Blackbox
Blackbox is a high-trust inference platform for enterprises that run open-weight frontier models. It offers Enterprise Inference with single-tenant, end-to-end encrypted GPU capacity, and a Blackbox Router connecting teams to 300+ hosted models with zero data retention. Agents API, VS Code extension and CLI share one token balance. Pricing is commit-based with published per-model rates and no platform fee.
What is Blackbox?
Blackbox is a secure inference platform positioned as the high-trust option for running open-weight AI models in production. It combines three products under one per-token commitment: Enterprise Inference, which deploys a customer-selected open-weight model on Blackbox's GPUs as a single-tenant, isolated deployment with end-to-end encryption and zero data retention; the Blackbox Router, which connects teams to the 300+ models Blackbox hosts through one endpoint with enforced zero retention and no training; and Agents & Tooling, including the Agents API, a VS Code extension and a CLI that all draw from the same token balance. The platform reports independently measured performance from Artificial Analysis, claiming the fastest output speed on NVIDIA Nemotron 3 Ultra at 454 tokens per second, 2.7 times lower cost than the second-fastest provider, and a 300+ model catalog behind one endpoint and one bill. Pricing is commit-based: customers purchase a token volume through a purchase order, the balance decreases as teams consume tokens at published per-model rates, and intelligent routing and prompt caching extend the commitment by 10-20 percent even on closed models. There is no credit-purchase fee and no per-seat charge; metering is per token. Customers can also connect their own OpenAI, Anthropic or Google accounts and route through existing provider relationships while keeping the dashboard, routing, failover and caching. Unused commitment expires at the end of the billing period with spend alerts at 75 and 90 percent. SSO sign-in works across every surface, making it practical for organizations with strict access-control requirements. The company publishes documentation, benchmark methodology and per-model rates openly, and its pricing page explains how commits work in plain language, including how balances are consumed, how caching extends them, and how unused commitment expires with advance warnings. The Blackbox Router also functions as an aggregation layer for closed frontier models through connected provider accounts, so organizations can standardize on one dashboard even when they route to OpenAI, Anthropic or Google under their own contracts. For evaluation, teams can size a small commit and expand it based on real consumption data rather than forecasting far ahead.
Blackbox Core Features
Single-tenant inference
Deploy the open-weight model of your choice on isolated, reserved GPU capacity
End-to-end encryption
Prompts and outputs are encrypted at the gateway with enforced zero data retention
300+ model router
One endpoint and one bill for the open models Blackbox hosts, including frontier releases
Per-token commitment
Purchase token volume through a purchase order with no platform fee and no per-seat charge
Agents & tooling
Agents API, VS Code extension and CLI all draw from the same balance
PII removal
Customer data is stripped before prompts reach closed models on the Enterprise tier
Verified performance
Independently measured output speed at 454 t/s on Nemotron 3 Ultra
SSO and spend controls
SSO sign-in across surfaces with spend alerts at 75% and 90% of commitment
Who is Blackbox for?
Blackbox is built for engineering and platform teams that deploy open-weight models in production and cannot compromise on data privacy. CTOs and infrastructure leads at AI-native startups choose it when they need reserved, single-tenant GPU capacity with end-to-end encryption and a zero-retention guarantee, which matters for regulated industries such as healthcare, finance and legal. ML engineers and MLOps teams use the OpenAI-compatible endpoint to route requests across 300+ models with one key and one bill, avoiding the operational burden of managing many provider accounts. Enterprises with data-usage policies use the PII-removal feature before prompts reach closed models. Security-conscious developers appreciate that the gateway enforces no training on their traffic. Teams that consume large token volumes use commit-based purchasing, sized so a director can sign without a long approval chain, with spend alerts at 75% and 90% of the commitment. Agent builders and internal tool teams use the Agents API, CLI and VS Code extension while sharing one balance across every surface. Finally, organizations that want verified performance use the independently measured speed benchmarks on models such as Nemotron 3 Ultra, where Blackbox reports 454 tokens per second. Security officers and procurement teams use the published security posture, zero-retention guarantees and SSO support to satisfy governance reviews, while finance teams appreciate the predictable commitment-based spend with spend alerts. Regulated industries such as healthcare, finance and legal use the PII-removal capability before prompts reach closed models. Engineering leads value the single endpoint and one bill across all surfaces, which removes the vendor sprawl of managing several inference providers.
Blackbox Use Cases
Run a production LLM workload on reserved GPU capacity with a zero-retention guarantee
Route requests across 300+ open-weight models through one endpoint and one API key
Deploy a fine-tuned or private model in a single-tenant environment for regulated industries
Automate code generation across the team with the VS Code extension and shared token balance
Build AI agents that need consistent, low-latency inference without per-seat pricing
Remove PII from prompts before they reach closed models in enterprise applications
Manage predictable AI spend with quarterly token commitments and real-time balance dashboards
Blackbox Pros and Cons
Pros
- Zero data retention and end-to-end encryption make it one of the strongest privacy positions in AI inference
- One commit covers Enterprise Inference, the Router, the API and agent tooling, simplifying procurement
- Independently benchmarked speed and cost position it competitively against closed labs
- SSO, spend alerts and per-token metering fit enterprise procurement and governance workflows
Cons
- Commit-based purchasing assumes predictable token volume, which may not suit small or bursty workloads
- There is no visible self-serve free tier, so teams cannot trial the platform without a sales conversation
- The focus on open-weight models means first-party closed frontier models are routed externally, adding complexity
FAQ About Blackbox
Blackbox Pricing
Commit-based enterprise pricing: purchase token volume via purchase order, pay per token at published per-model rates (from about $0.32/M tokens), no platform fee.
Check official pricingCommit
Token volume purchased via purchase order; metered per token at published per-model rates, with spend alerts at 75% and 90%
Enterprise Inference
Single-tenant reserved GPU deployment of an open-weight model with end-to-end encryption and PII removal
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