We used Oden to analyze how Fireworks.ai, Together.ai, Replicate, and Anyscale stack up as AI inference platforms for real-world workloads. If you’re trying to choose where to run open models, fine-tunes, or agentic apps, the tradeoffs around performance, reliability, and cost are not obvious. Below we synthesize data from vendor docs, pricing pages, review sites (G2, Product Hunt, Trustpilot, PeerSpot), and Reddit discussions to help you make a practical choice. This guide focuses on inference and deployment, not model training from scratch.

Which AI inference platform has the best rating?

Note: Ratings come from different sites and small sample sizes. Treat them as directional, not statistically rigorous.

Takeaways

• Replicate and Together.ai have the strongest developer sentiment in public ratings, but those come from different sites and small N (39 Product Hunt reviews for Replicate; 4 G2 reviews for Together).
• Fireworks.ai and Anyscale also have very small G2 samples, so any ranking is noisy; you should treat these as anecdotes, not hard evidence.
• Broader review sites (Trustpilot) surface more operational pain—most notably for Fireworks and Replicate—around support responsiveness, billing controls, and model changes.
• G2 itself has significant use of AI-generated and incentivized reviews, which can skew toward positive scores, so you should combine ratings with your own trials.

How much do AI inference platforms really cost?

Pricing shifts frequently, and each vendor mixes token‑based billing, GPU hours, and enterprise contracts. Below is a simplified view of entry-level economics as of late 2025.

Cost patterns

• Token vs GPU hours: Fireworks and Together mostly expose token‑based pricing for serverless inference, while Replicate and Anyscale lean on GPU‑hour style pricing for custom workloads. That makes Replicate/Anyscale feel more like “infrastructure” and Fireworks/Together more like “API products.”
• Entry‑level LLM costs: For commodity 7–8B chat models (e.g., Llama 3 8B), Fireworks and Together are in a similar ballpark—roughly low‑hundreds of mills ($0.10–$0.20) per 1M tokens at the low end and approaching ~$0.90 as you scale up to 70B+ or premium variants.
• DeepSeek R1/V3: Third‑party providers (Fireworks, Together) often charge several multiples of DeepSeek’s own API pricing, trading raw cost for privacy, U.S. data residency, and enterprise features.
• Enterprise discounts: All four platforms push volume discounts and committed‑use contracts hard at scale, so effective pricing can be materially lower than list if you’re running sustained workloads.

Pricing varies by region, usage pattern, and contract terms. Always double-check current prices with each vendor's calculator or sales team.

What are the key features of each platform?

Fireworks.ai

Core positioning: Fast, production-grade inference cloud for open models, with end‑to‑end support for building, tuning, and scaling compound AI systems.

Key Features:

• Large model library (100+ text, image, audio, embedding, multimodal models) running on a proprietary inference stack, optimized for latency, throughput, and cost per token.
• FireAttention CUDA kernels (now at V4 with FP4) for multi‑query attention and advanced quantization, claiming >250 tokens/s on B200 and multi‑x speedups vs vLLM.
• Flexible deployment: serverless per‑token API, on‑demand GPUs with per‑second billing, and enterprise reserved virtual cloud across 18+ regions and multiple providers.
• End‑to‑end model lifecycle tooling: experiment (“Build”), fine‑tune (including Reinforcement Fine Tuning and Multi‑LoRA), and scale with autoscaling and BYOC options.
• Enterprise RAG stack with embeddings, re‑ranking, long‑context models, and RAG‑specific APIs.

Best For:

• Teams who want fast open‑model inference with minimal infra work and strong performance optimization.
• Enterprise RAG, agentic systems, and multi‑step workflows that need long context and high throughput.
• U.S.‑based companies needing SOC2/HIPAA/GDPR compliance, data residency control, and enterprise SLAs.

Together.ai

Core positioning: “AI acceleration cloud” providing the fastest open‑model inference engine plus GPU cloud, fine‑tuning, and multi‑modal APIs.

Key Features:

• Together Inference Engine 2.0 claims 4× decoding throughput vs vLLM and 1.3–2.5× faster tokens/s than major cloud providers for Llama 3 8B and other models.
• Broad multi‑modal API: chat, code, images, video (e.g., Veo, Sora), TTS/voice, transcription, embeddings, rerankers, and moderation.
• Serverless inference for >200 open‑source models plus Dedicated Endpoints and Instant/Reserved GPU clusters on H100/H200/B200/GB200 for custom workloads.
• Fine‑tuning platform with LoRA and full‑fine‑tune options, plus reasoning‑cluster offerings for DeepSeek R1 at dedicated price points.
• OpenAI‑compatible APIs and detailed billing/rate‑limit tiers (“Build Tiers”) that scale with spend.

