Baseten vs Together AI vs Fireworks AI: Inference June 2026
Baseten vs Together AI vs Fireworks AI: Inference June 2026
Baseten’s $1.5B Series F at a $13B valuation on June 25, 2026 is the biggest news in AI inference platforms. It puts Baseten in the same fundraising tier as Together AI and Fireworks AI — the three platforms most commonly evaluated for production AI inference workloads in mid-2026. This comparison covers what each does, when to use each, pricing dynamics, and decision frameworks for AI teams.
Last verified: June 27, 2026.
TL;DR
- Baseten: Bring-your-own-model with managed deployment + enterprise tooling. $13B valuation post Series F.
- Together AI: API access to a broad catalog of hosted open-weight LLMs. Strong per-token pricing.
- Fireworks AI: Performance-optimized API for open-weight LLMs. Aggressive FP8/FP4 quantization.
- Best for custom models: Baseten
- Best for popular open-weight LLM API access: Together AI
- Best for extreme low-latency open-weight LLM inference: Fireworks AI
- Best for cost-sensitive batch inference: Together AI typically wins on per-token batch pricing
The three platforms head-to-head
| Dimension | Baseten | Together AI | Fireworks AI |
|---|---|---|---|
| Core workflow | Bring + deploy your model | API call to hosted catalog | API call to hosted catalog |
| Pricing model | Per-GPU-hour, per-cluster | Per-token | Per-token |
| Model catalog | Unlimited (bring your own) | Broad open-weight catalog | Broad open-weight catalog |
| Custom fine-tunes | Yes (core use case) | Yes (deployed endpoints) | Yes (deployed endpoints) |
| Multimodal models | Yes (full support) | Yes (image, audio) | Yes (image, multimodal) |
| Non-LLM models | Yes (any model) | Limited | Limited |
| Autoscaling | Yes (scale to zero) | Built into API | Built into API |
| Global clusters | 87 | Yes (multi-region) | Yes (multi-region) |
| Enterprise tooling | Strong (SOC 2, HIPAA, VPC, SSO) | Strong | Strong |
| Recent funding | $1.5B Series F June 2026 ($13B) | Prior raises ($2B+ valuation tier) | Prior raises ($500M-$1B+ tier) |
| Daily inference scale | 1B+ calls | High (not publicly disclosed) | High (not publicly disclosed) |
When Baseten is the right choice
Baseten’s wedge is “bring your own model, we operate it.” This is the right platform when:
You have a custom fine-tune or proprietary model. Bake-off comparison: Together and Fireworks support custom-deployed endpoints, but Baseten is built around the BYO-model workflow with stronger tooling for managing many custom models, A/B testing variants, and operationalizing internal models.
You run non-LLM models in production. Image generation, audio (Whisper, Bark, music), embeddings at scale, classical ML models, multimodal — Baseten handles all of them. Together and Fireworks have growing multimodal/image support but skew LLM-centric.
You need enterprise-grade operational tooling. Observability per request, cost attribution per workload, RBAC, audit logs, SSO, VPC peering, on-prem options, compliance certifications. Baseten’s enterprise tier is mature; smaller competitors are still catching up.
You have global low-latency requirements. The 87-cluster footprint enables geographic routing for latency-sensitive workloads (real-time agents, interactive features).
You want a single operational pane for many models. If you’re running 10+ models in production with different scaling profiles, Baseten’s operational layer is hard to replicate yourself or stitch together from multiple providers.
When Together AI is the right choice
Together AI’s wedge is the broadest, easiest API access to hosted open-weight LLMs.
You want zero-deployment access to popular open-weight models. Llama 4 (all sizes), Qwen 3.5 (all sizes), DeepSeek V3.5, Mistral Large, plus dozens of others. One API call, no deployment, no operational overhead. The catalog is broader than Fireworks AI’s.
You need cost-optimized batch inference. Together’s batch inference pricing on common open-weight models is among the most competitive in the market. For workloads where latency is not critical (overnight processing, async pipelines), Together typically wins on cost.
You want a public-API contract for portability. Together’s API is widely compatible (OpenAI-compatible endpoints for many models), making it easy to swap between providers behind a router.
You’re early-stage and don’t want infrastructure overhead. Together is the lowest-friction starting point for any team that wants to use open-weight LLMs without operating inference.
When Fireworks AI is the right choice
Fireworks AI’s wedge is performance-optimized inference for open-weight LLMs.
You need low latency on open-weight LLM inference. Fireworks’ aggressive FP8/FP4 quantization, custom CUDA kernels, and inference-stack optimization typically deliver faster time-to-first-token and higher throughput than alternatives at comparable cost.
