Together AI
Cloud platform for OSS LLM inference and fine-tuning — serverless inference, dedicated endpoints, fine-tuning API, training cluster.
At a glance
- Type
- Inference + fine-tuning cloud
- Tier
- T1
- Section
- Inference platforms & gateways
- Created
- 2022
- Latest release
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- License
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- GitHub
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- Pricing
- Pay-per-token / dedicated GPU
- Funding
- $228.5M total raised through Series A 2024-03 ($106M, Salesforce Ventures led; $1.25B valuation); Series B 2024-11 reportedly $200M+ at $3.3B.
Taxonomy
- storage
- n/a
- retrieval
- n/a
- persistence
- n/a
- update
- n/a
- unit
- n/a
- governance
- n/a
- conflict
- n/a
When to use
Optimised for: searched not found
Anti-fit: searched not found
Pros & cons
Pros
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Cons
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Claims & capabilities
$305M Series B (Feb 2025) at ~$3.3B; Salesforce, Snowflake among early customers.
Technical surface
- API surface
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- Backend storage
- not applicable — not a memory product
- Deployment
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- Embedding model
- not applicable — not a memory product
- Multi-tenancy
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- MCP
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- A2A
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- OpenTelemetry
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Similar systems
Other inference platforms & gateways in the catalog, ranked by inbound references.
- LiteLLM T1
BerriAI's open-source LLM gateway — unified OpenAI-format API for 100+ providers; LiteLLM Proxy adds budgets, fallbacks, observability, rate-limiting.
- vLLM T1
Open-source LLM inference engine with PagedAttention — high-throughput batching, paged KV-cache. UC Berkeley origin; de-facto OSS inference stack.
- Anyscale T1
Commercial platform built on Ray — distributed training, fine-tuning, serving. Anyscale Endpoints provides hosted OSS inference; ray cluster is the substrate underneath OpenAI's training stack.
- Baseten T1
Inference platform for ML models — Truss package format, Chains for multi-model workflows, autoscaling GPU/CPU serving. Targeted at production teams shipping LLM/diffusion endpoints.
- Fireworks AI T1
Fast inference platform for OSS LLMs — custom Cuda kernels, speculative decoding, multi-LoRA serving. Targets latency-sensitive production use cases.
- Modal T1
Serverless cloud for AI/ML — Python-decorator workflow defines GPU/CPU functions deployed to managed infra; widely used for training jobs, batch inference, and agent backends.