Best serverless LLM inference API
4 models · updated 2026-08-14
The verdict
Fireworks AI leads — 3 of 4 models rank Fireworks AI the top pick.
Not unanimous: Gemini picks Together AI.
As of 2026-08-14, ChatGPT, Claude, Gemini and Grok collectively rank Fireworks AI #1 for serverless llm inference api on ModelsAgree by aggregate score. The models' case: Best overall balance of fast, no-cold-start serving, strong open-model coverage, OpenAI-compatible APIs, prompt caching, batch discounts, fine-tuning, and a clean path to. The models' main caveat: Shared serverless latency can still vary, while priority and dedicated tiers materially increase cost. The strongest alternative is Together AI — Broadest production open-weight catalog, highly optimized inference engine with reliable speculative decoding, seamless custom fine-tuning deployment. Not unanimous: Gemini picks Together AI. Source: https://modelsagree.com/best/best-serverless-llm-inference-api (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #1Claude #1Gemini #3Grok #1
Best overall balance of fast, no-cold-start serving, strong open-model coverage, OpenAI-compatible APIs, prompt caching, batch discounts, fine-tuning, and a clean path to higher-reliability or dedicated capacity; narrowly beats Together for production ergonomics.
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GPT Best overall balance of fast, no-cold-start serving, strong open-model coverage, OpenAI-compatible APIs, prompt caching, batch discounts, fine-tuning, and a clean path to higher-reliability or dedicated capacity; narrowly beats Together for production ergonomics.
Claude Production-grade serverless inference with among the best latency/throughput on open weights (FireAttention kernels), a broad current catalog (Llama, Qwen, DeepSeek, Mixtral), per-token pricing, LoRA fine-tuning, JSON/grammar-constrained output, and strong reliability at scale — the best all-around default for a practitioner shipping open-model apps
Grok Highest production reliability (near-99.8% uptime track record), strongest function calling/structured output for agentic workloads, custom FireAttention kernels deliver competitive latency/throughput among GPU hosts, solid open-model catalog with LoRA support, OpenAI-compatible API, competitive mid-tier pricing, and enterprise compliance (SOC2/HIPAA) that typical production practitioners actually need; assumption is reliability + agent features outweigh pure cost or absolute peak speed
Gemini Exceptional serving performance via FireAttention optimizations, industry-leading time-to-first-token (TTFT), and native support for serverless dynamic LoRA adapter switching with negligible latency penalties.
Where it falls shortper GPT Shared serverless latency can still vary, while priority and dedicated tiers materially increase cost.
per Claude Not the absolute cheapest, and not for teams that want proprietary frontier models (Claude/GPT) natively in one place
per Gemini Less developer-facing UI tooling and ecosystem integrations than larger managed platforms; not suited for teams requiring all-in-one data annotation and fine-tuning pipelines.
per Grok Not the absolute cheapest on popular open models and not the raw-speed leader vs custom silicon
- 2GPT #2Claude #2Gemini #1Grok #3
Broadest production open-weight catalog, highly optimized inference engine with reliable speculative decoding, seamless custom fine-tuning deployment, and enterprise-grade reliability (near-tie with Groq depending on whether model breadth or raw speed is prioritized).
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Gemini Broadest production open-weight catalog, highly optimized inference engine with reliable speculative decoding, seamless custom fine-tuning deployment, and enterprise-grade reliability (near-tie with Groq depending on whether model breadth or raw speed is prioritized).
GPT Near-tie for first with broad, rapidly updated multimodal model coverage, competitive throughput and pricing, automatic cached-input discounts, batch inference, Serverless LoRA, and easy migration to dedicated deployments.
