{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"fireworks-ai","slug":"fireworks-ai","name":"Fireworks AI","type":"api","url":"https://fireworks.ai","page_url":"https://unfragile.ai/fireworks-ai","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":"$0.10/1M tokens"},"status":"active","verified":false},"capabilities":[{"id":"fireworks-ai__cap_0","uri":"capability://text.generation.language.multi.model.serverless.text.generation.with.per.token.pricing","name":"multi-model serverless text generation with per-token pricing","description":"Provides on-demand inference across 40+ text generation models (DeepSeek, Kimi, GLM, Qwen, Mixtral, DBRX, Gemma) via a unified REST API with per-token billing. Models are pre-optimized and globally distributed with zero cold starts; requests are routed to the nearest inference cluster and billed only for input and output tokens consumed, with 50% discounts on cached input tokens. Supports context windows up to 262,144 tokens and handles streaming responses for real-time output.","intents":["I need to run inference on multiple open-source models without managing infrastructure or dealing with cold starts","I want to compare model outputs across different architectures (MoE vs dense, different parameter counts) without deploying each one separately","I need to reduce inference costs by leveraging prompt caching for repeated queries with the same context","I want to scale inference from 1 to 1M requests/day without provisioning capacity upfront"],"best_for":["startups and solo developers building LLM applications without DevOps resources","teams evaluating multiple open-source models before committing to fine-tuning","applications with variable traffic patterns that need auto-scaling without cold starts","cost-conscious builders leveraging prompt caching for RAG or multi-turn conversations"],"limitations":["No local inference — all requests traverse the network, adding latency vs. local GPU deployment","Actual p50/p95/p99 latency metrics not published; claims of 'industry-leading' lack third-party benchmarks","Prompt caching discount (50% of input token price) only applies to identical cached segments; partial cache hits not supported","Maximum batch size for async jobs not documented; batch API lacks detailed SLA","No guaranteed rate limits per tier; 'high' and 'higher' limits are vague and subject to change"],"requires":["API key from Fireworks account (free tier: $1 credits)","HTTP client library (curl, requests, axios, etc.) or official SDK (Python/Node.js versions not versioned in docs)","Network connectivity to Fireworks global endpoints","Understanding of token counting for cost estimation (input/output tokens billed separately)"],"input_types":["text (prompts, conversations, system messages)","structured JSON (for function calling schemas)","images (for vision-capable models like Kimi K2.6, GLM-5.1, Qwen3 VL)"],"output_types":["text (streaming or buffered)","JSON (structured output mode)","function calls (tool-use format)"],"categories":["text-generation-language","llm-inference","serverless-api"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_1","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.registry","name":"function calling with schema-based tool registry","description":"Enables structured tool invocation across supported models via OpenAI-compatible function calling API. Developers define tool schemas (name, description, parameters) in JSON; the model receives the schema, reasons about which tool to call, and returns structured function calls with arguments. Fireworks handles schema validation and supports parallel function calling (multiple tools invoked in a single response). Works with DeepSeek, Kimi, GLM, Qwen, and other models that support tool-use.","intents":["I want to build an agent that can call APIs, databases, or custom functions based on user intent without writing complex prompt engineering","I need the model to return structured function calls that my application can directly execute without parsing natural language","I want to support multiple tools and let the model decide which one to use based on the user's request","I need to invoke multiple functions in parallel (e.g., fetch user data AND check inventory in one response)"],"best_for":["developers building LLM agents with external tool integration","teams implementing AI-powered automation workflows (customer support, data processing)","applications requiring deterministic function invocation without hallucination risk"],"limitations":["Function calling support varies by model; not all 40+ models support tool-use (specific model list not documented)","Schema validation is client-side responsibility; Fireworks does not enforce schema correctness before inference","No built-in retry logic for failed function calls; applications must implement their own error handling and re-prompting","Parallel function calling may increase latency due to model reasoning overhead; no documented performance impact","Tool definitions are stateless per request; no persistent tool registry or versioning system"],"requires":["API key for Fireworks","JSON schema definitions for each tool (name, description, parameters object with type and required fields)","Model that supports function calling (DeepSeek V3+, Kimi K2.5+, GLM-4.7+, Qwen3, etc.)","Application logic to handle returned function calls and execute them"],"input_types":["text (user query/prompt)","JSON (tool schema definitions)","conversation history (for