# Together AI AI visibility in AI/ML Infrastructure & LLM Tools

Canonical: https://devtune.ai/verticals/aiml-infrastructure-llm-tools/together-ai

[Website](https://www.together.ai/)

Updated: 2026-10-19T08:44:26.587154+00:00
Prompts: 25
Runs: 6


## Platforms

- chatgpt-search
- bing-copilot-search
- google-ai
- perplexity
- google-ai-mode
- xai-search

Rank: 10
Total brands: 13
Measured responses: 150
Presence percent: 0.6666666666666667
Share of voice percent: 0.966183574879227
Average position: 4.5
Docs presence percent: 0.6666666666666667
Blog presence percent: 0
Brand mention percent: 6.666666666666667


## Profile

Overview: Together AI is a Menlo Park, California–based AI infrastructure company founded in 2022 that operates what it calls the 'AI Native Cloud' — a full-stack platform for running, fine-tuning, and training open-source AI models. The platform provides serverless and dedicated inference APIs across 200+ models, GPU cluster rentals spanning H100 through NVIDIA Blackwell GB300, fine-tuning tools supporting LoRA and DPO, a code sandbox, managed storage, and proprietary systems research including FlashAttention and ATLAS speculative decoding. Together AI claims approximately 2× faster inference than comparable platforms through its custom kernel work. The company serves over 450,000 developers and named enterprise customers including Cursor, Salesforce, Decagon, Hedra, and The Washington Post. It raised a $305M Series B at a $3.3B valuation in February 2025, backed by General Catalyst, NVIDIA, Kleiner Perkins, and Salesforce Ventures.
Product summary: Together AI provides a full-stack 'AI Native Cloud' purpose-built for AI model deployment and development, combining serverless and dedicated LLM inference across 200+ open-source models, NVIDIA GPU cluster compute from H100 through Blackwell GB300, fine-tuning, evaluations, a code sandbox, and managed storage — all underpinned by proprietary systems research in inference optimization (FlashAttention, ATLAS, ThunderKittens) and an OpenAI-compatible API surface.


### Key capabilities

- Serverless inference API covering 200+ open-source models across chat, vision, image, audio, video, embeddings, and reranking modalities
- Dedicated model inference on single-tenant GPU hardware with guaranteed performance and autoscaling
- Batch inference API for async large-scale workloads at up to 50% lower cost than serverless
- GPU cluster provisioning (H100, H200, B200, GB200 NVL72, GB300 NVL72) with on-demand and reserved pricing
- Fine-tuning platform supporting LoRA and full fine-tuning via SFT and DPO, with no infrastructure management required
- Proprietary inference research deployed to production (FlashAttention-3/4, ATLAS runtime-learning speculative decoding, ThunderKittens GPU kernels)
- Code Sandbox for secure, scalable code execution integrated with LLM calls
- OpenAI-compatible API for seamless migration from existing integrations
- Model Evaluations API with LLM-judge scoring and automated reports
- AI Factory offering custom frontier-scale infrastructure deployments (GB200/GB300 clusters, custom power capacity)



### Target users

- AI-native SaaS companies and startups deploying open-source LLMs in production
- ML engineers and researchers requiring fine-tuning, training, and GPU cluster access
- Enterprise development and platform teams seeking OpenAI-compatible alternatives to closed-source providers
- Generative media companies (video, audio, image) needing scalable, low-latency inference
- Academic and research labs requiring large-scale NVIDIA GPU compute on demand
- AI application developers building RAG pipelines, agents, and voice AI products



### Key use cases

- Production LLM inference for AI-native SaaS applications requiring low latency and high throughput
- Fine-tuning open-source models on proprietary datasets for domain-specific accuracy
- High-throughput batch processing of text, image, and multimodal content at scale
- GPU cluster rental for LLM pre-training, RL post-training, and research workloads
- Real-time voice AI and sub-second latency agentic application serving
- Deploying custom generative media models (video, image, audio) in production
- Building RAG pipelines using integrated embedding models, vector store connectors, and reranking
- Rapid prototyping across 200+ open-source models via a unified OpenAI-compatible API

