# Backmesh vs Groq Backmesh records 874 monthly visits with +965.9% month-over-month growth and Free Trial, Paid pricing. Groq records 3,987,936 monthly visits with +10% growth and Paid pricing. | Metric | [Backmesh](https://openaitools.app/tool/backmesh) | [Groq](https://openaitools.app/tool/groq) | | :--- | :--- | :--- | | Website URL | https://backmesh.com | https://groq.com | | Categories | AI API, Large Language Models (LLMs), Open Source AI Models | AI API, Large Language Models (LLMs), Open Source AI Models | | Pricing Model | Free Trial, Paid | Paid | | Monthly Visits | 874 | 3,987,936 | | MoM Growth Rate | +965.9% | +10% | | Primary Market | Nigeria | India | | Direct Visits | 394 | 1,702,515 | | Review Score | 4.3 | 4.7 | | Saved Count | 0 | 4 | | Supported Platforms | Website | Website | ## Backmesh Backmesh solves a specific and expensive problem: GitHub-hosted runners are convenient but costly, and typical self-hosted runner setups require you to operate autoscaling infrastructure yourself. With a single install, Backmesh connects your GitHub organisation to spot VM capacity across AWS, Google Cloud and Azure, and launches one VM per job. Runners and their autoscaler run inside accounts you control, so your clouds bill you directly for compute. Security is designed around not holding your data. Backmesh stores no cloud keys: the install connects each cloud using a role created from your own cloud CLI session. Code, secrets, caches and logs travel only between GitHub and your runners, never to Backmesh, and only one small event per job is sent — describing the runner without naming the repository, workflow, job or branch. If Backmesh itself has an outage of roughly half an hour, in-flight jobs finish and new ones keep starting. Commercially, Backmesh charges by jobs started rather than minutes, with a 15-day free trial on every tier and monthly or yearly billing. The site publishes comparisons against GitHub-hosted runners — for example 150,000 minutes a month costing about $1,800 on GitHub versus roughly $210–$260 on Backmesh for 4-vCPU Linux jobs. The platform supports Linux x86-64, private repositories, and AWS, GCP or Azure per workflow. **Pricing & Plans:** Subscription tiers priced by jobs started over a rolling 30-day window, with a 15-day free trial on every tier and up to 30% off on yearly billing; tier prices and job caps are listed on the pricing page. - **One install across three clouds:** A single Backmesh install receives your GitHub queued jobs and dispatches each to its own spot VM in AWS, Google Cloud or Azure. - **No cloud keys stored:** Cloud connections use roles created from your own CLI session, and Backmesh never receives your code, secrets, caches or logs. - **Cost visibility and control:** A status command reports runner VMs, queue times, 24-hour and 7-day job stats and month-to-date cost in one place. - **Per-job resource telemetry:** Every job log ends with peak memory, OOM kills, CPU and disk usage so you can right-size jobs. - **Resilient by design:** Running jobs complete and new jobs keep starting through a Backmesh outage of about 30 minutes. **Strengths:** Substantially lower CI compute cost than GitHub-hosted runners on equivalent workloads; Runners and autoscaler live in your own cloud accounts, with no cloud keys stored by Backmesh; Works across AWS, GCP and Azure from a single install with unified status reporting; Per-job resource telemetry helps size runners accurately **Limitations:** Linux x86-64 and private repositories only — no macOS, Windows, Arm or GPU runners; Each job waits for its own VM to boot, roughly 30 s on AWS and 60–70 s on GCP and Azure; A reclaimed spot VM fails its job and must be rerun; Each job starts with an empty Docker layer cache [Profile (Markdown)](https://openaitools.app/tool/backmesh.md) ## Groq Groq is an AI inference cloud that runs large language models and other AI models at exceptionally low latency. Instead of designing chips for training, Groq built custom silicon called the LPU, now joined by LPX, optimized specifically for inference, the stage where models actually serve users. The company positions itself as a 'neocloud' for fast inference and combines that hardware with a software stack and developer APIs. Developers access Groq through an API that hosts popular open models such as Llama, Mixtral, Gemma, and Whisper speech models, along with text-to-speech capabilities. Because the platform is tuned for speed, it is often used for real-time chat, agents, and voice interfaces where response time matters. The API is broadly compatible with common OpenAI-style request formats, which lowers the cost of switching. Groq has scaled aggressively, announcing a $350 million Series A and building hundreds