multi-turn tool use)"],"output_types":["function call objects (name + arguments)","text (if model chooses not to call a tool)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_10","uri":"capability://automation.workflow.batch.api.for.async.cost.optimized.inference","name":"batch api for async, cost-optimized inference","description":"Processes inference requests asynchronously in batches with 50% cost reduction vs. serverless pricing. Supports text generation and speech-to-text (STT batch API has 40% discount). Ideal for non-urgent workloads (document processing, bulk transcription, batch classification). Requests are queued and processed when resources are available; results are retrieved via polling or webhook (webhook support not documented). Reduces costs significantly for high-volume, latency-tolerant applications.","intents":["I need to process thousands of documents or queries at 50% cost reduction without real-time requirements","I want to transcribe a large audio library overnight at 40% discount","I need to batch-process data for analytics, classification, or extraction at scale","I want to optimize infrastructure costs by deferring non-urgent inference to off-peak hours"],"best_for":["data processing pipelines with flexible latency requirements","bulk transcription or document processing services","analytics and reporting systems that can tolerate hours of processing delay","cost-sensitive applications processing large datasets"],"limitations":["Processing time is not guaranteed; batch jobs could take minutes to hours depending on queue depth","No progress tracking or job status updates documented; polling for results is manual","Webhook support not documented; developers must implement polling logic","Maximum batch size not documented; very large batches may fail or be split","No priority queue; all batch jobs are processed in FIFO order (no expedited processing option)","Cost savings (50% for text, 40% for STT) are modest; not suitable for cost-critical applications"],"requires":["API key for Fireworks","Batch of inference requests (format not documented)","Polling logic or webhook endpoint to retrieve results","Tolerance for processing latency (minutes to hours)"],"input_types":["batch of text prompts or audio files"],"output_types":["batch of inference results (text or transcriptions)","job status (queued, processing, completed, failed)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_11","uri":"capability://text.generation.language.reasoning.model.inference.with.deepseek.r1","name":"reasoning model inference with deepseek r1","description":"Provides access to DeepSeek R1, a reasoning-focused model that performs chain-of-thought reasoning before generating answers. The model explicitly shows its reasoning process, making it suitable for complex problem-solving, math, code generation, and multi-step reasoning tasks. Pricing and context window not documented. Reasoning models are slower than standard models due to extended thinking; latency tradeoff is not quantified.","intents":["I need the model to show its reasoning process for transparency and debugging","I want to solve complex math problems, logic puzzles, or multi-step reasoning tasks","I need better code generation for complex algorithms or architectural decisions","I want to improve accuracy on tasks requiring step-by-step reasoning"],"best_for":["educational applications explaining problem-solving steps","code generation for complex algorithms","math and logic problem-solving","applications where reasoning transparency is valuable"],"limitations":["Reasoning models are significantly slower than standard models; latency impact not documented","Pricing for reasoning models not clearly documented; may be higher than standard models","Context window size not documented; may be smaller than standard models","Reasoning output is verbose; applications must parse and extract the final answer","Reasoning quality depends on problem complexity; may not improve accuracy for simple tasks","No control over reasoning depth or verbosity; all requests use the same reasoning process"],"requires":["API key for Fireworks","Complex reasoning task (math, logic, code generation, etc.)","Tolerance for increased latency"],"input_types":["text (problem statement or prompt)"],"output_types":["text (reasoning process + final answer)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_12","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.api","name":"multi-provider llm abstraction with unified api","description":"Provides a unified REST API and SDK that abstracts away differences between multiple LLM providers (OpenAI, Anthropic, open-source models). Developers write code once and can switch between providers or models without changing application logic. Supports the same function calling, structured output, and streaming interfaces across all providers. Enables A/B testing different models and providers without code refactoring.","intents":["I want to compare outputs from different models (OpenAI, Anthropic, open-source) without writing provider-specific code","I need to switch providers if one goes down or becomes too expensive without rewriting my application","I want to A/B test different models to find the best quality-cost tradeoff","I need a single SDK that works with multiple LLM providers"],"best_for":["teams evaluating multiple LLM providers","applications requiring provider redundancy or failover","cost-optimization projects comparing model pricing","development