Best For:

• Teams prioritizing raw inference performance on NVIDIA GPUs and large‑scale throughput.
• Companies that want both API‑level inference and direct GPU access in one vendor (Instant/Reserved clusters).
• Privacy‑sensitive users who want DeepSeek‑class reasoning hosted in the U.S. with opt‑out data logging.

Replicate

Core positioning: Developer-friendly cloud to run, fine‑tune, and deploy thousands of community and custom models with a simple “run” API.

Key Features:

• Marketplace of “thousands of models” (image, video, text, audio, etc.) contributed by the community and accessible via a uniform API.
• Custom model deployments using the open‑source Cog tool, with dedicated GPU instances (including A100, H100, B200) that auto‑scale based on demand.
• Per‑second compute billing and autoscaling so you only pay when your model is running, with the ability to scale to thousands of GPUs.
• OpenAPI schemas for both the platform and each model, making it easy to auto‑generate clients and integrate with tool‑calling agents.
• Logging, metrics, and real‑time cost monitoring on deployments.

Best For:

• Product teams that want to prototype quickly across many models (e.g., Flux, SDXL, custom LoRAs) without managing GPUs.
• Startups with custom models that need dedicated GPU deployments and simple autoscaling semantics.
• Builders who value a community ecosystem and are comfortable with infrastructure being “best effort” rather than deeply enterprise‑governed.

Anyscale

Core positioning: Managed Ray “AI Compute Engine” for any AI workload—training, batch, and online inference—plus Anyscale Endpoints for LLM APIs.

Key Features:

• Built by the creators of Ray, providing a fully managed platform for Ray‑based training, data processing, batch inference, and model serving.
• Anyscale Runtime (formerly RayTurbo) with performance and resilience improvements over open‑source Ray, including checkpointing, mid‑epoch resume, and dynamic memory management.
• Anyscale Endpoints: OpenAI‑compatible LLM inference APIs with token‑based pricing (e.g., Llama‑2 chat models with per‑M‑token rates and cheap inference for fine‑tuned variants).
• Flexible deployment: fully hosted environment or BYOC/on‑prem on your own Kubernetes or cloud account, including Azure-native service in collaboration with Microsoft.
• Deep integration with the Ray ecosystem (Data, Train, Serve, Tune, RLlib), enabling unified pipelines for multimodal and LLM workloads.

Best For:

• Organizations already invested in Ray or distributed Python who want a managed control plane and runtime for training + inference.
• Teams building complex pipelines (ETL + training + batch + online inference) that need one coherent compute substrate.
• Enterprises that require BYOC, tight governance, and hybrid/on‑prem deployments across clouds.

What are the strengths and weaknesses of each platform?

Fireworks.ai

Strengths:

• Users on G2 appreciate the wide variety of models and convenient web playground, calling it a “one stop AI model shop.”
• Vendor and third‑party writeups highlight high throughput and low latency, with FireAttention‑based quantization delivering large speedups over vLLM and material cost savings.
• Frequently cited as powering production workloads for companies like Sourcegraph, Notion, Cursor, Quora, and DoorDash, suggesting real‑world maturity at scale.
• Strong support for RAG and embeddings/reranking, which matters if you’re building knowledge assistants rather than just generic chatbots.

Weaknesses:

• Trustpilot reviews complain about aggressive quantization hurting quality, serverless models being removed without notice (breaking production), and support being slow or unresponsive on billing issues.
• Some G2 reviewers describe the platform as hard to get started and ask for better quickstart guides and onboarding.
• Reddit discussions around DeepSeek pricing note that Fireworks can be significantly more expensive than DeepSeek’s own API (e.g., ~3× price multiples), which can surprise cost‑sensitive users.

Together.ai

Strengths:

• G2 reviewers praise Together for being fast and accurate, explicitly mentioning throughput of “almost 400 tokens/sec” and strong performance in production.
• Together’s own benchmarks and third‑party coverage claim 2–3× faster inference than major hyperscalers and 4× faster tokens/s than vLLM, especially on Llama and DeepSeek.
• Reddit users often see Together as a privacy‑friendlier alternative to using DeepSeek’s own API, valuing U.S. hosting and opt‑out controls.
• Strong GPU cloud offering (Instant/Reserved clusters) appeals to infra teams that want full control over hardware without building a datacenter.

