AI Infrastructure & APIs comparison · 2026

Blackbox vs Toloka

Compare Blackbox and Toloka as AI Infrastructure & APIs tools on fit, pricing, and the capabilities that actually overlap in 2026. Pick Blackbox for teams serving open-weight frontier models such as Nemotron Ultra on isolated capacity for regulated workloads and consolidating scattered OpenAI, Anthropic, and Google spend behind one endpoint and one invoice. Pick Toloka for teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning.

Updated Aug 22, 2026

Blackbox
blackbox.ai

Encrypted single-tenant inference plus a 300+ model router behind one endpoint

Starts at Custom (annual per-token commit; no published entry price)·AI Infrastructure & APIs

Toloka
toloka.ai

Expert-curated training, evaluation and red-teaming data for AI agents and LLMs

Starts at Custom quote (contact form budget bands start at under $25k)·AI Infrastructure & APIs

At a glance

BlackboxToloka
CategoryAI Infrastructure & APIsAI Infrastructure & APIs
PricingStarts at Custom (annual per-token commit; no published entry price)Starts at Custom quote (contact form budget bands start at under $25k)
Free tierNoNo
PlatformsWebWeb
Suitable forTeams serving open-weight frontier models such as Nemotron Ultra on isolated capacity for regulated workloads and consolidating scattered OpenAI, Anthropic, and Google spend behind one endpoint and one invoiceTeams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning
CompanyBlackbox AI Technologies Inc.Toloka Group, Inc.
Founded—2014

How they differ

Shared AI Infrastructure & APIs rubric, filled from each listing. Not a score.

Model catalog

BlackboxToloka
Text modelsYesNot published
Image modelsNot publishedNot published
Video modelsNot publishedNot published
Speech modelsNot publishedNot published
Open-weight modelsYesNot published
Pinned versionsLimitedNot published

Serving

BlackboxToloka
One unified APIYesNot published
OpenAI-compatible APIYesNot published
Streaming responsesYesNot published
Batch jobsNot publishedNot published
Provider fallbackYesNot published
Published rate limitsNot publishedNot published

Build & tune

BlackboxToloka
Fine-tuningNot publishedYes
EmbeddingsNot publishedNot published
Vector storeNot publishedNot published
RAG pipelinesNot publishedNot published
Agent frameworkYesYes
Tool / function callingNot publishedNot published

Observe & control

BlackboxToloka
Usage dashboardYesNot published
Per-request costsLimitedNot published
Traces / loggingNot publishedNot published
EvalsNot publishedYes
Prompt managementNot publishedNot published
Zero-retention optionYesNot published

Access

BlackboxToloka
Public APIYesNot published
Official SDKLimitedNot published
Self-serve signupLimitedNot published
Free trialNot publishedNot published
Team workspaceLimitedNot published
SSO / SAMLYesNot published
Mobile appsNoNot published
Browser extensionNot publishedNot published
Self-host / on-premNoNot published

Commercial

BlackboxToloka
Commercial licenseNot publishedNot published
Usage-based pricingYesNot published
Invoice / POLimitedNot published
SOC 2Not publishedNot published
GDPR / DPAYesNot published
Audit logYesNot published
Role-based accessYesNot published

Features

These listings describe different capabilities. What each one ships:

Only Blackbox

  • Blackbox Router exposes 300+ hosted open and closed models through one OpenAI-compatible endpoint with one key, one bill, and one dashboard
  • Blackbox Enterprise Inference runs the open-weight model you choose on reserved, single-tenant GPU capacity
  • End-to-end encryption on every connection plus encryption at rest
  • Zero data retention enforced at the gateway, contractual under the Enterprise DPA
  • PII removed before prompts reach closed models on Enterprise
  • Smart routing, failover, and prompt caching included at no extra platform fee
  • OpenAI-compatible REST and streaming, so only the base URL changes
  • Agents API for cloud coding agents and multi-agent workflows

