# Replicate AI vs Sieve

Replicate AI records 904,038 monthly visits with -8.4% month-over-month growth and Paid pricing. Sieve records 124 monthly visits with +1.6% growth and Contact for Pricing pricing.

| Metric | [Replicate AI](https://openaitools.app/tool/replicate-ai) | [Sieve](https://openaitools.app/tool/sieve) |
| :--- | :--- | :--- |
| Website URL | https://replicate.com | https://www.sieve.ai |
| Categories | AI API, AI Models, AI Developer Tools, Open Source AI Models | AI API, AI Dubbing, AI Video Translator, AI Video Editor |
| Pricing Model | Paid | Contact for Pricing |
| Monthly Visits | 904,038 | 124 |
| MoM Growth Rate | -8.4% | +1.6% |
| Primary Market | United States | Global |
| Direct Visits | 577,501 | 54 |
| Review Score | 4.6 | 4.1 |
| Saved Count | 5 | 0 |
| Supported Platforms | Website | Website |

## Replicate AI

Replicate's core pitch is a uniform interface over thousands of models. Models range from image generators such as FLUX, GPT Image, Nano Banana, Seedream, and Recraft, to video models from MiniMax, ByteDance, xAI, and Alibaba, plus speech, music, captioning, image restoration, and large language models. Each model page exposes concrete inputs, versioned releases, and cost estimates, and you call them the same way regardless of the underlying framework. The catalog blends community-contributed open-source models with officially maintained proprietary ones, and the company positions every listed model as actually working with a production-ready API rather than a demo.

Beyond running hosted models, Replicate is a deployment and training layer. Cog, the project's open-source packaging tool, converts a model into a container with a generated HTTP API, so your own weights can be served from the same interface. Public models run on shared infrastructure and you pay only for processing time, while private models run on dedicated hardware and are billed for the full time instances are online — setup, idle, and active — with an exception for fast-booting fine-tunes that only bill when processing. Automatic scaling handles traffic spikes without manual capacity planning.

Pricing is strictly consumption-based. Public models are usually billed per second of runtime according to the hardware selected, with some billed per input and output instead; published examples include Flux 1.1 Pro at $0.04 per output image and DeepSeek R1 at $3.75 per million input tokens. Hardware rates are public too, from a T4 at roughly $0.81 per hour up to eight H100s at about $43.92 per hour. You can start free, and enterprise contracts unlock additional multi-GPU A100, H100, and H200 capacity.

**Pricing & Plans:** Paid, usage-based — you only pay for what you use. Most public models are billed by runtime at per-second hardware rates; some models are billed per input and output (for example, $0.04 per output image on Flux 1.1 Pro, $3.75 per million input tokens on DeepSeek R1). Published GPU hardware ranges from about $0.81/hr for a T4 up to roughly $43.92/hr for 8x H100. Private deployments bill for setup, idle, and active time on dedicated hardware, and you can start for free. Enterprise and volume discounts available.

- **Thousands of models behind one API:** Run image, video, audio, speech, and language models through a uniform API surface, with official models for FLUX, Nano Banana, Seedream, Grok Imagine, MiniMax, Wan, Seedance, and more.
- **One-line-of-code inference:** Set up a model in minutes and make an API call in Python, Node, or plain HTTP after authenticating with an API token.
- **Cog packaging and custom deployments:** Package your own model with Cog, an open-source tool for packaging machine learning models, and deploy it on dedicated hardware that scales up and down with demand.
- **Model fine-tuning:** Fine-tune models on your own data, including fast-booting fine-tunes that are billed only while actively processing requests rather than for idle time.
- **Published hardware and inference rates:** Transparent per-second hardware rates across CPU, T4, L40S, A100, H100, and H200 configurations, with committed-spend options for multi-GPU capacity.

**Strengths:** Very broad model catalog spanning image, video, audio, speech, and LLMs behind one consistent API; Usage-based billing with openly published per-second hardware and per-output model rates; Cog makes it straightforward to package and deploy your own private models; Automatic scaling handles traffic spikes without manual capacity planning

**Limitations:** Costs are consumption-based and can rise quickly with heavy traffic or always-on private deployments; Private models bill for idle and setup time, not just request processing, unless they are fast-booting fine-tunes; Long-running GPU models can queue on shared public capacity during peak periods; It is an infrastructure layer, so you still need your own prompts, pipelines, and application code

[Profile (Markdown)](https://openaitools.app/tool/replicate-ai.md)

## Sieve

Sieve began life as a video API company offering endpoints for translation, dubbing, background removal, lipsync, autocrop and transcription. That product line has since been superseded: the company published an announcement titled "Reintroducing Sieve" describing its transition from video APIs to supplying multimodal data at petabyte scale, and the original sievedata.com domain now resolves to sieve.ai.

