# Backmesh vs General Compute

Backmesh records 874 monthly visits with +965.9% month-over-month growth and Free Trial, Paid pricing. General Compute records 13,828 monthly visits with +7.2% growth and Free Trial, Contact for Pricing pricing.

| Metric | [Backmesh](https://openaitools.app/tool/backmesh) | [General Compute](https://openaitools.app/tool/general-compute) |
| :--- | :--- | :--- |
| Website URL | https://backmesh.com | https://www.generalcompute.com |
| Categories | AI API, Large Language Models (LLMs), Open Source AI Models | AI API, AI Models, Large Language Models (LLMs), AI Developer Tools |
| Pricing Model | Free Trial, Paid | Free Trial, Contact for Pricing |
| Monthly Visits | 874 | 13,828 |
| MoM Growth Rate | +965.9% | +7.2% |
| Primary Market | Nigeria | United States |
| Direct Visits | 394 | 6,732 |
| Review Score | 4.3 | 4.2 |
| Saved Count | 0 | 0 |
| 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)

## General Compute

General Compute describes itself as the inference cloud for alternative chips. Rather than reselling standard GPU instances, the company acquires hardware directly, places it in colocation data centres, and takes responsibility for everything around the silicon: cooling sized per chip, power delivery, fast links between racks, and the software layer that makes heterogeneous hardware usable. Customers then buy either bare metal or a managed endpoint with a tokens-per-second SLA, priced per contract according to term, volume, and site.

Its central technical idea is disaggregated inference. Prompt prefill is compute-heavy but relatively short, so it runs on NVIDIA GPUs. Token decode is latency-bound, so it runs on faster-purpose silicon, including Cerebras CS-3 wafer-scale systems, SambaNova, Positron AI, and d-Matrix accelerators. Splitting the two stages lets each rack serve more users while each user still receives tokens quickly; General Compute reports up to 2.8x the throughput per rack at a given per-user speed, and up to 16.2x end-to-end gains in its open benchmarks.

The company has raised a $100M Series A, works with chip vendors and colocation partners, and reports more than 30 MW of capacity, with deployments scheduled to go live from Q1 2027. It positions itself mainly for latency-sensitive workloads, especially agentic coding tools and other applications where many sequential model calls make per-token speed the real bottleneck.

**Pricing & Plans:** Pricing is contract-based and requires a call; it is quoted per engagement according to term, volume, and site rather than published as self-serve rates. Indications of free trial availability exist, but no public price list is shown.

- **Disaggregated prefill and decode:** Prompt processing runs on NVIDIA GPUs while token generation runs on faster ASICs from Cerebras, SambaNova, Positron AI, and d-Matrix.
- **Bare metal or SLA endpoint:** Buy racks of GPUs for prefill and ASICs for decode, with General Compute handling cooling, power, networking, and the orchestration software.
- **Per-chip specifications:** Inspect each accelerator in an interactive 3D view with its SRAM, power draw, cooling type, and the workloads it suits best.
- **Software for non-standard silicon:** The platform abstracts bespoke hardware so models run without customers having to learn a new chip toolchain.
- **Throughput SLA and failover:** Multi-provider redundancy and automatic failover for production inference, backed by contracted throughput guarantees.

**Strengths:** Purpose-built for very low token latency rather than generic GPU rental; Handles cooling, power, networking, and orchestration so customers don't have to; Mix of GPU prefill and ASIC decode can improve throughput per rack; Well funded ($100M Series A) with named chip and partner ecosystem

**Limitations:** Pricing is contract-based rather than published per-token rates, which slows down self-serve evaluation; No instant public signup for compute; access begins with a sales conversation; Newest deployments are scheduled from Q1 2027, so capacity is in ramp-up; Workloads need to fit the disaggregated prefill/decode model to get the full speed benefit

[Profile (Markdown)](https://openaitools.app/tool/general-compute.md)

## Frequently Asked Questions

### How do Backmesh and General Compute compare in monthly traffic?

Backmesh has 874 monthly visits, while General Compute has 13,828. 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%; General Compute has +7.2%.

### 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. General Compute: Free Trial, Contact for Pricing. Pricing is contract-based and requires a call; it is quoted per engagement according to term, volume, and site rather than published as self-serve rates. Indications of free trial availability exist, but no public price list is shown.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Groq](https://openaitools.app/tool/groq) | 3,987,936 | +10% | Paid | Ultra-fast AI inference on the LPU and LPX platform | [.md](https://openaitools.app/tool/groq.md) |
| 2 | [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) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all b | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Rent cloud GPUs by the second from a global marketplace | [.md](https://openaitools.app/tool/vast-ai.md) |
| 5 | [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) |
| 6 | [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) |
| 7 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | An AI-native cloud built from silicon to API for training, tuning, and serving models | [.md](https://openaitools.app/tool/nebius.md) |
| 8 | [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) |
| 9 | [Salad](https://openaitools.app/tool/salad-gpu-cloud) | 713,519 | +10.4% | Free Trial, Paid, Contact for Pricing | Distributed GPU cloud that orchestrates the world's idle consumer GPUs | [.md](https://openaitools.app/tool/salad-gpu-cloud.md) |
| 10 | [Cerebras](https://openaitools.app/tool/cerebras) | 701,013 | -12.6% | Contact for Pricing | The world's fastest AI inference, powered by the world's biggest chip | [.md](https://openaitools.app/tool/cerebras.md) |

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Source: https://openaitools.app/compare/backmesh-vs-general-compute

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