Only Toloka

  • Environments generation: context-rich simulated environments for evaluating and training agents
  • Training datasets covering specialized agentic skills
  • Evaluation and red-teaming that assesses agent performance and identifies vulnerabilities
  • Agent trajectory demonstrations and step-by-step evaluations across tool-use workflows
  • Virtual environments and RL-gyms with MCP replicas and computer-use testbeds
  • Safety red-teaming for injection vulnerabilities and policy compliance
  • Demonstrations generation for Supervised Fine-Tuning (SFT)
  • Preference collection for RLHF and Direct Preference Optimization (DPO)

Use cases

These listings describe different use cases. What each one ships:

Only Blackbox

  • Serving open-weight frontier models such as Nemotron Ultra on isolated capacity for regulated workloads
  • Consolidating scattered OpenAI, Anthropic, and Google spend behind one endpoint and one invoice
  • Passing a security review that requires contractual zero retention and no model training on prompts
  • Cutting inference bills by routing cost-sensitive tasks to cheaper open-weight models with prompt caching
  • Running cloud coding agents and Remote Agent sandboxes on the same token budget as production inference
  • Migrating an existing OpenAI-compatible codebase to a new provider by swapping the base URL
  • Standardizing developer AI access across an organization with SSO, RBAC, and audit logs
  • Keeping negotiated provider rates while gaining failover and centralized observability

Only Toloka

  • Producing agent trajectory data to post-train tool-using and computer-use agents
  • Building RL gym environments with MCP replicas for agent evaluation and reinforcement learning
  • Collecting human preference pairs for RLHF and DPO alignment runs
  • Red-teaming assistants for prompt injection vulnerabilities and policy compliance
  • Generating repository-scale programming data for AI coding copilots
  • Sourcing domain-expert reasoning chains in regulated fields such as medicine and law
  • Running customized human evaluation of model outputs across text, image, video and audio
  • Licensing off-the-shelf datasets such as University-level Math Reasoning to bootstrap a benchmark

Plans

Blackbox

  • Enterprise Custom/mo or Custom/yr

    Annual PO burn-down, committed spend sized with your team · Closed models −5%+, open-weight models −10%+ discounts · Dedicated forward-deployed engineer; implementation included, $0 · Dedicated single-tenant deployment (Enterprise Inference) · Data residency · SAML SSO, SCIM, RBAC, audit logs · Zero data retention, contractual DPA · PII removed before closed models · End-to-end encryption on every connection · Guaranteed TPM / custom rate limits · Dedicated Remote Agent runners with SSO · Agents API: cloud coding agents

Toloka

  • Custom data engagement Custom quote/mo or Custom quote/yr

    Toloka publishes no pricing page, plan table or public rate card · Toloka scopes each project through the Talk to us form · Toloka contact form budget bands: under $25k, $25-50k, $50-100k, $100k-200k, $200k+ · Toloka timeline options: within a month, 1-3 months, 3-6 months, just researching · Toloka Terms of Use state that website terms do not govern paid services, which sit under separate terms

Blackbox strengths

  • One OpenAI-compatible endpoint reaches 300+ models, so migration is a base URL change rather than a rewrite
  • Blackbox publishes per-1M-token list rates for input, output, and cached reads instead of hiding all numbers behind sales
  • Blackbox states there is no platform fee, no credit-purchase fee, and no per-seat charge
  • Single-tenant deployments on Blackbox GPUs give isolation that shared multi-tenant routers cannot offer
  • The same commit covers Enterprise Inference, the Router, the API, the CLI, the Agents API, and the VS Code extension
  • Implementation with a dedicated forward-deployed engineer is included at $0 on Enterprise
  • Bring-your-own provider accounts preserve existing negotiated rates while keeping routing and dashboards
  • Blackbox cites an independent Artificial Analysis measurement of 454 tokens per second on Nemotron Ultra rather than a self-reported figure

Watch-outs

  • Unused committed spend expires at the end of the billing period and does not roll over; Blackbox only warns at 75% and 90% of the commit
  • No published $0 tier, no free credits, and no self-serve signup: Blackbox says engagement starts "with conversation, not signup"
  • Every plan is custom-quoted, so no buyer can compare an entry price without contacting Blackbox sales
  • PII removal before closed models, data residency, dedicated Remote Agent runners, custom SLAs, and the dedicated engineer are Enterprise-only
  • Zero retention and training suppression on routed traffic are qualified by Blackbox as applying "wherever the provider API supports it," so guarantees vary by upstream provider
  • Chairman LLM orchestration is listed as included but metered, which adds consumption on top of standard token spend
  • Vendor contradiction: the Blackbox homepage says 30% faster than the #2 provider on Nemotron Ultra, while the Blackbox contact page says 47% faster at the same 454 t/s and 2.7x price figures
  • Vendor contradiction: Blackbox structured data names the legal entity Blackbox AI Technologies Inc., while the Blackbox privacy policy names Cours Connecte Inc. doing business as Blackbox AI Technologies