Weaknesses:

• Reddit threads report that some high‑end models (especially early DeepSeek V3/R1 deployments) were slow, unstable, or mispriced when first launched, with 503s and long latencies.
• At least one Reddit discussion questions the business model as a “GPU broker” with high top‑line ARR but thin margins, which may worry very risk‑averse buyers (though others rebut this as misunderstanding).
• G2 reviewers mention a desire for more free access and occasional slow queries on large searches, though they generally view this as acceptable trade‑offs for accuracy.

Replicate

Strengths:

• Product Hunt reviewers consistently rave about Replicate’s clean, intuitive API and ease of integrating many models with a standard “run” interface, especially for teams using Next.js/Vercel or similar stacks.
• Community docs and examples show that custom deployments scale well, with per‑second GPU billing and automatic scale‑up/scale‑down, which teams appreciate for cost control.
• Reddit community posts (e.g., around LoRA training) show active usage and helpful workflows (downloadable weights, community subs, etc.).

Weaknesses:

• Multiple Trustpilot reviews and a Reddit thread complain about poor or absent customer support, including stuck GPU deployments, inability to terminate instances via the UI, and resorting to cancelling credit cards to stop charges.
• Recent Trustpilot reviews allege unauthorized usage and billing on compromised accounts and a lack of investigation or remediation from Replicate, which is a red flag if you need strict billing controls.
• Some external scam‑analysis sites note mixed reputation scores for replicate.com, generally deeming it low‑risk but pointing out a cluster of negative user testimonials, prompting advice to proceed but stay vigilant.

Anyscale

Strengths:

• G2 reviewers highlight Anyscale’s ability to simplify scaling Ray workloads from dev to production without major code changes, praising scalability and integrated developer experience.
• Anyscale Runtime benchmarks and case studies report big throughput and cost improvements (e.g., 43× throughput and 4× better GPU utilization for some customers; ~70% batch cost savings in others).
• Tight integration with major clouds (including Azure‑native Ray service) and BYOC options makes it attractive for enterprises standardizing on Ray as their AI compute engine.

Weaknesses:

• G2 reviews mention a noticeable learning curve, especially for teams new to Ray concepts, and say documentation could be more beginner‑friendly.
• At least one Reddit thread notes confusion between “Endpoints” and the broader “Platform” when signing up, suggesting onboarding still has some rough edges.
• Ray itself has had serious security vulnerabilities (e.g., CVE‑2023‑48022 remote code execution) that have been actively exploited when clusters are misconfigured and exposed publicly; Anyscale emphasizes proper deployment hygiene, but this still makes security posture and cluster configuration a non‑trivial responsibility.

How do these platforms position themselves?

Fireworks.ai markets itself as “Fastest Inference for Generative AI” and “Build. Tune. Scale.” with a focus on being the inference cloud for open models and compound AI systems. The homepage emphasizes blazing‑fast, cost‑optimized inference, complete model lifecycle management, and enterprise‑grade security and compliance.

Together.ai positions as an AI acceleration cloud and “fast, reliable AI inference at scale,” highlighting its proprietary Inference Engine, deep partnership with NVIDIA (Blackwell/GB200 clusters), and broad multi‑modal stack. Their messaging stresses record‑breaking tokens/s, 2–3× better price‑performance than hyperscalers, and end‑to‑end support from serverless APIs to GPU factories.

Replicate uses the tagline “Run AI with an API. Run and fine‑tune models. Deploy custom models. All with one line of code.” It’s clearly developer‑first, leaning on community models, Cog‑powered deployments, and per‑second GPU billing to pitch itself as the fastest way to go from idea to production without ML infra expertise.

Anyscale presents Ray as “The AI Compute Engine” and Anyscale as “Ray—perfected,” aimed at teams hitting the AI complexity wall across data, training, and inference. Marketing focuses on scale, efficiency, and flexibility (any workload, any hardware, any cloud), plus the separation of open‑source Ray (now under PyTorch Foundation) from the commercial managed platform.

Which platform should you choose?

Below are data‑driven heuristics rather than absolutes. In practice, many teams use more than one provider.

