# Fix list: Fireworks AI Inference From Anchor Terminal's listing at https://www.anchorterminal.com/tools/fireworks-inference, the October 2026 research run, assessed 19 October 2026. Grade BB, 73.5 out of 100. This is everything the published grade says the listing lacks, the biggest possible gain to the total first. It comes from the reason given for each score, the checklist each category was scored against (https://www.anchorterminal.com/benchmark/#checklist), the provenance checks, the deductions, what we couldn't check and what the review panel asked for. A fix counts at the next check, once it's public. For a coding agent working on Fireworks AI Inference: work through the items below in the product, its docs and its public pages. Each category gives the reason for its score, with the points each checklist item earned, and the checklist itself, so the gap is the items that earned less than their points. Change the product, not the wording, and keep a note of what you changed and where it's published. ## 1. Payments & pricing, 40 out of 100, up to 7.5 more on the total Why it scored 40: No machine payment protocol found in reviewed inference and billing documentation (0). Public per-token prices by model and serving mode (20). Billing FAQ documents a $1 starter credit usable without a payment method (20). Account signup and subsequent credit purchases require a person (0). The checklist (https://www.anchorterminal.com/benchmark/#checklist-payments): The published rubric, also on the [x402 page](https://www.anchorterminal.com/x402/). - 40, a machine payment protocol (x402, MPP or L402) on the tool's own endpoints. 10 to 30 when it covers only some endpoints or only goes through a third party, and the note says which. - 20, per-call or per-unit pricing published without a login. 10 for public plan-only pricing, 0 for "contact sales" or prices behind a login. - 20, a free tier or trial that doesn't need a card. - 20, autonomous onboarding, meaning an agent can get access without a person signing up in a browser (keyless use, x402, a programmatic key API). Payment platforms and agent wallets rarely charge for their own API over a machine protocol, so the first line has steps for them, and the highest one that applies counts. 40 when x402, MPP or L402 runs on all their own endpoints, 30 when it runs on part of their own API, 25 when their merchants can accept one, 20 for running a facilitator, 15 for paying as a buyer, and 0 when the only protocol is their own. Merchant acceptance sits above a facilitator because the platform's own customers can charge agents through it, while a facilitator settles for sellers who wire up the protocol themselves. The counter-argument (a facilitator does more for the protocol as a whole) has a point. Each note says which step applied. Open-source software you run yourself is scored on its hosted or paid option if it has one. A free, self-hosted package with nothing to buy gets 20, 20 and 20 for the last three lines, and 0 to 40 for the first only if it ships a payment protocol. ## 2. Reliability, 70 out of 100, up to 6 more on the total Why it scored 70: Public component status and incidents (20). The returned incident feed covers only 7 to 19 October, so a complete 90-day record was not established (5). Numeric adaptive token-limit rules and ceilings (15). Documented retryable errors, timeouts and backoff (15). Reserved throughput is documented as SLA-backed, but no numerical commitment was found on that page (5 of 10). Serverless is generally available (10). The checklist (https://www.anchorterminal.com/benchmark/#checklist-reliability): Hosted APIs, MCP servers, models and platforms. - 20, a public status page with component history (Statuspage, Instatus, BetterStack or the vendor's own). - 0 to 30, the incident record for the last 90 days on that page. 30 for a clean record or trivial incidents only, 20 for minor incidents only, 10 for one major outage (an hour or more of a core API down, or errors across the board), 0 for several. 