BlockRun.AI vs Vertex AI (Gemini Enterprise Agent Platform)

BlockRun.AI 100

x402 v2 on its own gateway, USDC on Base and Solana, and since MCP 0.55.0 (28 October) on Circle's Arc through arc.blockrun.ai (40). Per-token prices per model published without a login, plus a flat $0.001 fee per wallet call on Base (20). Seven free models per the status page's menu, no card or sign-up, and a wallet costs nothing to create (20). An agent can create its own wallet and pay per request with no human sign-up (20). 40 + 20 + 20 + 20 = 100.

Vertex AI (Gemini Enterprise Agent Platform) 40

No x402, MPP or L402 (0). Per-token prices per model and option published without a login (20). Express mode gives new Google Cloud users 90 days free within quotas with no billing details, though it's in Preview and needs a @gmail.com account (20). A person signs up in the console to get the express key (0).

BlockRun.AI 61

MCP and API reading. The credential is a wallet private key, auto-created at ~/.blockrun/.session or passed in an environment variable and optionally mirrored to the OS keychain, and it controls the whole balance. blockrun_wallet can delegate a capped budget to a child agent, and an API-key route backed by account credit exists (20 of 30, as on 1 October). blockrun_polymarket_read gives read-only positions and previews apart from the trading tool, trades need confirm:true with a $25 default order cap, --profile drops whole groups of tools, and BLOCKRUN_CONFIRM_SPEND=on pauses every paid call for approval, though it's off by default (16 of 20). Search, Exa, market and RPC tools return third-party content, and no prompt-injection guidance was found in the README or docs read (5, as knock and suprsend). Every payment settles on-chain and the wallet tool reports balances and spending (10 of 15). security.txt is valid on 1 November, with an email contact, a policy link and an Expires date of 1 November 2027, and SECURITY.md takes private GitHub reports with a 72-hour acknowledgement and a 7-day plan for fund-affecting issues. No bounty or certification found (10). 20 + 16 + 5 + 10 + 10 = 61.

Vertex AI (Gemini Enterprise Agent Platform) 97

Model reading. OAuth 2.0 with IAM roles and service accounts, which the docs recommend for production, with API keys for testing. No key in a query string found in the docs we read (30). The training restriction in the Service Specific Terms says Google won't use customer data to train or fine-tune any model without permission, GA and pre-GA alike (20). Default in-memory caching for 24 hours that can be disabled, flagged prompts logged up to 90 days unless an abuse-monitoring exception is granted or the customer has a Cloud Master Agreement, the Interactions API storing by default, and Claude Fable 5 and Mythos 5 requiring 30-day retention shared with Anthropic (12 of 15, a judgement call matching the Fable exception on the Anthropic and Bedrock listings). Cloud Audit Logs, with Data Access logs off until enabled, and optional request-response logging to BigQuery (15). google.com security.txt valid to 2030-04-01, the Google VRP, and Generative AI on the platform in ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 scope per the 1 October services-in-scope check for vertex-ai-tuning, not re-read on 6 October (20).

BlockRun.AI 65

Read with the local-software lines, as the listing's kind is mcp and the owner runs the server over stdio, though every paid call lands on BlockRun's gateway (the reading assymetrix-mcp and copilot-studio-mcp get). The 1 October check read it as a router. Installs from the official npm package @blockrun/mcp with Node 20.19 or later stated in engines (20). The CI workflow runs typecheck, build and tests on Node 20.19 and 22 on every push and pull request, with a required gate job. Run results weren't readable, as GitHub's pages answered 403 (20 of 25). Open issues couldn't be read. The changelog names numbered reports fixed in releases, such as #176 and #177 in 0.54.3 and 0.54.4 (10 of 25). Semver numbering with breaking changes marked BREAKING in CHANGELOG.md and shipped as minor bumps, as its own entries say (15). Version 0.55.0, pre-1.0 (0). 20 + 20 + 10 + 15 + 0 = 65. For the gateway behind it, blockrun.ai/status still shows live checks of the Base and Solana gateways and says "None recorded on this page yet" under incident history, the terms still say "We do not commit to any particular uptime", and free models are limited to 30 requests a minute and 300 an hour per IP with 429s carrying Retry-After.

Vertex AI (Gemini Enterprise Agent Platform) 99

Google Cloud status dashboard with per-product history (20). Its incident feed, read on 6 November, has no incident naming Vertex Gemini API, Gemini or the Agent Platform in the 90 days. The regional incidents of 20 August, 1 September and 20 October (europe-west2) and the Cloud Storage incident of 8 and 9 October didn't list it among affected products (30). Standard PayGo publishes usage tiers by 30-day spend with baseline throughput, 2,000,000 to 50,000,000 tokens a minute for Flash models and 500,000 to 10,000,000 for Pro, plus quota tables for other methods (15). A 429 page says to use the global endpoint and truncated exponential backoff, and a retry-strategy page (15). SLA of 99.5 per cent a month on generateContent and streamGenerateContent, 95 per cent for short-term availability models, and a latency SLO on Provisioned Throughput. The 429 page says pay-as-you-go 429s don't count toward the SLA error rate (10). Gemini 4 Argon reached GA on 13 October and Gemini 3.8 Flash moved to the 12-month tier, and the v1 API, 3.5 Flash-Lite and Claude Opus 5.5 are GA, but express mode was Preview at the 6 October check (9 of 10, up from 8). Up from 98 on 6 October.