teams avoiding vendor lock-in"],"limitations":["Abstraction may hide provider-specific features or optimizations; developers lose access to unique capabilities","API compatibility is not perfect; some providers have different parameter names or behaviors (not documented)","Latency overhead from abstraction layer; exact overhead not documented","Error handling may differ between providers; unified error codes may mask provider-specific issues","Pricing comparison is complex; different providers charge differently (per-token, per-request, etc.)"],"requires":["API key for Fireworks","API keys for other providers (OpenAI, Anthropic, etc.) if using their models","SDK (Python/Node.js — versions not documented)"],"input_types":["text (prompts)"],"output_types":["text (inference output)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_13","uri":"capability://automation.workflow.globally.distributed.inference.with.no.cold.starts","name":"globally distributed inference with no cold starts","description":"Claims 'globally distributed virtual cloud infrastructure' with 'no cold starts' for serverless inference, implying models are pre-loaded across multiple geographic regions. Specific regions not documented. Cold-start elimination suggests persistent model loading or aggressive caching, but implementation details unknown. Latency claims ('industry-leading throughput and latency') unquantified. Distributed infrastructure presumably enables geographic load balancing and reduced latency for global users.","intents":["I want low-latency inference for users globally without geographic routing complexity","I need to avoid cold-start delays in serverless inference","I want to scale inference across multiple regions automatically"],"best_for":["global applications requiring consistent low-latency inference","teams avoiding cold-start penalties in serverless architectures"],"limitations":["Specific geographic regions not documented — unclear where models are deployed","Cold-start claim unquantified — no latency benchmarks provided","No geographic routing control — clients cannot specify preferred region","Latency SLA not provided — 'no cold starts' is marketing claim without guarantees","Data residency requirements not addressed — unclear if models can be deployed in specific regions for compliance"],"requires":["Fireworks API key","Global user base or latency-sensitive application"],"input_types":["standard inference requests"],"output_types":["low-latency inference results (claimed but unquantified)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_2","uri":"capability://text.generation.language.json.mode.and.grammar.based.structured.output","name":"json mode and grammar-based structured output","description":"Constrains model output to valid JSON or custom grammar formats without post-processing. JSON mode forces the model to generate only valid JSON matching a provided schema; grammar mode uses GBNF (GBNF format) to define arbitrary output structures (e.g., YAML, custom DSLs). Both modes prevent invalid output at generation time by restricting token selection during decoding, eliminating the need for output parsing or validation.","intents":["I need the model to always return valid JSON that I can directly deserialize without error handling","I want to extract structured data (entities, relationships, classifications) in a specific format without parsing natural language","I need to generate code or configuration files in a specific syntax (YAML, SQL, etc.) without manual formatting","I want to reduce latency and cost by eliminating retry loops for malformed output"],"best_for":["data extraction pipelines requiring 100% valid output","API response generation where clients expect strict JSON schemas","code generation tasks where output must be syntactically valid","applications with low tolerance for parsing failures"],"limitations":["JSON mode requires a valid JSON schema; complex nested schemas may constrain model creativity or accuracy","Grammar mode requires GBNF syntax knowledge; no visual schema builder or validation tool provided","Constraint enforcement adds ~5-15% latency overhead due to token filtering during decoding (not documented by Fireworks)","Model may generate semantically invalid JSON (e.g., correct syntax but wrong field values); constraints only enforce format, not logic","No support for conditional schemas (e.g., 'if type=A then require field X') — only static grammar definitions"],"requires":["API key for Fireworks","Valid JSON schema (JSON Schema format) or GBNF grammar definition","Model that supports structured output (most recent models; specific list not documented)"],"input_types":["text (prompt)","JSON schema or GBNF grammar definition"],"output_types":["JSON (valid, parseable)","custom grammar output (YAML, DSL, etc.)