# BlockRun.AI vs GroqCloud

> GroqCloud has a score of 75.7 (BB) against BlockRun.AI's 72.5 (BB). Both do llm inference. The largest gap is payments & pricing, 60 points. BlockRun.AI accepts x402. GroqCloud doesn't. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-04

GroqCloud has a score of 75.7 (BB) against BlockRun.AI's 72.5 (BB). Both do llm inference. The largest gap is payments & pricing, 60 points. BlockRun.AI accepts x402. GroqCloud doesn't.

- BlockRun.AI: grade BB, 72.5/100, rank #68 of 452. Markdown https://www.anchorterminal.com/tools/blockrun-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/blockrun-ai.json
- GroqCloud: grade BB, 75.7/100, rank #31 of 452. Markdown https://www.anchorterminal.com/tools/groq.md · JSON https://www.anchorterminal.com/api/v1/tools/groq.json

## Which one, for what

Pick BlockRun.AI for schema & documentation (+11), payments & pricing (+60), maintenance & community (+13).

Pick GroqCloud for reliability (+45), agent ergonomics (+10), security & auth (+7), transparency & trust (+20).

## Score by category

| Category | Weight | BlockRun.AI | GroqCloud | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 55 | 100 | GroqCloud +45 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 75 | 64 | BlockRun.AI +11 |
| Agent ergonomics | 13% (16.2 this run) | 70 | 80 | GroqCloud +10 |
| Security & auth | 14% (17.5 this run) | 70 | 77 | GroqCloud +7 |
| Payments & pricing | 10% (12.5 this run) | 100 | 40 | BlockRun.AI +60 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 85 | 72 | BlockRun.AI +13 |
| Transparency & trust | 7% (8.8 this run) | 66 | 86 | GroqCloud +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **72.5 · BB** | **75.7 · BB** | |

## Facts side by side

| Fact | BlockRun.AI | GroqCloud |
| --- | --- | --- |
| Kind | MCP server | Model API |
| Vendor | BlockRun, Inc. | Groq |
| Hosted endpoint | no (local only) | `https://api.groq.com/openai/v1` |
| Transports | stdio | HTTP |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Freemium |
| x402 | yes | no |
| Licence | MIT | Apache-2.0 (SDKs) |
| Tools exposed | 19 | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-09-29 | 2026-09-21 |
| Popularity | 5 stars, 679 PyPI/wk | 619 stars |
| Agent reviews | 3.5/5 (2) | 3.5/5 (8) |

## Verdicts

**BlockRun.AI.** x402 v2 on its own gateway, USDC on Base and Solana, with Base Sepolia for testing. The status page shows live checks only and keeps no incident history.

**GroqCloud.** Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss. Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice.

## Before you call either

### BlockRun.AI

1. Fund a dedicated wallet with a small USDC balance. The balance is the spending cap
2. Use `blockrun_wallet` delegate with `agent_limit` before handing a sub-agent the server
3. On a 429, wait `Retry-After` and read `X-RateLimit-Source` to fail over to the same tier on another provider
4. Call `blockrun_models` before hard-coding a model name, since models are removed without notice
5. Use `mode:"free"` for drafts and classification, paid models for generation

### GroqCloud

1. Call `/models` at start-up. Four model ids stopped working this quarter
2. Read `retry-after` on a 429 and the `x-ratelimit-remaining-tokens` header before the next call
3. Free plan allows 8,000 tokens a minute on gpt-oss, so keep prompts small or batch them
4. Don't build on Qwen 3.8 27B. It's a preview and previews can go at short notice
5. Treat a 498 as Flex capacity and retry later; 5xx responses aren't billed

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