monitoring:** Monitor servers, containers, serverless workloads and cloud services across AWS, Azure, GCP and Kubernetes from one place. - **Log monitoring with AI insights:** Correlate and query high-volume logs assisted by AI so debugging does not rely on manual grep-style detective work. - **APM and distributed tracing:** Track application performance, trace slow requests, and inspect database and endpoint behaviour end to end. - **LLM observability:** Monitor and evaluate LLM application calls, latency, cost and quality alongside your traditional stack telemetry. **Strengths:** Single platform for infra, APM, logs, RUM and LLM observability; AI SRE agent aims at remediation, not just alerting; Free tier available to evaluate before committing; Explicitly positioned as an alternative to major legacy vendors **Limitations:** Full pricing for larger deployments requires contacting sales; Breadth of features can mean a longer initial setup; Advanced AI agent capabilities may vary by plan [Profile (Markdown)](https://openaitools.app/tool/middleware.md) ## RunLLM RunLLM, now operating under the Herald brand, has evolved from an enterprise technical-support assistant into what it calls an AI SRE: an agent that catches production issues before engineers are even paged. The pitch is that traditional observability is broken — you have to instrument every data stream, tune alert thresholds, and write runbooks for each failure mode, and anything nobody documented stays invisible. Herald takes a different approach. Before any alerts fire, it builds a context graph spanning your observability, codebase, CI/CD, documentation, and dependencies, so it learns what normal looks like. It then builds a custom anomaly detection model for each data stream — no thresholds to set — and surfaces validated signals before customers notice. When something looks wrong, the agent evaluates multiple hypotheses simultaneously against the right data sources and hands engineers a root-cause analysis in minutes, no runbook required. The company reports 100% RCA accuracy on known issues and over 70% on novel incidents for one of the world's biggest B2B2C platforms, and notes that the agent learns from every investigation so it never makes the same mistake twice. Herald was founded by PhDs and professors from UC Berkeley's RISELab, combining expertise in AI, LLMs, data systems, and scalable infrastructure. **Pricing & Plans:** Free trial with demos available; enterprise pricing is quoted on request via sales contact. Check the site's pricing and demo pages for current details. - **Context-aware learning:** Herald builds a context graph across observability, codebase, CI/CD, docs, and dependencies before alerts fire, so it understands what normal looks like. - **Predictive detection:** No thresholds to tune: Herald builds a custom anomaly detection model for each data stream and surfaces validated issues before customers notice. - **Automated investigation:** The agent evaluates multiple hypotheses at once against the right data sources and delivers root-cause analyses in minutes without runbooks. - **Grounded in production:** 100% RCA accuracy on known issues and 70%+ on novel incidents, learning from every investigation so it never repeats a mistake. - **Fast onboarding:** Scans your codebase, finds the tools you use, and configures itself in minutes, adapting quickly to your stack. **Strengths:** Detects and investigates issues before alerts fire; No thresholds or runbooks to author and maintain; Correlates signals across code, infra, and telemetry; Built by UC Berkeley RISELab researchers [Profile (Markdown)](https://openaitools.app/tool/runllm.md) ## Frequently Asked Questions ### How do Middleware and RunLLM compare in monthly traffic? Middleware has 35,967 monthly visits, while RunLLM has 182. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Middleware has a month-over-month growth rate of -12.3%; RunLLM has -90.6%. ### How does pricing compare? Middleware: Freemium, Contact for Pricing. Free tier to get started, with paid plans and enterprise pricing available on request. RunLLM: Free Trial, Contact for Pricing. Free trial with demos available; enterprise pricing is quoted on request via sales contact. Check the site's pricing and demo pages for current details. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | The AI coding agent for building ambitious software | [.md](https://openaitools.app/tool/cursor.md) | | 2 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | Describe your idea and Replit builds a working app in minutes | [.md](https://openaitools.app/tool/replit.md) | | 3 | [Datadog](https://openaitools.app/tool/datadog) | 5,175,760 | -4.8% | Freemium, Free Trial, Paid, Contact for Pricing, Free | Unified observability, security and incident response for cloud-scale engineering teams | [.md](https://openaitools.app/tool/datadog.md) | | 4 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | The coding agent that learns your coding taste | [.md](https://openaitools.app/tool/command-code.md) | | 5 | [KaneAI](https://openaitools.app/tool/kaneai) | 901,340 | +13.2% | Freemium, Paid, Contact for Pricing | GenAI-native end-to-end QA agent that plans, writes and heals tests in plain language | [.md](https://openaitools.app/tool/kaneai.md) | | 6 | [Augment Code](https://openaitools.app/tool/augment-code) | 326,215 | +24.1% | Freemium, Free Trial, Contact for Pricing | Agent orchestration platform for your whole software development lifecycle | [.md](https://openaitools.app/tool/augment-code.md) | | 7 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) | | 8 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) | | 9 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Evaluate, monitor, and govern your AI applications in production | [.md](https://openaitools.app/tool/maxim-ai.md) | | 10 | [PromptLayer](https://openaitools.app/tool/promptlayer) | 85,899 | -4.6% | Free Trial, Contact for Pricing | The collaboration layer for AI engineering teams | [.md](https://openaitools.app/tool/promptlayer.md) | --- Source: https://openaitools.app/compare/middleware-vs-runllm More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)