Fireworks AI vs vLLM
Fireworks 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
| Fireworks AI | vLLM | |
|---|---|---|
| Cost tier | searched not found | searched not found |
| $/Mtok input | searched not found | searched not found |
| $/Mtok output | searched not found | 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.
| Fireworks AI | vLLM | |
|---|---|---|
| Type | Fast OSS LLM inference cloud | Fast LLM inference engine |
| Created | 2022 | 2023 |
| Latest release | v1.2.30 (2026-10-21) — fireworks-ai | 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; on-demand GPUs $8/hr (H100/H200), $13/hr (B200), $15/hr (B300); fine-tuning from $0.50 per 1M training tokens (LoRA SFT up to 16B) | Free (open source, Apache-2.0) |
| Funding | $77M total raised through Series B 2024-07 ($52M, Sequoia led; $552M valuation). | searched not found |
| Deployment | searched not found | Self-hosted only — pip / Docker inference server with OpenAI-compatible API; managed hosting only via third-party clouds |
| API surface | OpenAI-compatible REST API, Python SDK (fireworks-ai) | searched not found |
At a glance
| Fireworks AI | vLLM | |
|---|---|---|
| Section | Inference platforms & gateways | Inference platforms & gateways |
| Tier | T1 | T1 |
| Type | Fast OSS LLM inference cloud | Fast LLM inference engine |
| Created | 2022 | 2023 |
| Latest release | v1.2.30 (2026-10-21) — fireworks-ai | 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; on-demand GPUs $8/hr (H100/H200), $13/hr (B200), $15/hr (B300); fine-tuning from $0.50 per 1M training tokens (LoRA SFT up to 16B) | Free (open source, Apache-2.0) |
| Funding | $77M total raised through Series B 2024-07 ($52M, Sequoia led; $552M valuation). | searched not found |
| Deployment | searched not found | Self-hosted only — pip / Docker inference server with OpenAI-compatible API; managed hosting only via third-party clouds |
| API surface | OpenAI-compatible REST API, Python SDK (fireworks-ai) | 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
Fireworks AI
Pros: searched not found
Cons: searched not found
vLLM
Pros: searched not found
Cons: searched not found