5 when there's no history we could read, and the note says so. - 15, rate limits documented with numbers. - 15, documented 429 or overload handling (Retry-After, backoff guidance), and idempotency keys or safe-retry guidance where writes are involved. - 10, an SLA published for any paid tier. - 10, the surface agents use is generally available, not beta or preview. Local packages, SDKs, frameworks and stdio MCP servers. - 20, installs from an official package with supported runtimes stated. - 25, a public CI and test suite, passing on the default branch. - 0 to 25, open crash or regression issues relative to activity (25 for few and handled, 0 for many, old and unanswered). - 15, semver discipline and breaking changes called out in a changelog. - 15, version 1.0 or later, or declared stable. Protocols are read from their reference implementations, the public facilitators or servers, spec stability and test vectors. ## 3. Agent ergonomics, 80 out of 100, up to 3.3 more on the total Why it scored 80: Compact model API with bounded output (25). Token limits, streaming, structured outputs and batch options (20). Detailed transient-error recovery guidance (20). Retry guidance without a general deduplication guarantee (5 of 20). Official Python SDK and OpenAI-compatible examples, but no separate official TypeScript SDK established (10 of 15). The checklist (https://www.anchorterminal.com/benchmark/#checklist-ergonomics): - 0 to 25, context cost. For MCP, the number and size of the tool definitions (25 for ten or fewer compact tools, 15 for 11 to 30, 5 for more than 30, plus up to 10 back for toolsets, dynamic loading or read-only subsets). For APIs, whether responses can be sized (field selection, limits, summaries). - 20, pagination, filtering and output-size controls. - 20, actionable, documented error responses, codes and messages an agent can recover from. - 20, idempotency or safe retries, and for MCP the `readOnlyHint` and `destructiveHint` annotations. - 15, sensible defaults, few required parameters, and official SDKs in at least two languages. Models are read for tool use, structured output, prompt caching, context length, batch and SDKs. Frameworks for how much code and how many defaults a tool-calling agent with MCP needs. ## 4. Maintenance & community, 65 out of 100, up to 3.1 more on the total Why it scored 65: Changelog updated on 18 October (30), with at least three dated changes in 90 days (20). Support responsiveness not tested (0). Official Python SDK for inference (15). Dependency and CI health not audited (0). The checklist (https://www.anchorterminal.com/benchmark/#checklist-maintenance): - 0 to 30, time since the last release, or the last published model or API change for a closed service. 30 within 30 days, 20 within 90, 10 within 180, 0 older. - 20, at least three releases or dated changelog entries in the last 90 days. - 0 to 25, responsiveness. Issues and pull requests answered on GitHub (the open issues and how recent the replies are). For closed services, a public changelog and a support or community channel that answers, 0 to 15. - 15, presence in the official MCP registry under a verified namespace (MCP servers), or current official SDKs (APIs and models). - 10, package health, current dependencies and CI. Models are read for deprecation notice periods and model churn rather than release counts. ## 5. Transparency & trust, 66 out of 100, up to 3 more on the total Made of editorial 60, provenance 71. Why it scored 66: Proprietary service with linked terms and Apache-2.0 SDK; terms are unreadable to the policy reader under robots rules (10 of 30). Explicit chat ZDR and Responses API retention exception with deletion controls (25 of 30). Dated serverless removals with advance notice, but no general minimum deprecation period established (15 of 20). Data residency choices and storage providers are discussed, but a complete subprocessor/location audit was not made (10 of 20). The checklist (https://www.anchorterminal.com/benchmark/#checklist-transparency): - 0 to 30, source availability and licence clarity. 30 for open source under an OSI licence, 15 for closed with clear terms, 0 for unclear terms. - 0 to 30, data handling and retention statements that agree with each other (privacy policy, DPA, retention periods, subprocessors). - 0 to 20, a deprecation policy or notices with dates. - 0 to 20, telemetry disclosed with an opt-out (local software), or subprocessors and data locations disclosed (hosted). The other half of Transparency and trust is the provenance score, computed from checked facts (below). The category score is the mean of the two. Provenance checks not met in full (half of this category, computed from checked facts): - Domain age: fireworks.ai, no registry record we could read (0 of 15) - Terms of service: published, but our reader couldn't read it (7 of 10) - Privacy policy: read, states 7 of the 8 things a reader expects (9.3 of 10) - security.txt: not found (0 of 10) ## 6. Security & auth, 85 out of 100, up to 2.6 more on the total Why it scored 85: Service-account keys with roles and create/delete controls (30). An inference-user role prevents resource creation and mutation (20). No inference prompt-injection mitigation established (0). Enterprise audit logs and per-key usage visibility documented (15). Data-security documentation states SOC 2 Type II and ISO 27001 certification and links its trust centre (20). Certifications were not independently audited. The checklist (https://www.anchorterminal.com/benchmark/#checklist-security): - 0 to 30, the credential model. 