Integrations ecosystem: Together AI offers an OpenAI-compatible REST API enabling drop-in replacement of existing integrations by changing the base URL. Officially documented integrations span: agent frameworks (LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, AutoGen/AG2, PydanticAI, Agno, Arcade.dev); vector databases and data stores (Pinecone, MongoDB, Pixeltable); LLM observability (Helicone, Composio); and developer SDKs (Vercel AI SDK, HuggingFace Inference). The platform supports Bring Your Own Model (BYOM) via Dedicated Container Inference and is available on the AWS Marketplace. Strategic model and infrastructure partnerships include NVIDIA (Blackwell GPU access), Cartesia (Sonic voice AI), Black Forest Labs (FLUX image models), MiniMax, and Hugging Face.
Pricing summary: Together AI uses pay-per-use pricing across all product lines. Serverless inference is billed per million tokens: small models start at approximately $0.03–$0.10/1M input tokens; large reasoning models such as DeepSeek R1 cost up to $3.00/1M input and $7.00/1M output tokens. Batch inference is priced at up to 50% below serverless rates. Fine-tuning starts at $0.48/1M tokens for LoRA SFT on models up to 16B parameters, scaling to specialized model pricing (e.g., $10/1M for DeepSeek-class models). GPU clusters are available on-demand (H100 at $3.49/hr, H200 at $4.19/hr, B200 at $7.49/hr) with reserved discounts for commitments of one week or longer (e.g., H100 from $2.55/hr at 4–6 months). Dedicated inference instances start at $3.99/hr (H100). Sandbox compute is billed at $0.0446/vCPU/hr and $0.0149/GiB RAM/hr. Enterprise-tier, AI Factory, and GB200/GB300 cluster pricing require direct sales contact.
Review summary: Third-party verified review data for Together AI's cloud platform is sparse on major review platforms as of research date. Developer community analysis and third-party assessments highlight strong inference throughput (~400 tokens/sec in production), ease of OpenAI API compatibility enabling rapid migration, broad open-source model selection, and competitive pricing relative to closed-source providers. Identified criticisms across community sources include a learning curve for full platform utilization, technical expertise requirements, occasional documentation gaps, and some payment or access friction. Enterprise customers (Salesforce, Cursor, Hedra, Decagon, Zomato) publicly report strong outcomes around cost reduction, latency improvement, and scaling reliability.
Competitive positioning: Together AI positions itself as the 'AI Native Cloud' — a research-grounded, full-stack alternative to both legacy cloud providers and narrow inference API services. It differentiates through proprietary inference research (FlashAttention, ATLAS speculative decoding, ThunderKittens kernels), claiming approximately 2× faster inference than comparable platforms, an OpenAI-compatible API surface covering 200+ open-source models, and an integrated stack spanning serverless inference, dedicated GPU clusters, fine-tuning, and sandbox environments. Compared to Fireworks AI and Replicate, Together AI offers a broader compute and training surface; versus hyperscalers, it provides a model-native, open-source-first developer experience at more competitive per-token economics.
Limitations: Together AI's platform is primarily optimized for open-source models; teams requiring proprietary closed-source frontier models (GPT-4o, Claude) are not directly served. Third-party review data is sparse, with very few verified reviews on major platforms, making systematic user sentiment analysis difficult. Some community feedback identifies a learning curve for full platform utilization and occasional documentation gaps. Sustained large-scale GPU cluster usage represents significant spend, with enterprise-tier and AI Factory pricing requiring direct sales engagement. The platform's published compliance and SLA documentation is limited in publicly available detail.


