Together AI vs vLLM
Together AI vs vLLM: side-by-side comparison of two inference platforms & gateways systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Together AI | vLLM | |
|---|---|---|
| Cost tier | mid | searched not found |
| $/Mtok input | — | searched not found |
| $/Mtok output | — | searched not found |
Where they differ (9)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Together AI | vLLM | |
|---|---|---|
| Cost tier | mid | searched not found |
| Type | Inference + fine-tuning cloud | Fast LLM inference engine |
| Created | 2022 | 2023 |
| Latest release | v3.0.0 (2026-10-22) — together (Python SDK) | v0.32.0 (2026-10-18) |
| License | proprietary; Python SDK Apache-2.0 | Apache-2.0 |
| GitHub | searched not found | vllm-project/vllm — ~95.4k stars, last commit 2026-10 |
| Pricing | Serverless pay-per-token (e.g. $0.15–$3.00 per 1M input tokens); dedicated endpoints per GPU-hour (H100 $5.49/hr); fine-tuning per 1M training tokens; GPU clusters from $3.19–$3.99 per H100-hour | Free (open source, Apache-2.0) |
| Funding | $800M Series C 2026-07-01 (Aramco Ventures, NVIDIA, Vista Equity, General Catalyst, Salesforce Ventures and others); earlier rounds: $20M seed, $102.5M Series A, $305M Series B at $3.3B valuation | searched not found |
| Deployment | Managed-only — serverless and dedicated inference, fine-tuning, GPU clusters | Self-hosted only — pip / Docker inference server with OpenAI-compatible API; managed hosting only via third-party clouds |
At a glance
| Together AI | vLLM | |
|---|---|---|
| Section | Inference platforms & gateways | Inference platforms & gateways |
| Tier | T1 | T1 |
| Type | Inference + fine-tuning cloud | Fast LLM inference engine |
| Created | 2022 | 2023 |
| Latest release | v3.0.0 (2026-10-22) — together (Python SDK) | v0.32.0 (2026-10-18) |
| License | proprietary; Python SDK Apache-2.0 | Apache-2.0 |
| GitHub | searched not found | vllm-project/vllm — ~95.4k stars, last commit 2026-10 |
| Pricing | Serverless pay-per-token (e.g. $0.15–$3.00 per 1M input tokens); dedicated endpoints per GPU-hour (H100 $5.49/hr); fine-tuning per 1M training tokens; GPU clusters from $3.19–$3.99 per H100-hour | Free (open source, Apache-2.0) |
| Funding | $800M Series C 2026-07-01 (Aramco Ventures, NVIDIA, Vista Equity, General Catalyst, Salesforce Ventures and others); earlier rounds: $20M seed, $102.5M Series A, $305M Series B at $3.3B valuation | searched not found |
| Deployment | Managed-only — serverless and dedicated inference, fine-tuning, GPU clusters | Self-hosted only — pip / Docker inference server with OpenAI-compatible API; managed hosting only via third-party clouds |
| API surface | searched not found | searched not found |
| Multi-tenancy | searched not found | searched not found |
| MCP | searched not found | searched not found |
| A2A | searched not found | searched not found |
| OpenTelemetry | searched not found | searched not found |
| Optimised for | searched not found | searched not found |
| Anti-fit | searched not found | searched not found |
Pros & cons
Together AI
Pros: searched not found
Cons: searched not found
vLLM
Pros: searched not found
Cons: searched not found