--- title: "Darkbloom free inference" description: "Public alpha access is evaluation-only, but every displayed model has a positive token price." canonical_url: "https://freeinferencing.com/provider/darkbloom/" md_url: "https://freeinferencing.com/provider/darkbloom.md" last_updated: "2026-09-19" --- # Darkbloom > Public alpha access is evaluation-only, but every displayed model has a positive token price. ## Classification - **Directory:** Shame - **Free access:** Not a free offer - **Status:** Paid Public Alpha - **Confidence:** Medium High - **Payment card:** Unknown - **Account:** Not documented - **Equivalent paid value:** Not quantifiable ## Models mentioned No stable model IDs were recorded in this snapshot. ## Limits and terms No structured limits block was recorded. ## What happens to your prompts? **Partially private.** Privacy protections are incomplete, conditional, or not fully documented. Privacy is audited separately from price. Review the current governing terms before sending sensitive or regulated data. ## Data governance and agreements ```yaml data_governance: review_status: reviewed plan_scope: Darkbloom public-alpha consumer and provider services. prompt_retention: Content may be retained for shorter operational periods and longer for support, abuse review, legal compliance, or disputes; no fixed duration is published. response_retention: Covered by the same Content retention language as prompts. ordinary_logging: Coordinator is designed not to log prompt content in ordinary request logs, but logs operational metadata. model_training: Current policy does not grant Darkbloom the right to use Content for general-purpose model training; it says terms would be updated before any future change where law requires. product_improvement: Aggregated or de-identified information may be created for analytics, security, reporting, and service improvement. human_or_operator_access: The coordinator currently processes request payloads in plaintext transiently; relevant Content is disclosed to the selected independently operated provider under technical and contractual controls. subprocessors_and_routing: Third-party routers may terminate TLS and have technical access before requests reach Darkbloom; their own policies apply. deletion_controls: Privacy requests are available, subject to identity verification and legal, billing, security, compliance, and dispute exceptions. conflict: Product marketing describes encrypted requests hidden from operators, while current terms and privacy disclosures say universal end-to-end encryption is not yet present and coordinator plaintext access is technically possible. agreements: terms_of_service: https://www.darkbloom.dev/terms.html privacy_policy: https://www.darkbloom.dev/privacy.html ``` ## Eligibility No structured eligibility block was recorded. ## API compatibility and modalities - **Compatibility:** Provider-specific or not documented - **Modalities:** Model inference ## Before you build with Darkbloom ### Read the classification narrowly This record describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every account and region. “Not a free offer” should be interpreted together with the Shame directory placement, Medium High confidence, Unknown payment-card status, and the plan-specific sources below. ### Resolve the live model route No stable model ID is recorded. Resolve the current machine-readable ID, endpoint, authentication method, and request shape from the provider's live documentation before writing fixed production configuration. ### Match the quota to the workload shape Translate the allowance into peak requests per minute, input and output tokens, concurrency, retries, and every daily or monthly ceiling that applies to the intended account. The first limit reached by the workload is the practical ceiling. A large token pool can still fail interactive bursts, and a high request limit can still fail long-context work. Include tool calls and retry traffic, test the largest realistic payload, and treat research, experimental, and community access as conditional on their eligibility and fair-use terms. ### Preserve billing and privacy boundaries The recorded payment-card requirement is Unknown, and account access is not documented. Confirm both in the live signup flow, set provider-side budgets when charges are possible, and observe the balance or usage fields after a complete request. The prompt-handling classification is **Partially private** because Privacy protections are incomplete, conditional, or not fully documented. Re-read the terms for the exact route and plan before sending sensitive, regulated, or proprietary content. ### Follow the evidence, then re-check it This record links 3 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest and most specific governing document or live catalog when sources disagree. A dated finding can become stale even when the page remains online, so retain the source and snapshot that supported the decision and submit a correction when a provider changes a material term. ### Plan fallback without policy drift A fallback should preserve modality, context length, streaming, structured output, tools, safety controls, and data terms, not only API syntax. Decide which errors may retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. Rotating aliases and free pools can change behavior without changing the endpoint, so retain the response model field and test at least one substitute route before the primary offer becomes unavailable. ### Monitor the offer as a dependency Capture the model ID, response model, rate-limit headers, usage fields, latency, HTTP status, and provider request identifier. Watch authorization failure, quota exhaustion, catalog removal, policy revision, and balance movement as separate failure modes. Re-check the live catalog and governing sources on a schedule proportionate to the workload's importance, and keep an owner and exit path for any production dependency on volatile free capacity. ### Test one complete request before scaling Start with the smallest permitted request using the exact credential, model ID, endpoint, region, and account type intended for deployment. Record the status, headers, usage fields, response model, latency, and dashboard balance movement. Then exercise an invalid model, quota exhaustion, or rate limit so failure is explicit and cannot silently switch to a paid route. Validate streaming, structured output, and tool calls separately because a free model can expose fewer features than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and do not send sensitive data until the observed route matches the reviewed agreement. ## Primary sources - [Darkbloom](https://www.darkbloom.dev/): Official Product - [Terms of Service](https://www.darkbloom.dev/terms.html): Official Terms - [Privacy Policy](https://www.darkbloom.dev/privacy.html): Official Privacy ## Sitemap See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.