AI assistants and search decision
Blackbox AI
A capable engineer can build a useful open router/proxy with caching and metering in about a week, but replicating Blackbox's single-tenant high-throughput infrastructure, enterprise SLAs, and 300+ model catalog is not realistic for a small DIY effort.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.
What a replacement has to do
- Proxy incoming OpenAI-compatible inference requests, route to chosen model(s), stream responses back, and meter/cache tokens.
What it still won’t have
- Dedicated single-tenant GPU infrastructure and the measured high-throughput (Nemotron 3 Ultra) performance
- Forward-deployed engineering and enterprise implementation services
- Enterprise SLAs, audits (SOC2/ISO) and contractual zero-retention guarantees
- Catalog of 300+ models behind one commercial bill and volume-committed pricing
What remains hard
- Infrastructure at scale
We run the open-weight model that you choose as your own dedicated deployment — one tenant, isolated from every other customer.
- Infrastructure at scale
Artificial Analysis verified us as the #1 Nemotron 3 Ultra provider at 454 tokens/sec, July 2026.
- Execution quality
Every surface: CLI, VS Code, IDE, mobile, Builder
First-year cost
No published price
Blackbox AI does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a lightweight Blackbox-like inference router using Python (FastAPI) + Redis for cache + Postgres for metering, deployed on a single small cloud VM (e.g., 4 vCPU / 8 GB). Implement: (1) an OpenAI-compatible REST+streaming /v1/chat/completions proxy accepting API keys, (2) pluggable adapters for at least two model providers (one open-weight hostable model via a local/hosted inference endpoint and one closed-provider API), (3) cost-aware routing and per-request token accounting, (4) prompt/result caching with TTL and cache-read billing, (5) basic web dashboard to view prepaid balance and recent requests, (6) TLS, API-key auth, input validation, error handling, and unit tests for routing, billing, and caching. Out of scope: single-tenant GPU cluster orchestration, enterprise SLAs, and forward-deployed engineering services.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 cited sources+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score61
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.
How scoring works →Cited sources · 4
Every page the run actually retrieved.
- official productBlackbox — The high-trust platform for frontier inference
- official pricingPricing — commit more, pay less per token — Blackbox
- open sourcelm-sys/FastChat
- open sourcearc53/DocsGPT
Integrity checks
What held up, and what did not.