### Source urls

- https://www.together.ai/
- https://www.together.ai/pricing
- https://www.together.ai/about-us
- https://www.together.ai/customers
- https://www.together.ai/customers/cursor
- https://docs.together.ai/docs/integrations
- https://www.together.ai/blog/together-ai-announcing-305m-series-b
- https://www.prnewswire.com/news-releases/together-ai-raises-305m-series-b-to-scale-ai-acceleration-cloud-for-open-source-and-enterprise-ai-302380967.html
- https://www.datacenterdynamics.com/en/news/together-ai-seeks-1bn-in-funding-report/
- https://sacra.com/c/together-ai/
- https://taptwicedigital.com/stats/together-ai
- https://www.g2.com/products/together-ai-together/reviews

Reviewed at: 2026-04-28T23:16:48.526+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Cursor | Cursor partnered with Together AI to deploy real-time coding inference on NVIDIA Blackwell (GB200 NVL72), establishing a repeatable quantization pipeline from new model weights to a production-like test endpoint. | 72 GB200 GPUs in production; days from weights to test endpoint |
| Decagon | Decagon used Together AI's inference and fine-tuning infrastructure to power sub-second voice AI, achieving a significant cost reduction compared to competing closed-source API providers. | 6x cost reduction per turn vs. GPT-5 mini |
| Hedra | Hedra scales viral AI video generation using Together AI's Dedicated Container Inference and GPU clusters, absorbing traffic surges without performance degradation. | 60% cost savings |
| Salesforce | Salesforce AI Research migrated inference workloads to Together AI, achieving measurably faster response times and lower infrastructure spend compared to prior providers. | 2x latency reduction; ~33% cost savings |
| Vercept | Vercept achieved a major inference performance breakthrough with Together AI after standard inference frameworks failed to meet latency and throughput requirements. | 11x faster inference |
| Zomato | Zomato built an AI customer support bot on Together AI's inference platform that doubled customer satisfaction and scaled to handle high request volumes. | 2x CSAT score; scaled to 1,000+ messages per minute |



### Reviews breakdown





### Review themes



#### Praised

- Fast inference speed (~400 tokens/sec reported in production)
- OpenAI API compatibility for seamless migration
- Large and diverse open-source model selection (200+)
- Competitive pricing vs. closed-source API providers
- Strong documentation, cookbooks, and code examples
- Eliminates GPU cluster management overhead for teams
- Research-backed performance improvements (FlashAttention, ATLAS)



#### Criticized

- Technical expertise required for full platform utilization
- Very sparse verified reviews on major third-party platforms
- Occasional documentation gaps noted by some users
- Payment and access friction reported by a subset of users
- No direct support for closed-source frontier models (GPT-4o, Claude, etc.)




### Company facts

Founded year: 2022
Hq: Menlo Park, CA, USA


#### Founders

- Vipul Ved Prakash
- Ce Zhang
- Chris Ré
- Tri Dao
- Percy Liang

Employees range: ~197
Total funding: ~$537M
Valuation: $3.3B (Feb 2025 Series B)
Arr: ~$300M (Sept 2025, est. Sacra)
Customer count: 450,000+ developers
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Braintrust | 18 | 150 | 12 | 5.813953488372093 |
| MLflow | 14 | 150 | 9.333333333333334 | 7.193548387096774 |
| LangChain | 12 | 150 | 8 | 5.181818181818182 |
| Langfuse | 8 | 150 | 5.333333333333334 | 4.935483870967742 |
| LiteLLM | 8 | 150 | 5.333333333333334 | 8.84 |
| Modal | 6 | 150 | 4 | 4.875 |
| Replicate | 6 | 150 | 4 | 7 |
| Weights & Biases | 4 | 150 | 2.666666666666667 | 9.705882352941176 |
| Helicone | 2 | 150 | 1.3333333333333335 | 4.5 |
| Together AI | 1 | 150 | 0.6666666666666667 | 4.5 |
| Comet ML | 1 | 150 | 0.6666666666666667 | 6.5 |
| Fireworks AI | 1 | 150 | 0.6666666666666667 | 12.5 |
| Anyscale | 0 | 150 | 0 | Not available |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| chatgpt-search | 0 | 0 |
| bing-copilot-search | 0 | 0 |
| google-ai | 0 | 0 |
| perplexity | 1 | 4 |
| google-ai-mode | 0 | 0 |
| xai-search | 0 | 0 |