30 for OAuth 2.1 with scopes, or scoped and revocable keys with rotation. 20 for plain revocable API keys. 10 for one all-powerful key. 10 off when a secret can travel in a URL query string as a documented option. - 0 to 20, read-only or least-privilege modes, and confirmation or approval for destructive actions. - 0 to 15, prompt-injection posture where the tool returns untrusted content (documented mitigations or guidance). A tool that returns no untrusted content gets 10. - 0 to 15, audit logs or per-call visibility for the operator. - 0 to 20, a security programme. security.txt or a disclosure policy, a bug bounty, SOC 2 or ISO 27001, advisories handled in public. Models are read for retention, whether API data trains models (and whether that's off by default), zero-retention options and certifications. Frameworks for telemetry defaults, approval hooks, guardrails and sandboxing. ## 7. Schema & documentation, 93 out of 100, up to 1.1 more on the total Why it scored 93: Public chat/completion OpenAPI 3.1 (25), llms.txt and Markdown docs (10), capability and serving-mode explanations (16 of 20), typed inputs and constraints (13 of 15), examples and error guidance (14 of 15), and versioned endpoints with dated changes (15). The checklist (https://www.anchorterminal.com/benchmark/#checklist-schema): APIs and MCP servers. - 25, a machine-readable contract (a public OpenAPI file or similar; for MCP, typed JSON Schema inputs on every tool). - 10, llms.txt or Markdown docs served for agents. - 0 to 20, descriptions that say what a tool is for, when to use it and when not to, read from the tool definitions in the source or the API reference. - 0 to 15, typed inputs with enums, constraints and required fields, and no free-form JSON blobs. - 0 to 15, examples and documented error responses. - 15, versioning and a public changelog. Models are read from the API reference, the OpenAPI file, llms.txt, the structured-output and tool-use docs and the model cards. Frameworks from docs a model can follow, typed interfaces, examples and the API reference. ## What we couldn't check What we couldn't read counted as absent. Publishing it on a page a plain HTTP fetch can read (not only in a browser) lets the next check count it. - Authenticated output quality, latency, throughput and billing were not tested. - SDK dependency health and support response times were not audited. - The returned incident feed covered only 7 to 19 October, not 90 days. - The numerical SLA for reserved throughput was not found on the public product guide. ## Weaknesses - The Responses API stores conversations for 30 days by default unless store is false - Adaptive limits vary by account, model and traffic history; bursts can still be rejected - Priority, Fast and US-only serving paths have different availability and prices ## What costs an agent a turn today The notes we give agents before they call it. Each one is a workaround an agent shouldn't need. - Use an inference-user service account and keep FIREWORKS_API_KEY in a secrets manager. - Use https://api.fireworks.ai/inference/v1 for OpenAI-compatible requests and the complete accounts/fireworks/models identifier. - Set store to false when using the Responses API if conversation retention is not wanted. - Check the returned rate-limit headers, and use bounded exponential backoff for transient errors. - Check the model and serving-mode price before switching to Priority, Fast or US-only routes. ## What the review panel asked for - store=false as default - Show a single price table per mode - Document request deduplication - Machine payment route ## When it's done Send what changed and where it's published as a dispute (https://www.anchorterminal.com/builders/#disputes, or `POST https://www.anchorterminal.com/api/v1/contact` with `"kind": "dispute"`). Disputes are answered in public, and the listing is checked again by the same checklist. Paying for an audit or a listing claim changes nothing here.