of megawatts of inference capacity. It highlights reliability and cost alongside speed, arguing that fast and affordable no longer need to be a tradeoff for teams running AI workloads in production. **Pricing & Plans:** Paid, usage-based inference API. Groq charges per token for hosted models, with free developer tiers and enterprise capacity options; check the official pricing page for current rates. - **LPU and LPX inference hardware:** Custom accelerators designed specifically for inference rather than training, tuned for very low latency. - **Open model catalog:** Hosted access to popular open models including Llama, Mixtral, Gemma, and Whisper speech models. - **Fast developer API:** An API broadly compatible with common OpenAI-style request formats for quick integration. - **Speech and audio models:** Automatic speech recognition with Whisper plus text-to-speech capabilities for voice applications. - **Scales for production:** Capacity built for high-volume production workloads with an emphasis on reliability and cost. **Strengths:** Very fast inference latency for real-time applications; Broad catalog of popular open-source models; OpenAI-compatible API reduces migration effort; Managed cloud removes the need to run your own GPUs **Limitations:** Focus is inference only, not model training; Pricing is usage-based, so costs scale with traffic; Availability and model selection depend on the hosted catalog [Profile (Markdown)](https://openaitools.app/tool/groq.md) ## Frequently Asked Questions ### How do Backmesh and Groq compare in monthly traffic? Backmesh has 874 monthly visits, while Groq has 3,987,936. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Backmesh has a month-over-month growth rate of +965.9%; Groq has +10%. ### How does pricing compare? Backmesh: Free Trial, Paid. Subscription tiers priced by jobs started over a rolling 30-day window, with a 15-day free trial on every tier and up to 30% off on yearly billing; tier prices and job caps are listed on the pricing page. Groq: Paid. Paid, usage-based inference API. Groq charges per token for hosted models, with free developer tiers and enterprise capacity options; check the official pricing page for current rates. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | High-efficiency open-weight and hosted reasoning AI powered by DeepSeek-V4 and R1 at ultra-low token costs. | [.md](https://openaitools.app/tool/deepseek.md) | | 2 | [OpenAI Platform](https://openaitools.app/tool/openai) | 234,678,639 | -9.6% | Paid | Proprietary frontier models with powerful reasoning and multimodal features, but lacking open-weight model hosting and custom inference speeds. | [.md](https://openaitools.app/tool/openai.md) | | 3 | [Google AI Studio](https://openaitools.app/tool/google-ai-studio) | 112,620,020 | +2% | Freemium | Google's rapid prototyping IDE and developer API workbench for building multimodal applications with Gemini models. | [.md](https://openaitools.app/tool/google-ai-studio.md) | | 4 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Unified API gateway aggregating hundreds of models across providers, but acting as a router rather than direct hardware host. | [.md](https://openaitools.app/tool/openrouter-ai.md) | | 5 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | The AI developer cloud for GPU pods, serverless inference and clustering | [.md](https://openaitools.app/tool/runpod.md) | | 6 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Robust cloud platform for open-source model inference, offering extensive fine-tuning support but slightly slower raw speeds. | [.md](https://openaitools.app/tool/together-ai.md) | | 7 | [Browser Use](https://openaitools.app/tool/browser-use) | 1,206,376 | +41.1% | Freemium, Paid, Contact for Pricing | Browser agents and stealth cloud browsers for real web tasks | [.md](https://openaitools.app/tool/browser-use.md) | | 8 | [Modal](https://openaitools.app/tool/modal-com) | 1,196,021 | +30.2% | Freemium, Contact for Pricing | Serverless cloud infrastructure for AI and data workloads | [.md](https://openaitools.app/tool/modal-com.md) | | 9 | [OrcaRouter](https://openaitools.app/tool/orcarouter) | 975,300 | -5.2% | Freemium | One OpenAI-compatible gateway for adaptive LLM routing, guardrails, and spend control | [.md](https://openaitools.app/tool/orcarouter.md) | | 10 | [Cerebras](https://openaitools.app/tool/cerebras) | 701,013 | -12.6% | Contact for Pricing | Wafer-scale hardware cloud delivering ultra-fast token generation speeds comparable to Groq, but supporting a more specialized model catalog. | [.md](https://openaitools.app/tool/cerebras.md) | --- Source: https://openaitools.app/compare/backmesh-vs-groq More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)