## Strengths





## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about? | 4 |
| Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs? | 4 |
| What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure? | 3 |
| Which LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side? | 3 |
| Looking for an LLM evaluation platform a solo engineer can get running in a day without deep ML expertise — what are my options? | 3 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 0 |
| Developer Experience | 5 | 0 |
| Integrations & Ecosystem | 5 | 0 |
| Performance & Reliability | 5 | 0 |
| Setup & First Run | 5 | 1 |



## Prompt results

- Prompt text: What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |
| Braintrust | 5 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 4 |



##### Google-ai

| Display name | Position |
| --- | --- |
| MLflow | 2 |
| Braintrust | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which LLM orchestration frameworks handle long-running multi-agent workflows reliably — including surviving infrastructure restarts when a task takes hours?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 14 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 2 |



##### Google-ai-mode





##### Xai-search




- Prompt text: I'm evaluating managed LLM inference platforms versus self-hosted GPU instances for a high-traffic workload — what are the key trade-offs and what should I look at?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search





##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| MLflow | 7 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 1 |
| Replicate | 6 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Modal | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Modal | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Modal | 1 |
| Replicate | 3 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What's the easiest LLM gateway to set up that adds caching, rate limiting, and cost tracking across multiple model providers without custom code?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |
| Helicone | 7 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 5 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search





##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Helicone | 2 |
| LiteLLM | 3 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which AI/ML platforms have the best compliance story for SOC 2 and data residency — ensuring training data and model outputs stay in a specific region?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search





##### Bing-copilot-search





##### Google-ai





##### Perplexity





##### Google-ai-mode





##### Xai-search




- Prompt text: Which LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 4 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 3 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which managed LLM inference platforms handle cold starts well — is there a way to keep a model warm without paying for idle GPU time?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 3 |
| Replicate | 5 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Replicate | 3 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which LLM orchestration frameworks are best for onboarding a software engineering team with no ML background — what's realistic for the first week?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 7 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 4 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 4 |
| MLflow | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| MLflow | 1 |
| Braintrust | 5 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 3 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 4 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Looking for an LLM evaluation platform a solo engineer can get running in a day without deep ML expertise — what are my options?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 2 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| MLflow | 1 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| LangChain | 3 |
| Langfuse | 4 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which AI infrastructure platforms support running the same orchestration logic locally against a mock LLM before deploying to production?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 2 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What ML experiment tracking tools handle multi-user collaboration well — so multiple data scientists can work on the same project without stepping on each other's runs?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search





##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Weights & Biases | 1 |
| MLflow | 3 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What tools let you set up a RAG pipeline evaluation framework to measure retrieval quality and answer accuracy before going to production?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search





##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 7 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What LLM gateway or routing tools support automatic fallback when a primary model provider goes down in production?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which AI observability tools are best at detecting prompt injection attempts and guardrail violations in production LLM apps?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search





##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What ML platforms handle dataset versioning alongside model versioning so you can reliably reproduce a training run from six months ago?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Weights & Biases | 8 |
| MLflow | 20 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| MLflow | 4 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What LLM infrastructure platforms give the best cost-to-latency balance for a high-throughput app doing 10,000 requests per hour?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 4 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity





##### Google-ai-mode





##### Xai-search




- Prompt text: What are the best ML experiment tracking tools for a team currently logging metrics to spreadsheets — which ones get you value fast with minimal setup?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Weights & Biases | 1 |
| MLflow | 3 |
| Comet ML | 6 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| MLflow | 2 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What AI infrastructure platforms handle multi-model setups well — letting you switch between LLM providers and open-source models without rewriting application code?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 13 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Google-ai-mode





##### Xai-search




- Prompt text: What platforms can affordably serve a fine-tuned 7B parameter model with low latency for a production app without requiring a dedicated ML team?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: 4
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 1 |
| Replicate | 8 |
| Fireworks AI | 12 |



##### Bing-copilot-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Replicate | 3 |
| Together AI | 4 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Weights & Biases | 1 |
| MLflow | 3 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| MLflow | 3 |



##### Google-ai

| Display name | Position |
| --- | --- |
| MLflow | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| MLflow | 1 |



##### Google-ai-mode





##### Xai-search




- Prompt text: Which LLM observability platforms support exporting trace data to BigQuery or Snowflake for custom analysis?