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_3","uri":"capability://image.visual.vision.model.inference.with.multi.image.and.document.analysis","name":"vision model inference with multi-image and document analysis","description":"Provides image understanding and document analysis via vision-capable models (Kimi K2.5/K2.6, GLM-5/5.1, Qwen3 VL 30B) with context windows up to 262,144 tokens. Supports multiple images per request, OCR-like document analysis, and reasoning over visual content. Images are encoded as base64 or URLs; the model processes them alongside text prompts and returns text descriptions, extracted data, or answers to visual questions.","intents":["I need to extract text and data from documents (PDFs, screenshots, scans) without a separate OCR service","I want to analyze multiple images in a single request (e.g., compare product photos, analyze a photo series)","I need to answer questions about images or documents (e.g., 'What's the total in this invoice?')","I want to process documents with very long context (262K tokens) to handle multi-page PDFs or image sequences"],"best_for":["document processing pipelines (invoices, receipts, contracts, forms)","e-commerce applications analyzing product images","accessibility tools converting images to text descriptions","research applications analyzing scientific figures or charts"],"limitations":["Image encoding must be base64 or public URL; no direct file upload endpoint (requires client-side encoding)","Maximum image resolution not documented; very high-resolution images may be downsampled by the model","Vision models are slower than text-only models; no published latency benchmarks for vision inference","OCR accuracy varies by model and document quality; no guarantee of 100% accuracy for handwritten or degraded text","Multi-image requests may hit context window limits faster than text-only requests; token counting for images not clearly documented"],"requires":["API key for Fireworks","Vision-capable model (Kimi K2.5+, GLM-5+, Qwen3 VL 30B, etc.)","Images as base64-encoded strings or public URLs","Text prompt describing what to extract or analyze"],"input_types":["text (prompt/question)","images (base64 or URL)","multiple images per request"],"output_types":["text (descriptions, extracted data, answers)","JSON (structured extraction with schema)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_4","uri":"capability://image.visual.speech.to.text.with.diarization.and.batch.processing","name":"speech-to-text with diarization and batch processing","description":"Transcribes audio to text using Whisper V3 Large or Whisper V3 Large Turbo models. Supports diarization (speaker identification) with a 40% cost surcharge. Offers two pricing tiers: serverless (per-minute billing) and batch API (40% discount, async processing). Audio is sent as file upload or URL; output includes transcription text and optional speaker labels. Batch API processes multiple audio files asynchronously, ideal for high-volume transcription.","intents":["I need to transcribe audio files (interviews, meetings, podcasts) to text at scale","I want to identify who said what in multi-speaker audio (diarization) without manual annotation","I need to reduce transcription costs by 40% for non-urgent, batch processing workflows","I want to transcribe audio in real-time (serverless) or in bulk (batch API) depending on latency requirements"],"best_for":["media and podcast companies transcribing large audio libraries","meeting recording platforms (Zoom, Teams) adding transcription features","research teams processing interview recordings","customer service teams analyzing call recordings"],"limitations":["Diarization adds 40% cost and may not work reliably for overlapping speech or poor audio quality","Audio file size limits not documented; very long audio files may require chunking","Batch API is async; no guaranteed processing time (could be minutes to hours)","Whisper V3 accuracy varies by language, accent, and audio quality; no accuracy metrics published","Serverless pricing ($0.0015/min for V3 Large) is higher than some competitors; batch discount (40%) is modest"],"requires":["API key for Fireworks","Audio file (MP3, WAV, FLAC, etc.) or public URL","For batch API: async job polling or webhook support (webhook support not documented)"],"input_types":["audio files (MP3, WAV, FLAC, OGG, M4A)","audio URLs"],"output_types":["text (transcription)","JSON (with speaker labels if diarization enabled)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_5","uri":"capability://image.visual.image.generation.with.flux.and.sdxl.models","name":"image generation with flux and sdxl models","description":"Generates images from text prompts using FLUX.1 (dev, schnell, Kontext Pro/Max) and SDXL models. Pricing is per-inference-step (SDXL ~30 steps, FLUX dev ~28 steps, FLUX schnell ~4 steps) or flat-rate per image (Kontext variants). Supports prompt engineering, negative prompts, and seed control for reproducibility. Requests are processed asynchronously; output is a URL to the generated image.","intents":["I need to generate product images, marketing visuals, or concept art from text descriptions","I want to use FLUX.1 for higher-quality image generation than SDXL, even if it costs more","I need fast, cheap image generation (FLUX schnell at $0.0014/image) for high-volume applications","I want predictable costs (flat-rate Kontext models) instead of per-step billing"],"best_for":["e-commerce platforms generating product images","marketing agencies creating visual content at scale","game developers generating concept art","applications with high-volume image generation needs (FLUX schnell for cost efficiency)"],"limitations":["Image generation is slow (async); no real-time generation for interactive use cases","FLUX.1 dev is expensive ($0.014/image) compared to SDXL ($0.0039/image); cost-quality tradeoff not always clear","No fine-tuning or style transfer; all models generate from scratch based on prompts","Output image resolution not documented; aspect ratio control not mentioned","No built-in content moderation; users responsible for ensuring generated images comply with policies","Seed