What is the difference between Baseten, Together AI, and Fireworks AI?

All three are AI inference platforms, but they target different workflows. Baseten is the 'managed deployment of your own model' platform — bring a custom fine-tune, an open-weight model, or a multimodal model, and Baseten operates it for you with enterprise-grade tooling. Together AI is the 'API for hosted open-weight LLMs' platform — call a public endpoint for open models such as Kimi K3, Qwen3.8, DeepSeek V4, GLM-5.3 and MiniMax M3 (its October 2026 catalog) without deploying anything yourself. Fireworks AI is the 'extreme low-latency inference for open-weight LLMs' platform — same API-style access as Together, with Standard, Priority and Fast serving modes and aggressive performance optimization. Choose Baseten when you bring the model; Together when you want API access to popular open-weight models; Fireworks when latency or cost per token on open-weight LLMs is the deciding factor.

Which is cheapest for production AI inference?

It depends on workload shape. For per-token API consumption of common open-weight models, Fireworks AI and Together AI compete on tightly-priced per-token rates and are typically cheaper than running the same model on Baseten with reserved GPU capacity for low-to-moderate volume. For high-volume sustained inference (millions of requests/day on a stable model), Baseten's per-GPU-hour pricing model can be cheaper than per-token pricing once utilization is high enough. For bursty traffic, Baseten's autoscaling-with-scale-to-zero is more cost-effective than provisioning fixed capacity. As a rough guide: under $5K/month inference spend → Together or Fireworks; $5K-$50K with stable demand on a custom model → Baseten; $50K+ with predictable workload → self-host on raw GPU or Baseten enterprise pricing; spiky workloads → Baseten or Modal at any scale.

Which platform supports the most models?

Together AI has the broadest public catalog of open-weight models with one-line API access — dozens of LLMs (as of October 2026: Kimi K3, Qwen3.8, DeepSeek V4 Pro and V4.1 Flash, GLM-5.3, MiniMax M3, Gemma 4, Llama 3.3/4), image, video, audio, embedding and rerank models. Fireworks AI has a similar broad catalog focused on performance-optimized open-weight LLMs and image/multimodal models. Baseten supports any model you can package — including the same open-weight models plus custom fine-tunes, proprietary models, multimodal models, and non-LLM models (image generation, audio, embeddings, classical ML). Baseten has fewer pre-built public model endpoints but unlimited support for the 'bring your own model' workflow. Pick by whether you need pre-hosted convenience (Together/Fireworks) or bring-your-own flexibility (Baseten).

Should I use Baseten, Together AI, or Fireworks AI for my AI startup?

For most AI startups in mid-2026: start with Together AI or Fireworks AI for the popular-open-weight-LLM workloads you can serve via their API. This is the lowest-friction path and removes the need to operate inference infrastructure. Add Baseten when you start running custom fine-tunes, multimodal models, or any non-API-catalog model — Baseten's managed deployment is the easiest path to production for those. Add a router (OpenRouter, Helicone, Portkey) to abstract the platform layer so swapping providers is a config change. Avoid self-hosting until you have a specific cost-justified reason: predictable >$50K/month spend on a stable model, compliance requirements that prohibit multi-tenant, or specialty hardware needs. Most AI startups should keep inference managed for the first 18-24 months, then evaluate vertical integration.

Together AI vs Fireworks AI vs Baseten: how do they compare for model inference?

As of October 2026: Together AI (valued at $8.3B after an $800M Series C on July 1, 2026) sells per-token serverless access to a broad open-model catalog, plus dedicated endpoints and on-demand GPU clusters. Fireworks AI (valued at $17.5B after a $1.505B Series D on July 15, 2026, and past $1B in annualized revenue) sells per-token serverless inference tuned for speed, with Standard, Priority and Fast modes and batch at 50% of serverless prices. Baseten (valued at $13B after its June 2026 Series F, and reported by Bloomberg on October 30, 2026 to be raising at $26B–$30B) deploys your own or open models on dedicated infrastructure and, since September 29, 2026, serves open models to OpenAI enterprise customers inside Codex and the Responses API. Pick Together for catalog breadth, Fireworks for latency on open models, and Baseten for custom models and enterprise deployment.

Quick Answer

Baseten vs Together AI vs Fireworks AI: Inference November 2026

Published: June 27, 2026 • Updated: November 4, 2026

Baseten vs Together AI vs Fireworks AI: Inference November 2026

Baseten’s $1.5B Series F at a $13B valuation on June 22, 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: November 3, 2026. (First published June 27, 2026.)

What changed as of November 2026

Baseten’s next round is now reported at about $30B. Bloomberg reported on October 30, 2026 that Baseten is set to raise about $2B in two tranches, priced at $26B and then $30B. It is a reported deal, not an announced close; Baseten’s last confirmed valuation is still $13B (June 2026 Series F).
Together AI raised Kimi K3 to $3.00 / $15.00 per 1M tokens (it listed $2.70 / $13.50 on October 6). Together and Fireworks now charge the same list price for Kimi K3, Qwen3.8 Max, GLM-5.3, DeepSeek V4.1 Flash, MiniMax M3 and GLM-5.3 Flash.
Llama 3.3 70B is no longer on Together’s serverless price list (it is still offered for fine-tuning). New on both serverless lists since October: Muse Spark Open ($1.25 / $4.25 on both) and gpt-oss-120B ($0.15 / $0.60 on both). Fireworks now also lists its own Ember-1 model at $3.00 / $15.00.