#### Brand position by platform

Chatgpt-search: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Perplexity: Not available
Google-ai-mode: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 3 |



##### Perplexity





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Xai-search







## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://www.together.ai/pricing | Pricing \| Together AI | together.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b37fa1a1-08a3-4432-b0cd-d464482e8423/20f6fe2f18c1566a58e7990784a14dc517f8596d.png | commercial | product_page | 5 | 5 |
| https://docs.together.ai/docs/dedicated-endpoints/pricing | Pricing | docs.together.ai | Not available | commercial | documentation | 2 | 2 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about? | google-ai | While many serverless GPU platforms focus tightly on low-latency inference (like Replicate or Together AI), several robust options explicitly support on-demand fine-tuning and batch-style job execution. |
| Which managed LLM inference platforms handle cold starts well — is there a way to keep a model warm without paying for idle GPU time? | google-ai | Multi-Tenant Serverless Pools (The Provider's Trick) On true serverless endpoints (like AWS Bedrock, Together AI, or Perplexity's API tiers), you never pay for idle hardware because the provider pools requests across thousands of customers. |
| What LLM infrastructure platforms give the best cost-to-latency balance for a high-throughput app doing 10,000 requests per hour? | google-ai | Together AI / Fireworks AI * The Balance: Exceptional. They specialize in ultra-fast open-weight inference (hosting models like Llama and DeepSeek variants) using heavily optimized runtime engines like vLLM. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| chatgpt-search | Modal | ### 1. Modal Best general-purpose training runtime Run your own PyTorch, Hugging Face TRL, Unsloth, or custom training code. |
| bing-copilot-search | Modal | ...Platforms Supporting Fine‑Tuning Platform\| Fine‑Tuning Support\| GPU Options\| Practical Limits / Notes ---\|---\|---\|--- Modal\| Full fine‑tuning, LoRA, QLoRA\| B200, H200, H100, A100, L40S, RTX Pro 6000, etc.\| Code‑first SDK; persistent volumes for... |
| google-ai | Modal | Modal ### Key Serverless GPU Platforms Supporting Fine-Tuning * Modal: Widely regarded as a top choice for code-first serverless ML. |
| perplexity | Modal | \| Platform \| Fine-tuning support \| Practical limits to plan around \| \|---\|---\|---\| \| Modal \| Yes. |
| perplexity | Replicate | [1][2] \| \| Replicate \| Yes. Replicate supports user-provided training data and fine-tunes, with a training API and a model destination. |
| chatgpt-search | Weights & Biases | My shortlist is Weights & Biases (W&B), MLflow, TensorBoard, Aim, and ClearML. |
| chatgpt-search | MLflow | My shortlist is Weights & Biases (W&B), MLflow, TensorBoard, Aim, and ClearML. |
| bing-copilot-search | MLflow | Best options for minimal-code PyTorch experiment tracking: If you want to start logging runs with almost no changes to your training loop, the most seamless integrations are Tracelet , ClearML , and MLflow. |
| google-ai | MLflow | ...ses (W&B) \| Low (1–3 lines) \| Rich visual dashboard & hardware tracking \| `wandb.init()` + `wandb.watch(model)` \| \| MLflow \| Lowest (1 line) \| Built-in autologging & model registry \| `mlflow.pytorch.autolog()` \| \| Neptune.ai \| Low \|... |
| perplexity | MLflow | PyTorch Lightning + MLflow: Probably the closest to “turn it on and train.” |
| chatgpt-search | LangChain | ## Best tools for agent pipeline debugging ## 1. LangSmith Best for LangChain / LangGraph Best when you want a visual execution tree for a multi-step agent. |
| chatgpt-search | Langfuse | LLM Monitoring for Cost & Latency Website: LangSmith ## 2. Langfuse Best open-source option A strong choice for inspecting complete request traces, nested spans, generations, tool calls, and their inputs and outputs. |



## Trend

Visibility delta: -1.25
Avg position delta: Not available
Citation count delta: -2