control for reproducibility not explicitly documented"],"requires":["API key for Fireworks","Text prompt describing the desired image","Optional: negative prompt, seed, aspect ratio parameters"],"input_types":["text (prompt)","text (negative prompt, optional)","integer (seed, optional)"],"output_types":["image URL (PNG or JPEG)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_6","uri":"capability://data.processing.analysis.text.embeddings.with.semantic.search.support","name":"text embeddings with semantic search support","description":"Generates dense vector embeddings for text using models up to 350M parameters (e.g., Qwen3 8B). Embeddings are fixed-dimensional vectors (dimension size not documented) suitable for semantic search, clustering, and similarity comparison. Supports batch embedding of multiple texts in a single request. Embeddings can be stored in vector databases (Pinecone, Weaviate, etc.) for retrieval-augmented generation (RAG) or recommendation systems.","intents":["I need to embed documents or queries for semantic search without managing a separate embedding service","I want to build a RAG system where documents are embedded once and queries are matched against them","I need to find similar documents or cluster text data based on semantic meaning","I want to use embeddings for recommendation systems or content discovery"],"best_for":["developers building RAG systems with LLMs","search platforms implementing semantic search","recommendation engines based on content similarity","clustering and classification tasks"],"limitations":["Embedding dimension size not documented; cannot optimize for specific vector database constraints","Batch embedding size limits not specified; very large batches may fail","No built-in vector storage; embeddings must be stored externally (Pinecone, Weaviate, Milvus, etc.)","Embedding model selection is limited (up to 350M parameters); no access to larger models like OpenAI's text-embedding-3-large","No fine-tuning for domain-specific embeddings; all models are pre-trained on general text"],"requires":["API key for Fireworks","Text to embed (single or batch)","Optional: external vector database for storage and retrieval"],"input_types":["text (single or batch)"],"output_types":["vector (fixed-dimensional embedding)","batch vectors (for multiple inputs)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_7","uri":"capability://code.generation.editing.supervised.fine.tuning.and.dpo.with.managed.deployment","name":"supervised fine-tuning and dpo with managed deployment","description":"Enables fine-tuning of open-source models (Llama, Mixtral, etc.) using supervised fine-tuning (SFT) or direct preference optimization (DPO). Supports both LoRA (parameter-efficient) and full-parameter fine-tuning. Fine-tuned models are immediately deployable on Fireworks' serverless or on-demand infrastructure at the same price as base models. Training is managed (no GPU provisioning required); pricing is per 1M training tokens, with separate costs for LoRA vs. full-parameter methods.","intents":["I want to adapt a base model to my domain (e.g., customer support, code generation) without managing training infrastructure","I need to optimize model behavior using preference data (DPO) to align with my specific use case","I want to use LoRA for cost-efficient fine-tuning when full-parameter training is too expensive","I need to deploy fine-tuned models immediately without additional setup or infrastructure"],"best_for":["teams building domain-specific AI applications (customer support, code generation, content creation)","companies optimizing model behavior for specific use cases without hiring ML engineers","startups with limited ML infrastructure budgets (LoRA fine-tuning is 5-10x cheaper than full-parameter)"],"limitations":["Training data must be formatted correctly (conversation format for SFT, preference pairs for DPO); no automatic data validation or cleaning","Fine-tuning quality depends heavily on data quality and quantity; no guidance on minimum dataset size","LoRA fine-tuning may produce lower-quality results than full-parameter fine-tuning; no published accuracy comparisons","Training time not documented; no progress tracking or early stopping options mentioned","Fine-tuned models inherit base model limitations (context window, latency, etc.); no improvement in base model capabilities","No A/B testing framework to compare fine-tuned vs. base model performance"],"requires":["API key for Fireworks","Training dataset (SFT: conversation format with system/user/assistant messages; DPO: preference pairs with chosen/rejected responses)","Base model selection (Llama, Mixtral, etc.)","Choice of fine-tuning method (LoRA or full-parameter) and training approach (SFT or DPO)"],"input_types":["JSON (training data in conversation or preference format)","text (dataset file upload)"],"output_types":["fine-tuned model (immediately deployable on Fireworks)","training metrics (loss, accuracy, etc. — not documented)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_8","uri":"capability://automation.workflow.on.demand.gpu.deployments.with.auto.scaling","name":"on-demand gpu deployments with auto-scaling","description":"Allows deployment of custom models or base models on dedicated GPU infrastructure with auto-scaling. Billing is per GPU-second (exact rates not documented). Deployments support custom Docker containers, enabling arbitrary model architectures or inference code. Auto-scaling adjusts GPU count based