BlockRun.AI 73

The editorial half. The MCP server and SDKs are MIT and the gateway is closed. The listing is graded as the MCP server, so 30 as assymetrix-mcp got for an MIT server in front of a closed API (was 15 under the router reading). The terms and privacy policy, both last updated 21 October 2026, now name the same entity, BlockRun, Inc., as contracting party and data controller. The privacy policy now says prompts, completions and tool calls to blockrun.ai, arc.blockrun.ai, nano.blockrun.ai and api.blockrun.ai are stored with the paying wallet or account and kept "until we delete it; it has no fixed expiry", usage records and connection metadata for 13 months, while the Solana gateway keeps no request content. The 1 October policy said prompts weren't stored past the request and metadata was kept 30 days. The documents agree with each other, but content has no retention period and no subprocessors are named (18 of 30, was 20). No deprecation policy, and the terms say a provider may withdraw a model at any time. Removals are dated in the changelog, such as Gemma 4 31B delisted on 2 October, the same day (5 of 20). Upstream providers are named and Google Analytics is disclosed with its cookies and retention. Data locations aren't stated, and whether the MCP server sends telemetry wasn't read (10 of 20). 30 + 18 + 5 + 10 = 63.

Vertex AI (Gemini Enterprise Agent Platform) 90

Closed service under the Google Cloud terms, with the Gen AI SDKs under Apache-2.0 and a public discovery document (18, as for Bedrock's open service models). The zero data retention and abuse monitoring pages agree with the Cloud terms and give periods for each case, 24 hours in memory, up to 90 days for flagged prompts, 30 days for Advanced AI models, 3 days for Search grounding logs and 30 days for Maps grounding, and say which can't be switched off (27 of 30). A dated model versions table with release and retirement dates and a stated 12-month or 45-day policy, but on 6 November it still listed Gemini 2.5 Pro, Flash and Flash-Lite as available with a 20 October 2026 retirement date and left them out of the retired table (18 of 20, down from 20). The Cloud subprocessor list with locations, per the 1 October vertex-ai-tuning check, regional and multi-region endpoints, and per-model ML processing locations on the partner model cards (20).

BlockRun.AI 88

MCP reading. 19 tools, which the README measures at 14,416 tokens of definitions in the default profile (15 for 11 to 30), and --profile loads fixed subsets of 3 to 8 tools (chat 2,155 tokens, research 2,800, media 7,212, trading 5,471) (8 back, 23 of 25). Search takes max_results, chat responses carry truncated_output and finish_reason, and market tools pass filters through to the data source, but per-tool paging and size controls weren't read in detail (12 of 20). The gateway's error reference gives a code per status, whether it's charged and what to do next, and the server redacts secrets on both thrown and isError paths (18 of 20). Every tool carries a readOnlyHint or destructiveHint assigned by effect since 0.33.0, with a test that fails on an unannotated tool. Payment settles only after a successful upstream response, and since 0.54.2 a withdrawal that may have moved money is never signed twice (20). Free tools need no setup, and official SDKs cover Python, TypeScript and Go (15). 23 + 12 + 18 + 20 + 15 = 88.

Vertex AI (Gemini Enterprise Agent Platform) 97

Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with FunctionCallingConfig mode ANY and allowedFunctionNames to force a named tool (20). Schema-guaranteed JSON output (15). Implicit and explicit context caching, cached input at 0.1x, $0.075 per million on 3.8 Flash through 31 December (15). 1,048,576-token context on 3.8 Flash, a unified 1,048,576-token input and output limit on Gemini 4 Argon, and 1,000,000 on Claude Opus 5.5 (15). Flex/Batch at half price on Gemini (10). Gen AI SDKs for Python and JavaScript among others, and the OpenAI libraries work with Google Cloud auth (10). 429 handling documented per quota framework, with standard Google status codes elsewhere (12 of 15).

BlockRun.AI 85

MCP reading. @blockrun/mcp 0.55.0 published to the official MCP registry on 28 October 2026, and a gateway changelog entry on 25 October (30). 40 registry versions from 0.35.0 on 3 August to 0.55.0 (20). Issue replies couldn't be read, as GitHub's issue pages answered 403; the changelog credits numbered reports fixed within releases (10 of 25). Listed in the official MCP registry as io.github.BlockRunAI/blockrun-mcp, a GitHub-verified namespace, active, with 0.55.0 marked latest (15). CI on Node 20.19 and 22 with typecheck and tests on every pull request, and a Renovate configuration in the repository (10). 30 + 20 + 10 + 15 + 10 = 85.

Vertex AI (Gemini Enterprise Agent Platform) 91

Model reading. Gemini Omni 1.2 Flash GA on 5 November, Grok 4.7 GA on 27 October, Gemini 4 Argon GA on 13 October and Claude Haiku 5.5 on 7 October 2026 (30). Stable models stay at least 12 months, and since 6 October Gemini 4 Argon and Gemini 3.8 Flash are in that tier, while 3.7 and 3.6 Flash remain short-term with at least 45 days' notice once a date is set (10 of 12, up from 8). Gemini 2.5 Pro, Flash and Flash-Lite were due to retire on 20 October, three models on one date after more than 12 months, with replacements named. We didn't check partner model retirements (6 of 8, down from 8). Dated release notes on most working days, newest 5 November, and Cloud Customer Care (15). python-genai replies not sampled this run (5 of 10). Current SDKs, google-genai 3.0.0 on 6 November and @google/genai 3.0.0, and google-cloud-aiplatform 2.7.0 on 28 October 2026 (15). Supported runtimes stated, Python 3.10 or later and Node 20 or later, per the 6 October check (10). Unchanged at 91.