What changed as of October 2026

Together AI vs Fireworks AI vs Baseten for model inference, in one paragraph: Together is the widest per-token catalog of open models, Fireworks is the fastest-growing per-token platform tuned for latency, and Baseten is where you deploy your own or open models on dedicated capacity. All three raised large rounds in the three months after this page was first written:

Together AI raised an $800M Series C at an $8.3B post-money valuation on July 1, 2026, led by Aramco Ventures. (This page previously described it only as “prior raises, $2B+ tier”.)
Fireworks AI raised a $1.505B Series D at a $17.5B valuation on July 15, 2026, and says it has passed $1B in annualized revenue and serves 40+ trillion tokens a day. (Previously “$500M–$1B+ tier”.) It is now the most highly valued of the three.
Baseten announced a partnership with OpenAI on September 29, 2026: OpenAI enterprise customers can use open models served by Baseten inside Codex and the Responses API, paid from existing OpenAI commitments. Bloomberg reported on September 23–24 that Baseten is in talks to raise at about $26B — a reported negotiation, not a closed round.
The model catalogs moved on. Llama 4, Qwen 3.5 and DeepSeek V3.5 are no longer the headline open models; in October 2026 both Together and Fireworks lead with Kimi K3, Qwen3.8 Max, DeepSeek V4 Pro / V4.1 Flash, GLM-5.3 and MiniMax M3. The price table below is updated to today’s list rates.

TL;DR

Baseten: Bring-your-own-model with managed deployment + enterprise tooling. $13B valuation post Series F; OpenAI open-model partner since Sep 29, 2026.
Together AI: API access to a broad catalog of hosted open-weight LLMs. Strong per-token pricing. $8.3B valuation (July 2026).
Fireworks AI: Performance-optimized API for open-weight LLMs. Aggressive FP8/FP4 quantization. $17.5B valuation (July 2026).
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	90+ across 20+ clouds (September 2026)	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); reported ~$2B raise at $26B–$30B (Bloomberg, Oct 30, 2026)	$800M Series C, July 1, 2026 ($8.3B)	$1.505B Series D, July 15, 2026 ($17.5B)
Daily inference scale	1B+ calls (as of June 2026)	Not publicly disclosed	40T+ tokens/day (July 2026)
Serverless modes	Model APIs + dedicated	Serverless, batch, dedicated, GPU clusters	Standard, Priority, Fast; batch at 50%

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 90-plus-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 list prices (re-checked November 3, 2026, USD per 1M tokens, input / output):

Model	Together AI	Fireworks AI (Standard)
Kimi K3	$3.00 / $15.00	$3.00 / $15.00
Muse Spark Open	$1.25 / $4.25	$1.25 / $4.25
gpt-oss-120B	$0.15 / $0.60	$0.15 / $0.60
Qwen3.8 Max	$2.00 / $6.00	$2.00 / $6.00
GLM-5.3	$1.40 / $4.40	$1.40 / $4.40
DeepSeek V4.1 Flash	$0.30 / $1.20	$0.30 / $1.20
MiniMax M3	$0.30 / $1.20	$0.30 / $1.20
GLM-5.3 Flash	$0.15 / $0.50	$0.15 / $0.50
Llama 3.3 70B	No longer on the serverless list (Nov 2026; was $1.04 / $1.04)	—

As of November 3, 2026 the two list identical prices on every headline open model in this table; the differences are in serving modes (Fireworks Priority costs ~25% more; Fast variants ~50% more), cached-input rates and batch (Fireworks bills batch at 50% of serverless). The June 2026 version of this table quoted ranges for Llama 4 70B, Qwen 3.5 and DeepSeek V3.5; those are no longer the reference models.

Baseten per-GPU-hour pricing — Baseten’s public pricing page now shows plans (Basic: pay as you go; Pro and Enterprise: volume discounts by quote) rather than a per-GPU rate card, so the ranges below were last confirmed on June 27, 2026 and could not be re-verified today:

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

For a public reference point, Together’s GPU Clusters list on-demand H100 at $3.99, H200 at $5.99 and B200 at $8.19 per GPU-hour (unchanged when re-checked November 3, 2026); Together’s dedicated inference endpoints list H100 at $5.49 and B200 at $8.99 per GPU-hour.

Break-even math example. (Worked with June 2026 rates. In October 2026 Together listed Llama 3.3 70B at $1.04 / $1.04 per 1M tokens — higher than the $0.65 used here, which moves the break-even point toward dedicated GPUs sooner; by November 2026 that model had left Together’s serverless list.) Suppose you serve a 70B-class model with a ~10M tokens/day workload (5M input, 5M output) at per-token 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. (October 2026: the OpenAI partnership landed on September 29. November 2026: Bloomberg reports a ~$2B raise in two tranches at $26B and $30B, not yet announced by Baseten.)
Together vs Fireworks consolidation. The market may not support both at full scale long-term. Watch for fundraising signals. (October 2026: both raised in July — Together at $8.3B, Fireworks at $17.5B — so neither is under near-term funding pressure.)
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.

Sources

Last verified: November 3, 2026.

Baseten vs Together AI vs Fireworks AI: Inference November 2026

What changed as of November 2026

What changed as of October 2026

TL;DR

The three platforms head-to-head

When Baseten is the right choice

When Together AI is the right choice

When Fireworks AI is the right choice

Pricing dynamics (the real cost picture)

Decision framework

The multi-platform pattern (recommended)

What to watch over the next 6 months

Sources

Related