Claude The widest serverless open-model catalog with competitive per-token pricing, solid speed, fine-tuning, and batch/dedicated-endpoint options — the safest breadth pick when you want many models behind one API and room to grow into training
Grok Broadest open-weight catalog (200+ models including multimodal), first-class fine-tuning + dedicated endpoints path, solid OpenAI-compatible serverless performance, research pedigree that keeps new models available quickly, and clean migration for teams leaving closed APIs; strong full-lifecycle value for practitioners who iterate on models
Where it falls shortper GPT Its very broad catalog has uneven model-specific performance, so serious workloads require benchmarking rather than trusting platform-wide speed claims.
per Claude Per-model latency and reliability are less consistently tuned than Fireworks/Groq at the very top of the throughput/latency curve
per Gemini Peak single-stream throughput is outpaced by specialized custom-silicon architectures; not optimal for applications where ultra-low-latency voice or instant text rendering is the sole metric.
per Grok Slightly higher average latency and pricing than the speed or pure-cost leaders on overlapping models
- 3GPT #3Claude #3Gemini #2Grok #2
Unmatched token generation throughput and latency via LPU architecture (consistently 300–800+ tokens/sec), making it the gold standard for real-time voice agents and multi-step agentic execution loops.
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Gemini Unmatched token generation throughput and latency via LPU architecture (consistently 300–800+ tokens/sec), making it the gold standard for real-time voice agents and multi-step agentic execution loops.
Grok Unmatched real-world latency and throughput via custom LPU silicon (often 300-800+ tok/s and sub-150ms TTFT on supported models), competitive pricing relative to the speed advantage, OpenAI-compatible, zero cold-start feel on curated menu, excellent for interactive/real-time/voice/coding agents where every token compounds; near-tie with Fireworks when latency is the dominant constraint
GPT Exceptional token-generation speed and low latency on supported models, straightforward OpenAI-compatible integration, competitive token pricing, and discounted batch processing make it the strongest choice for latency-sensitive interactive applications.
Claude Unmatched tokens/sec and time-to-first-token via custom LPU hardware; when interactive latency or high-throughput agentic loops dominate, nothing serverless is faster, with clean OpenAI-compatible API and low per-token cost
Where it falls shortper GPT The curated model catalog and hardware-specific support are substantially narrower than Fireworks or Together, limiting model choice and customization.
per Claude Narrow curated model menu and periodic capacity/rate limits — not for teams needing a long tail of models or guaranteed dedicated capacity
per Gemini Restricted model selection constrained by on-chip SRAM capacity, with no capability for hosting custom model architectures or dynamic serverless LoRA adapters.
per Grok Narrow curated model list with limited/no custom weights or fine-tuning, plus occasional capacity constraints at peak
- 4GPT #4Claude —Gemini #4Grok #4
Excellent price-to-performance, broad open-model selection, simple pay-as-you-go OpenAI-compatible APIs, embeddings and rerankers, and low operational friction make it especially compelling for cost-conscious applications.
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GPT Excellent price-to-performance, broad open-model selection, simple pay-as-you-go OpenAI-compatible APIs, embeddings and rerankers, and low operational friction make it especially compelling for cost-conscious applications.
Gemini Best-in-class price-to-performance economics with ultra-low per-token costs, rock-solid uptime, and drop-in OpenAI API compatibility across leading text, vision, and audio foundation models.
Grok Consistently the lowest or near-lowest per-token pricing on popular open models (often 2-4x cheaper than premium hosts), broad catalog, simple OpenAI-compatible API, and reliable enough for cost-sensitive or high-volume bulk workloads without idle charges
Where it falls shortper GPT It offers fewer production-control, capacity-guarantee, and enterprise deployment options than the top two.
per Gemini Lacks custom hardware acceleration for ultra-high TPS workloads and offers minimal platform-native fine-tune hosting or orchestration capabilities.
per Grok Lower and less consistent throughput/latency than optimized production hosts; not ideal when p99 latency or peak reliability matter most
- 5GPT #5Claude #5Gemini #5Grok —
Class-leading inference speed and very high throughput can transform coding agents, search, and other sequential workloads where each generation blocks the next; it is a near-tie with Groq when raw latency dominates.
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GPT Class-leading inference speed and very high throughput can transform coding agents, search, and other sequential workloads where each generation blocks the next; it is a near-tie with Groq when raw latency dominates.
Claude Wafer-scale hardware delivers extreme speed on large open models (frontier-size Llama/Qwen tiers) at very high tokens/sec, a compelling speed-per-dollar option for latency-critical large-model serving
Gemini Record-breaking raw generation speeds exceeding 1,000+ tokens/second powered by wafer-scale hardware, dramatically reducing latency for deep reasoning and code generation tasks.