Toloka strengths

  • Toloka documents concrete agent-specific deliverables such as RL-gyms with MCP replicas and computer-use testbeds, not generic labeling
  • Toloka names its quality methodology explicitly: post-verification, dynamic overlaps, cross-validation, golden sets, 50+ automated QC methods
  • Toloka reports expert coverage across 50+ knowledge domains and 120+ subdomains, useful for medicine, law and other specialized work
  • Toloka publishes security posture details including ISO 27001, ISO 27701, SOC 2, GDPR, CCPA and HIPAA compliance plus on-premises storage options
  • Toloka sells off-the-shelf datasets and Toloka Arena alongside custom projects, giving smaller teams an entry point

Watch-outs

  • Toloka publishes no pricing page, plan table or unit rates, so no cost can be estimated before a sales call
  • The Toloka contact form's lowest budget band is under $25k, which signals a project floor unsuitable for small experiments
  • Vendor contradiction: the Toloka footer credits Toloka AI BV while the Toloka Terms of Use define Toloka as Toloka Group, Inc. of Wilmington, Delaware, and the privacy notice applies to Toloka AI B.V.
  • Vendor contradiction: Toloka's website Terms of Use explicitly state they do not govern paid services, so the terms a buyer actually signs with Toloka are not published
  • Toloka work is delivered as a managed service, so buyers cannot self-serve a project without contacting Toloka
  • Toloka's Eligibility and Geographic Restrictions policy and terms warn that access may not be lawful in some countries, limiting where contributors and buyers can participate
  • Toloka's privacy notice states that contributor names, emails and Slack, Jira or Git handles can appear inside delivered coding task materials shared with clients
  • Toloka's website terms cap total liability for site-related claims at US $100, and paid-service liability is negotiated separately

Blackbox vs Toloka verdict

Who each product is for, then labeled AI takes. Not a generic winner.

Bottom line

Pick Blackbox for teams serving open-weight frontier models such as Nemotron Ultra on isolated capacity for regulated workloads and consolidating scattered OpenAI, Anthropic, and Google spend behind one endpoint and one invoice. Pick Toloka for teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning.

Who Blackbox is for

Teams serving open-weight frontier models such as Nemotron Ultra on isolated capacity for regulated workloads and consolidating scattered OpenAI, Anthropic, and Google spend behind one endpoint and one invoice

Encrypted single-tenant inference plus a 300+ model router behind one endpoint

Who Toloka is for

Teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning

Expert-curated training, evaluation and red-teaming data for AI agents and LLMs

AI take on Blackbox

In August 2026, Blackbox stands out for dedicated open-weight inference and broad routing, but its commit-only sales model demands careful validation.

AI take on Toloka

Toloka stands out for agent trajectories and RL environments, but the managed-service model demands procurement tolerance and careful contracting.

Blackbox vs Toloka FAQ

Common questions when choosing between Blackbox and Toloka.

Is Blackbox or Toloka the better AI Infrastructure & APIs tool?

Pick Blackbox for teams serving open-weight frontier models such as Nemotron Ultra on isolated capacity for regulated workloads and consolidating scattered OpenAI, Anthropic, and Google spend behind one endpoint and one invoice. Pick Toloka for teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning.

Which is cheaper, Blackbox or Toloka?

Blackbox starts at Custom (annual per-token commit; no published entry price). Toloka starts at Custom quote (contact form budget bands start at under $25k). Confirm current pricing on each vendor site.

Who should choose Blackbox?

Teams serving open-weight frontier models such as Nemotron Ultra on isolated capacity for regulated workloads and consolidating scattered OpenAI, Anthropic, and Google spend behind one endpoint and one invoice

Who should choose Toloka?

Teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning

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