Today Sieve operates as a data lab for what it calls real-world intelligence. Instead of selling individual API calls, it works with frontier AI companies to design, capture, annotate and deliver high-quality training and evaluation data. Its public offerings include egocentric first-person video of economically valuable work with hand-pose and sensor annotations, custom multi-camera studio recordings, simulated and real-world robotics evaluations, virtual-world capture with synchronised control inputs and camera metadata, and datasets targeting motion, causality and spatial reasoning.

Underneath the data products sits serious infrastructure. Sieve has published engineering work on embedding one billion videos and querying tens of billions of vectors, which is the kind of stack you need to make petabyte-scale audiovisual archives searchable for model training. The practical route in is a conversation rather than a signup: visitors are pointed at "Talk to us" and "Request data" flows, and the company is actively hiring researchers and engineers.

**Pricing & Plans:** No public price list. Sieve is positioned as a custom engagement led by a "Talk to us" / "Request data" contact flow, so commercial terms are scoped per project, data volume and capture complexity. Earlier self-serve API pricing is no longer published.

- **Egocentric data capture:** First-person recordings of economically valuable physical work, paired with hand-pose, motion and synchronised sensor annotations.
- **Studio and custom rig recordings:** Multi-camera video and audio collection with bespoke rigs and controlled environments built to a client's specification.
- **Robotics evaluations:** Both simulated and real-world test suites that measure how well robot policies perform across tasks and conditions.
- **Large-scale media infrastructure:** Petabyte-scale synchronised video and audio datasets, including published engineering work on embedding a billion videos for retrieval.
- **Real-world reasoning datasets:** Datasets and benchmarks targeting motion, causality and spatial relationships for models that must reason about the physical world.

**Strengths:** Deep specialisation in hard-to-collect embodied and egocentric data; In-house research and infrastructure proven at billion-video scale; Offers evaluations alongside datasets, not just raw media; Works directly with frontier labs, which signals high data quality standards

**Limitations:** Pricing is quote-based, so there is no self-serve cost transparency; Most offerings require an engagement rather than instant API signup; Documentation for the older video API suite is no longer reachable at the previous docs domain; Overkill for teams that only need a single simple video-processing endpoint

[Profile (Markdown)](https://openaitools.app/tool/sieve.md)

## Frequently Asked Questions

### How do Replicate AI and Sieve compare in monthly traffic?

Replicate AI has 904,038 monthly visits, while Sieve has 124. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Replicate AI has a month-over-month growth rate of -8.4%; Sieve has +1.6%.

### How does pricing compare?

Replicate AI: Paid. Paid, usage-based — you only pay for what you use. Most public models are billed by runtime at per-second hardware rates; some models are billed per input and output (for example, $0.04 per output image on Flux 1.1 Pro, $3.75 per million input tokens on DeepSeek R1). Published GPU hardware ranges from about $0.81/hr for a T4 up to roughly $43.92/hr for 8x H100. Private deployments bill for setup, idle, and active time on dedicated hardware, and you can start for free. Enterprise and volume discounts available. Sieve: Contact for Pricing. No public price list. Sieve is positioned as a custom engagement led by a "Talk to us" / "Request data" contact flow, so commercial terms are scoped per project, data volume and capture complexity. Earlier self-serve API pricing is no longer published.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Excels at routing text and multimodal prompts across various providers, but lacks Replicate's dedicated custom ML deployment. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 2 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Faster serverless execution for image and video models, but less generalized custom model deployment than Replicate. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Stronger focus on LLM inference and fine-tuning, but lacks Replicate's diverse community-curated media model catalog. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Build, train, and deploy computer vision for the physical world | [.md](https://openaitools.app/tool/roboflow.md) |
| 5 | [ImageKit](https://openaitools.app/tool/imagekit) | 947,721 | -13.7% | Freemium, Paid, Contact for Pricing | Real-time image and video APIs, agentic DAM and creative automation in one platform | [.md](https://openaitools.app/tool/imagekit.md) |
| 6 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Specializes specifically in ultra-efficient model fine-tuning, rather than providing a general-purpose model hosting API. | [.md](https://openaitools.app/tool/unsloth-ai.md) |
| 7 | [Atlas Cloud](https://openaitools.app/tool/atlas-cloud) | 777,031 | -16.4% | Paid | Offers a broad mix of generative media models via API, but lacks Replicate's massive community deployment ecosystem. | [.md](https://openaitools.app/tool/atlas-cloud.md) |
| 8 | [Fireworks](https://openaitools.app/tool/fireworks-ai) | 659,687 | +6% | Contact for Pricing | Delivers exceptionally fast inference speeds for open models, but has a narrower model selection than Replicate. | [.md](https://openaitools.app/tool/fireworks-ai.md) |
| 9 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | The data, evaluations and systems behind AI that has to be right. | [.md](https://openaitools.app/tool/scale.md) |
| 10 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Significantly cheaper pricing for running open-weight models, but offers fewer advanced custom training and deployment features. | [.md](https://openaitools.app/tool/deep-infra.md) |

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Source: https://openaitools.app/compare/replicate-ai-vs-sieve

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