Where it falls shortper GPT Its relatively small supported-model catalog makes it unsuitable when breadth, custom models, or multimodal coverage matters more than speed.
per Claude Limited model selection and capacity availability; a specialist tool, not a general-purpose catalog, and less proven for broad production breadth
per Gemini Narrow catalog limited to standard open-weights (e.g., Llama) with rigid hardware constraints that prevent custom model modifications or complex heterogeneous model deployments.
- 6GPT —Claude #4Gemini —Grok —
The strongest choice for enterprises already on AWS — fully serverless, no infra, first-party access to Anthropic, Meta, Mistral, Amazon Nova and others, plus IAM, VPC, guardrails, and provisioned throughput for compliance-bound workloads
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Claude The strongest choice for enterprises already on AWS — fully serverless, no infra, first-party access to Anthropic, Meta, Mistral, Amazon Nova and others, plus IAM, VPC, guardrails, and provisioned throughput for compliance-bound workloads
Where it falls shortper Claude Higher latency and weaker price/perf on open models than specialist providers; AWS lock-in and slower access to the newest community models
- 7GPT —Claude —Gemini —Grok #5
Single OpenAI/Anthropic-compatible endpoint covering both strong open weights and closed frontier models, competitive speeds on key models (tops independent benchmarks on several), production features (router, batch at ~50% cost, prompt caching, guardrails), simple prepaid economics, and rapid 2026 traction for teams wanting one bill and minimal provider sprawl
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Grok Single OpenAI/Anthropic-compatible endpoint covering both strong open weights and closed frontier models, competitive speeds on key models (tops independent benchmarks on several), production features (router, batch at ~50% cost, prompt caching, guardrails), simple prepaid economics, and rapid 2026 traction for teams wanting one bill and minimal provider sprawl
Where it falls shortper Grok Newer platform with less long-term production track record than Fireworks/Together and still evolving rate-limit/SLA maturity
Rank history
Just missed the top 5
GPT Cloudflare Workers AI — excellent edge integration and developer value, but weaker model breadth and inference specialization than the leaders · Baseten — strong production platform for custom-model deployment, but less compelling for practitioners seeking a broad, inexpensive shared serverless model catalog
Claude OpenRouter — excellent unified router across providers with fallback/price routing, but it's an aggregation/routing layer rather than a first-party serverless inference engine · Replicate — great for custom, multimodal, and long-tail models with per-second billing, but not optimized for lowest-cost high-throughput LLM token serving
Gemini Cloudflare Workers AI — exceptional global edge integration and zero-cold-start compute, but limited context lengths and lower throughput ceilings for heavy LLM reasoning · Replicate — outstanding multi-modal catalog and rapid prototyping UX, but higher latency and cost per token compared to dedicated high-performance inference engines
Grok Cerebras — extreme sustained throughput on wafer-scale but capacity/waitlist and narrower practical access for typical users · OpenRouter — excellent multi-provider gateway with fallbacks but adds a routing layer and is not a pure inference host
By model
ChatGPT
- 1.Fireworks AI
- 2.Together AI
- 3.Groq
- 4.DeepInfra
- 5.Cerebras
Claude
- 1.Fireworks AI
- 2.Together AI
- 3.Groq
- 4.Amazon Bedrock
- 5.Cerebras
Gemini
- 1.Together AI
- 2.Groq
- 3.Fireworks AI
- 4.DeepInfra
- 5.Cerebras
Grok
- 1.Fireworks AI
- 2.Groq
- 3.Together AI
- 4.DeepInfra
- 5.DigitalOcean Serverless Inference
Common questions
What is the best serverless llm inference api according to AI models?
Fireworks AI leads. 3 of 4 models rank Fireworks AI the top pick. The current top 3: Fireworks AI, Together AI, Groq. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-08-14. Source: modelsagree.com.
Which serverless llm inference api did each AI model pick first?
ChatGPT: Fireworks AI. Claude: Fireworks AI. Gemini: Together AI. Grok: Fireworks AI.
Do the AI models agree on the best serverless llm inference api?
Not unanimous. Gemini picks Together AI.
What changed in the latest serverless llm inference api ranking?
In the latest poll (2026-08-14): Amazon Bedrock and DigitalOcean Serverless Inference entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this serverless llm inference api ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best serverless LLM inference API” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-08-14. https://modelsagree.com/best/best-serverless-llm-inference-api (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand