The 8 Best Together AI Alternatives in 2026 (Priced)

Together AI is one of the default places builders run open-weight models, but it is not the only one — and for specific workloads it is not always the best fit. If you are shopping for an alternative, the real decision is not "which logo" but which of three things you are optimizing for: per-token price, raw speed on custom silicon, or confidentiality. This page compares 8 credible alternatives on those axes, with dated prices.

DeAI is an independent publication covering open-weight AI models, private inference, and decentralized infrastructure — the tools for running AI you actually control. We test providers on price, privacy, and refusal behavior and publish the numbers, not the vibes. DeAI is powered by Morpheus (mor.org), a decentralized inference marketplace, and covers it on the same terms as every other provider.

Key takeaways

• Like-for-like serverless rivals: Fireworks AI and DeepInfra compete directly with Together AI on per-token, OpenAI-compatible serving of open models. None is categorically cheaper — all three reprice often, so compare the specific model you need on the day you buy.
• Speed plays: Groq and Cerebras run on custom LPU and wafer-scale silicon and market much higher token-per-second throughput than GPU clouds. Real latency still depends on model and load — benchmark before you commit.
• Confidentiality play: Chutes (Bittensor SN64) serves open models in hardware-attested TEEs and claims prompts are invisible to GPU operators — a policy/attestation claim, not an independently verified fact.
• Price floor (dated): the lowest verified open-weight list price on 2026-09-08 was openai/gpt-oss-120b at $0.037 input / $0.17 output per million tokens on the OpenRouter catalog; the closed frontier flagship gpt-5.5 listed at $5 / $30.
• Switching is cheap: most of these are OpenAI-compatible, so moving is usually a base-URL swap. Model slugs are not portable — map the exact name first.

What you are actually choosing between

"Together AI alternative" bundles three different kinds of provider. Grouping them correctly saves you a wasted afternoon:

1. General-purpose serverless inference clouds — Together AI, Fireworks AI, DeepInfra. Per-token billing, broad open-model catalogs, fine-tuning and dedicated-instance tiers. These are near-interchangeable at the API layer.
2. Fast-silicon inference — Groq, Cerebras. Custom chips (LPU, wafer-scale) tuned for extreme throughput on a narrower set of models. You trade catalog breadth for speed.
3. Decentralized / confidential networks — Chutes, and marketplaces like Morpheus. Compute is supplied by independent operators; the differentiator is verifiable execution (TEE attestation), not a single vendor's SLA.

Decide which group your workload belongs to before comparing prices, because a fast-silicon provider and a TEE network answer different problems.

The 8 Together AI alternatives, compared

Sorted by use case, not by a claimed ranking. Prices are list rates observed 2026-09-08 and move weekly — confirm on each provider's page before budgeting. Where a provider does not publish a single cross-catalog list, we say so rather than invent a number.

1. Fireworks AI

Fireworks AI is the closest like-for-like competitor to Together AI: founded by former Meta PyTorch engineers, it sells fast, OpenAI-compatible serving of open-weight models billed per million tokens, with dedicated "on-demand" deployment billed per GPU-second for scale. It markets a custom low-latency serving stack and offers LoRA fine-tuning. If you are leaving Together AI over a specific model's latency or rate limit rather than price, Fireworks is the first endpoint to A/B against it — the two are architecturally similar enough that an afternoon benchmark settles it. For a head-to-head on exactly this pair, see Together AI vs Fireworks AI.

2. DeepInfra

DeepInfra is the budget-conscious serverless option: per-token pricing on a broad catalog of open models, with an emphasis on undercutting the bigger inference clouds on popular checkpoints. It is OpenAI-compatible, so the switching cost is a base-URL change. The trade-off is that headline low prices apply to specific models and tiers — pull the exact model you run on both DeepInfra and Together and compare on your own input/output mix, since output tokens usually cost several times input.

3. Groq

Groq is a speed play, not a price play. It runs inference on its own LPU (Language Processing Unit) silicon rather than commodity GPUs and markets very high token-per-second throughput on supported models. Choose Groq when tail latency is the product requirement — real-time agents, voice loops, interactive tools — and the model you need is on its supported list. It is a centralized cloud, so prompts leave your infrastructure under its self-published retention terms.

4. Cerebras

Cerebras is the other fast-silicon option, built on wafer-scale chips. Its pricing page advertises a free trial tier with $5 in credits, a self-serve developer tier, and an enterprise tier with dedicated queues and support for custom weights — and it claims up to 30x faster inference than GPU systems (a vendor claim). Like Groq, it trades catalog breadth for throughput. Benchmark both on the model you actually serve.

5. Nebius

Nebius is a GPU cloud (NVIDIA H100/H200-class) that also operates a per-token "Token Factory" inference API. It suits teams who want to move between raw GPU rental and managed per-token serving under one roof, with EU and US regions that matter for data-residency requirements. Token rates live on the Token Factory side; the public prices page is GPU-hour oriented, so confirm the per-token rate for your model before treating it as a Together replacement.

6. Hyperbolic

Hyperbolic describes itself as an "open-access AI cloud": on-demand GPU compute plus an OpenAI-compatible inference API, drawing on a decentralized supply of hardware. It targets cost-sensitive builders who want GPU flexibility without a hyperscaler contract. As with any provider routing third-party compute, treat its performance and retention statements as claims to verify against your own benchmarks and its current terms.

7. Chutes (Bittensor SN64)

Chutes is the decentralized, confidentiality-first option on this list. It is a serverless inference network on Bittensor serving open models in hardware-attested TEEs, and it claims prompts are invisible to the GPU operators — an attestation/policy claim DeAI has not independently verified. Its public endpoint lists per-token prices in USD: as of 2026-09-08, Qwen/Qwen3-32B-TEE listed at $0.104 / $0.416 per million tokens and moonshotai/Kimi-K3-TEE at $3.00 / $15.00. Chutes is the pick when verifiable execution — not a vendor's say-so — is the requirement. For the broader category, see the decentralized AI inference networks that actually work.

8. OpenRouter (the aggregator route)

OpenRouter is not a host but a router across many of them, and it is the fastest way to comparison-shop. Its public catalog on 2026-09-08 listed the lowest verified open-weight row, openai/gpt-oss-120b at $0.037 / $0.17 per million tokens. The catch: the listed rate is what the router charged for that slug that day, and it can include routing markup or a cheaper backend, and retention varies by the underlying provider. For the full field see OpenRouter alternatives and DeAI's dated Price Index.

How to choose (the short version)

• You just want a drop-in open-model API: start with Fireworks AI or DeepInfra and A/B them against Together on your exact model.
• Latency is the product: benchmark Groq and Cerebras.
• Prompts must stay confidential: look at Chutes and the TEE-attested tier, and read the attestation model yourself.
• You are price-shopping hard: use OpenRouter's catalog as the dated benchmark, then confirm the first-party rate on whichever host you land.

Switching is usually a base-URL swap

Most providers here expose an OpenAI-compatible chat-completions endpoint, which makes price-shopping operationally cheap:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.deepinfra.com/v1/openai",  # swap per provider
    api_key=os.environ["PROVIDER_API_KEY"],
)

resp = client.chat.completions.create(
    model="meta-llama/Llama-4-Maverick",  # exact slug varies by provider
    messages=[{"role": "user", "content": "ping"}],
)

Keep the base URL and model name in config, not code, and a provider change becomes a deploy-time decision rather than a migration. Two cautions: model slugs are not portable across providers, and a cheaper model that fails your evals is not cheaper after retries. For the mechanics see how to migrate off the OpenAI API and what an OpenAI-compatible API actually is.

Current state (September 2026)

Open-weight inference is a buyer's market in 2026: per-token prices keep falling and new fast-silicon and confidential-compute entrants keep arriving. The one durable habit is to re-shop quarterly against a dated benchmark — DeAI's Price Index publishes a weekly snapshot — rather than locking to a provider on a price you saw once.

FAQ

What is the best alternative to Together AI?

There is no single best one. Fireworks AI and DeepInfra are the closest like-for-like serverless, per-token competitors. Groq and Cerebras win on raw speed via custom silicon. Chutes is the decentralized, TEE-attested option. The right pick depends on whether you optimize for price, latency, or confidentiality — see the comparison table.

Is Fireworks AI cheaper than Together AI?

Not categorically. Both bill per million tokens with rates tiered by model size, and both reprice often. As of 2026-09-08 neither published a single cross-catalog list DeAI could rank; compare the specific model you need on each provider's pricing page on the day you buy.

Which Together AI alternative is fastest?

Groq and Cerebras market the highest token-per-second throughput because they run on custom LPU and wafer-scale silicon rather than commodity GPUs. Real latency still depends on model, quantization, and live load — benchmark both endpoints yourself before committing.

Which alternative keeps my prompts private?

Chutes is the only option here built around confidential computing: it serves open models in hardware-attested TEEs and claims prompts are invisible to the GPU operators — a policy/attestation claim, not an independently verified fact. The centralized providers are US/EU clouds where prompts leave your infrastructure; read each one's retention terms.

Can I switch from Together AI without rewriting code?

Usually yes. Together AI and most of these alternatives expose an OpenAI-compatible chat-completions endpoint, so switching is typically a base-URL and API-key change. Model slugs are not portable across providers, so map the exact model name before you cut over.

Is there a free or cheapest way to test open models?

The lowest verified list price on 2026-09-08 was openai/gpt-oss-120b at $0.037 input / $0.17 output per million tokens on the OpenRouter aggregator. Most providers also give small free credits — Fireworks starts at $1 and Cerebras at $5 — which is enough to run an afternoon A/B benchmark.