You’re cost-sensitive and willing to accept quantization tradeoffs. FP8/FP4 quantization sometimes shows quality regression on hard tasks (frontier math, complex reasoning). For mainstream production workloads (summarization, classification, chat, simple coding), the quality is typically indistinguishable from full precision — and cost is meaningfully lower.
You’re running an inference-bound workload at scale. Fireworks’ performance optimizations matter most when inference latency or throughput is the bottleneck. For workloads where the LLM call is one step in a longer pipeline, the latency advantage matters less.
You need cutting-edge open-weight models fast. Fireworks tends to be quick to add new open-weight model releases with optimized inference. If the day a new Llama or Qwen drops matters, Fireworks is often first.
Pricing dynamics (the real cost picture)
The three platforms use different pricing models and break-even points vary by workload:
Together AI / Fireworks AI per-token pricing typically lands in these ranges (June 2026):
| Model class | Together / Fireworks (per 1M tokens, input/output) |
|---|---|
| Llama 4 70B / Qwen 3.5 72B | ~$0.40-$0.90 / ~$0.40-$0.90 |
| DeepSeek V3.5 / Qwen 3.5 Max | ~$0.27-$1.00 / ~$1.00-$3.00 |
| Smaller open-weight (8B, 13B) | ~$0.10-$0.30 / ~$0.10-$0.30 |
Baseten per-GPU-hour pricing lands roughly:
- A100 80GB: ~$2.50-$4.00/hour
- H100 80GB: ~$5.00-$8.00/hour
- H200/B200/MI300X: ~$8.00-$15.00/hour
- Cluster pricing decreases for committed capacity
Break-even math example. Suppose you serve Llama 4 70B with a ~10M tokens/day workload (5M input, 5M output) at Together pricing of ~$0.65 in / $0.65 out per 1M tokens:
- Together cost: ~$6.50/day = ~$195/month
- Baseten cost: 1× H100 at ~$5/hour 24/7 = $120/day = ~$3,600/month — only worth it at much higher utilization
At ~500M tokens/day on a single model:
- Together cost: ~$325/day = ~$9,750/month
- Baseten cost: ~2-3 H100s = $240-$360/day = ~$7,200-$10,800/month — close to break-even
At ~5B tokens/day on a single stable model:
- Together cost: ~$3,250/day = ~$97,500/month
- Baseten cost: ~10-15 H100s reserved = ~$1,200-$1,800/day = ~$36,000-$54,000/month — Baseten wins
The break-even point varies with model size, latency requirements, batch efficiency, and committed-capacity discounts. Use it as a heuristic, not a rule.
Decision framework
Are you running a custom fine-tune or non-API-catalog model?
YES → Baseten (or self-host if scale demands)
NO → Are you cost-sensitive on a high-volume open-weight LLM workload?
YES → Together AI (batch pricing) or Fireworks AI (performance + cost)
NO → Is latency-to-first-token critical?
YES → Fireworks AI
NO → Together AI (broader catalog, simpler)The multi-platform pattern (recommended)
In mid-2026, most teams shouldn’t standardize on a single inference platform:
- Default for popular open-weight LLM API access: Together AI or Fireworks AI (pick based on catalog + latency)
- Default for custom fine-tunes and multimodal: Baseten
- For frontier proprietary models (GPT-5.x, Claude, Gemini): their native APIs or hyperscaler integrations (AWS Bedrock, GCP Vertex, Azure Foundry)
- Behind a router: OpenRouter, Helicone, or Portkey to make swapping easy
This pattern adds operational complexity but provides cost optimization, vendor diversification (important after the Mythos 5 and GPT-5.6 staged-access episodes), and the ability to migrate workloads as pricing shifts.
What to watch over the next 6 months
- Baseten’s deployment of the $1.5B war chest. Expect aggressive enterprise sales hiring, infrastructure expansion, and possible acquisitions of smaller inference players.
- Together vs Fireworks consolidation. The market may not support both at full scale long-term. Watch for fundraising signals.
- Hyperscaler inference pricing. AWS Bedrock, GCP Vertex, and Azure Foundry are getting more aggressive on open-weight LLM pricing. They could squeeze the independents.
- New cost-efficient inference architectures. Sail Research (agent-specialized inference) and Unconventional AI (oscillator-based architecture) represent next-generation approaches that could change the cost frontier.
- Inference cost compression generally. Expect per-token costs on common open-weight models to drop another 30-50% by end of 2026 as competition + technical improvements compound.