--- title: "Fireworks AI free inference" description: "$1 for new accounts; it does not recur. See verified limits, model IDs, privacy terms, and primary sources for Fireworks AI." canonical_url: "https://freeinferencing.com/provider/fireworks_ai/" md_url: "https://freeinferencing.com/provider/fireworks_ai.md" last_updated: "2026-09-19" --- # Fireworks AI > $1 for new accounts; it does not recur. ## Classification - **Directory:** Current - **Free access:** One-time trial - **Status:** Current trial - **Confidence:** High - **Payment card:** Unknown - **Account:** Not documented - **Equivalent paid value:** $1.00 once - **API endpoint:** `https://api.fireworks.ai/inference/v1` ## Models mentioned No stable model IDs were recorded in this snapshot. ## Limits and terms ```yaml signup_credit_usd: 1 recurrence: false caveat: This is a small promotional signup credit, not a recurring free tier. Self-serve accounts moved to prepaid billing on 2026-07-01 and service suspends after balance depletion until payment. ``` ## What happens to your prompts? **Partially private.** Open-model requests are transient and no-training by default, but stored-response and proprietary partner routes can differ. 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: Fireworks AI open-model inference and Responses API; proprietary model partners, explicit logging opt-ins, and advanced features can differ. prompt_retention: Open-model prompts and generations exist only in volatile memory for the request by default; prompt caches may remain in volatile memory for several minutes. Responses API stores full conversation data for 30 days when store=true, which is the default. response_retention: Same default ZDR for ordinary inference and 30-day stored conversation policy for Responses API. ordinary_logging: Token counts and service-delivery metadata are logged without content under ZDR. FireOptimizer and similar advanced features can collect content only after explicit opt-in. model_training: Fireworks does not log or store open-model prompt or generation data for training without explicit opt-in; proprietary model partner terms must be checked separately. product_improvement: Metadata can support service delivery and improvement, while content use requires an explicit logging or feature opt-in under the published open-model policy. human_or_operator_access: Default open-model payloads are not written to persistent storage. Stored Responses conversations and opted-in feature data can be accessed for service, support, security, and legal purposes. subprocessors_and_routing: Fireworks hosts open models and may offer partner models under separate model-provider terms; its subprocessors are documented through the trust center. deletion_controls: Responses users can set store=false or immediately delete a stored response by ID; otherwise conversation data expires after 30 days. caveat: “ZDR by default” does not describe the Responses API default, which is store=true. Always set store=false when using Responses and review proprietary-model terms. agreements: terms_of_service: https://fireworks.ai/terms-of-service privacy_policy: https://fireworks.ai/privacy-policy ``` ## Eligibility No structured eligibility block was recorded. ## API compatibility and modalities - **Compatibility:** OpenAI Compatible - **Modalities:** Model inference ## Before you build with Fireworks AI ### 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. “One-time trial” should be interpreted together with the Current directory placement, 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 Open-model requests are transient and no-training by default, but stored-response and proprietary partner routes can differ. 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 8 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 - [Pricing](https://fireworks.ai/pricing): Official Pricing - [Billing and pricing FAQ](https://docs.fireworks.ai/faq-new/billing-pricing/how-much-does-fireworks-cost): Official Billing Faq - [Fireworks AI Terms of Service](https://fireworks.ai/terms-of-service): Official Terms - [Fireworks AI Privacy Policy](https://fireworks.ai/privacy-policy): Official Privacy - [Fireworks AI data handling and retention](https://docs.fireworks.ai/guides/security_compliance/data_handling): Official Docs - [Fireworks Trust Center](https://trust.fireworks.ai/): Official Trust - [Fireworks Series D announcement (more than 40 trillion tokens served every day), 2026-07-15](https://fireworks.ai/blog/series-d-announcement): Official Announcement - [PyPI download stats for the official fireworks-ai package](https://pypistats.org/api/packages/fireworks-ai/recent): Package Registry Stats ## Sitemap See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.