Choose Fireworks.ai If:

1. You care most about open‑model performance and RAG at API level, not running your own Ray or Kubernetes clusters; Fireworks’ proprietary stack and FireAttention are optimized specifically for open‑source models.
2. Your workloads are latency‑sensitive (chat, coding assistants, high‑QPS search) and you’re willing to trade some quantization risk for speed and cost efficiency.
3. You want a strong RAG toolchain (embeddings, reranking, enterprise RAG use‑cases) with long context and multi‑modal support baked into the platform.
4. You can tolerate some platform volatility (e.g., model lineup changes) and are prepared to monitor quality yourself—especially if you’re leaning heavily on quantized or frontier models. Trustpilot feedback suggests you should negotiate support SLAs and guardrails for model deprecation.
5. Your security and compliance needs match their cloud model (U.S. regions, SOC2/HIPAA/GDPR, optional BYOC) and you don’t require deep control over underlying Ray/cluster infrastructure.

Choose Together.ai If:

1. Raw tokens‑per‑second and price‑performance at scale are your top priorities, especially for Llama and DeepSeek; Together’s Inference Engine 2.0 is explicitly engineered to beat vLLM and major clouds.
2. You want both APIs and GPUs from one vendor—serverless endpoints for app teams and Instant/Reserved Blackwell/H‑series clusters for infra teams, with unified billing and support.
3. You’re betting on open reasoning models like DeepSeek R1/V3 and value U.S. hosting, privacy controls, and OpenAI‑compatible APIs, even at a markup over the official API.
4. Your workloads span multiple modalities—text, images, video, audio, embeddings, rerankers—and you’d like to centralize them on one cloud without stitching multiple vendors.
5. You can handle occasional early‑launch rough edges (e.g., early DeepSeek V3 issues) and are ready to phase new models in behind feature flags while Together stabilizes them.

Choose Replicate If:

1. You want to explore and ship quickly across a huge range of community and official models, especially in generative media (images/video), without needing an infra team.
2. Your primary need is a developer‑friendly abstraction over GPUs, where you pay per second of compute and let Replicate handle server orchestration, autoscaling, and deployment.
3. You’re deploying custom models with Cog and want rolling deployments, metrics, and logs without building your own Kubernetes + CI/CD pipeline.
4. You’re comfortable with some operational risk: user reports show that support and billing dispute resolution can be slow or missing, so you should enforce strict cost caps (e.g., per‑project budgets, external monitoring) and avoid single‑point‑of‑failure reliance for mission‑critical workloads.
5. You value community and ecosystem tooling (GitHub examples, community subs, Product Hunt ecosystem) as much as formal enterprise guarantees.

Choose Anyscale If:

1. You’re standardizing on Ray as your AI compute layer and want a managed environment for training, batch, and online inference rather than a pure LLM API.
2. Your workloads are complex pipelines (multimodal ETL → training → batch inference → online serving) where having one engine and runtime (Ray + Anyscale Runtime) simplifies debugging and optimization.
3. You need hybrid/BYOC deployments—for compliance, data residency, or cost reasons—and want Anyscale to manage the control plane while you control infrastructure and security policies.
4. You’re okay investing in a learning curve (Ray concepts, Anyscale Runtime specifics) in exchange for long‑term flexibility and potential cost savings vs “pure API” providers. G2 reviews and customer case studies suggest the payoff can be large once teams are up the curve.
5. You’re sensitive to vendor lock‑in on core infra tech; Ray now lives under the PyTorch Foundation’s governance, which reduces single‑vendor risk while Anyscale competes on runtime and service quality.

Sources & links

Company Websites

• Fireworks.ai
• Together.ai
• Replicate
• Anyscale

Pricing Pages

• Fireworks.ai Pricing
• Together.ai Pricing
• Replicate Pricing
• Anyscale Pricing

Documentation

• Fireworks Docs
• Together Docs
• Replicate Docs
• Anyscale Docs

G2 Review Pages

• G2 – Fireworks AI
• G2 – Together
• G2 – Anyscale

Reddit Discussions

• Reddit – Deepseek v3 now on Together
• Reddit – What inference provider hosts DeepSeek v3?
• Reddit – Careful with using replicate.com in production
• Reddit – Critical vulnerability in Ray AI framework

Additional Resources

• FireAttention V4 announcement
• Together Inference Engine 2.0
• Anyscale Runtime blog
• Originality.ai – G2 reviews case study