on traffic; minimal cold starts (faster than serverless but slower than pre-warmed). Suitable for high-throughput, latency-sensitive applications.","intents":["I need lower latency than serverless for real-time applications (chat, search, recommendations)","I want to deploy a custom model or inference code that isn't available on Fireworks' serverless platform","I need guaranteed throughput and auto-scaling without managing Kubernetes or cloud infrastructure","I want to optimize costs by running dedicated GPUs for high-volume workloads"],"best_for":["high-traffic production applications requiring sub-100ms latency","teams deploying custom models or specialized inference code","applications with predictable traffic patterns (auto-scaling works best with stable load)","enterprises with budget for dedicated GPU infrastructure"],"limitations":["Per-GPU-second pricing model is opaque; no published rates or cost calculators provided","Auto-scaling has 'minimal' cold starts, but exact cold start latency not documented","Custom Docker containers require DevOps expertise; no managed container registry or CI/CD integration documented","No SLA or uptime guarantees documented; reliability depends on Fireworks' infrastructure","Scaling limits not documented; maximum GPU count per deployment unknown","No built-in monitoring or cost optimization tools; developers must track GPU utilization manually"],"requires":["API key for Fireworks","Model or inference code (custom Docker container or base model)","Estimated traffic/throughput to right-size GPU allocation","Docker expertise for custom deployments"],"input_types":["Docker image (custom inference code)","model identifier (for base models)"],"output_types":["inference endpoint (HTTP API)","metrics (GPU utilization, latency, throughput)"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__cap_9","uri":"capability://memory.knowledge.prompt.caching.with.50.input.token.discount","name":"prompt caching with 50% input token discount","description":"Caches repeated input tokens (system prompts, context, documents) and charges only 50% of the base input token price for cached tokens on subsequent requests. Caching is automatic for identical token sequences; no explicit cache management required. Ideal for RAG systems, multi-turn conversations, or applications with large static context (e.g., system prompts, knowledge bases). Reduces both latency and cost for repeated queries.","intents":["I want to reduce costs for RAG systems where the same documents are queried multiple times","I need to optimize multi-turn conversations where system prompts and context are reused","I want to cache large knowledge bases or documents and only pay for new query tokens","I need to improve latency for repeated queries by avoiding re-processing of cached context"],"best_for":["RAG systems with static document collections","chatbots with consistent system prompts and context","applications with high query volume on the same documents","cost-sensitive applications where token savings are significant"],"limitations":["Caching only works for identical token sequences; partial matches or similar (but not identical) context are not cached","Cache hit rate depends on query patterns; applications with highly variable queries may see minimal savings","Cached tokens still count toward context window limits; no reduction in model latency from caching (only cost savings)","No explicit cache invalidation; cached tokens persist for the lifetime of the API session (duration not documented)","Cache key is opaque; developers cannot inspect or manage cached tokens directly"],"requires":["API key for Fireworks","Repeated queries with identical input token sequences","Model that supports prompt caching (most recent models; specific list not documented)"],"input_types":["text (prompts with repeated context)"],"output_types":["text (inference output)","cache metadata (tokens cached, savings, etc. — not documented)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fireworks-ai__headline","uri":"capability://tool.use.integration.fast.inference.api.for.ai.models","name":"fast inference api for ai models","description":"Fireworks AI is a fast inference API designed for open-source and custom AI models, providing low latency and high throughput for various AI tasks including text generation and function calling.","intents":["best AI inference API","AI API for low latency models","high throughput API for AI tasks","custom model inference API","open-source model API"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key from Fireworks account (free tier: $1 credits)","HTTP client library (curl, requests, axios, etc.) or official SDK (Python/Node.js versions not versioned in docs)","Network connectivity to Fireworks global endpoints","Understanding of token counting for cost estimation (input/output tokens billed separately)","API key for Fireworks","JSON schema definitions for each tool (name, description, parameters object with type and required fields)","Model that supports function calling (DeepSeek V3+, Kimi K2.5+, GLM-4.7+, Qwen3, etc.)","Application logic to handle returned function calls and execute them","Batch of inference requests (format not documented)","Polling logic or webhook endpoint to retrieve results"],"failure_modes":["No local inference — all requests traverse the network, adding latency vs. local GPU deployment","Actual p50/p95/p99 latency metrics not published; 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