Together AI Alternative: When You Need Gateway Simplicity, Not Infra

Most teams shopping for a Together AI alternative do not need a different GPU cluster; they need fewer moving parts. Together's own docs describe serverless per-token inference, plus separate dedicated, fine-tuning, and provisioned-throughput products. TokenLab documents one balance, one API key, and four request formats. We read both sets of docs on 2026-10-03 and rebuilt this article around what they actually state. Several numbers in the earlier version were wrong, so we replaced them.

Key Takeaways

Together AI alternative: what the docs say side by side

We compared only what both vendors publish. Where a cell is empty of numbers, the docs we read gave none.

Item	Together AI	TokenLab	Source and observed date
Base URL	https://api.together.ai/v1	https://api.tokenlab.sh/v1 (Anthropic SDK and Gemini use https://api.tokenlab.sh)	Together OpenAI compatibility, TokenLab migration guides; 2026-10-03
API compatibility	OpenAI SDK calls for chat, completions, embeddings, images, speech, transcription; Assistants and OpenAI-shape batches not supported	Chat Completions, Responses, Anthropic Messages, Gemini generateContent; each model lists its accepted_request_formats	Same two pages plus API formats; 2026-10-03
Rate limits	No numeric limit on the page; 429 or 503 under high demand; provisioned throughput for guarantees	Per API key: User 1,000, Partner 10,000, VIP 10,000 requests per minute	Together rate limits, TokenLab rate limits; 2026-10-03
glm-5.2 per 1M tokens	Input $1.40, output $4.40 (zai-org/GLM-5.2)	Input $1.4, output $4.4	Together models, TokenLab model API; 2026-10-03
qwen3.7-plus per 1M tokens	Input $0.32, output $1.28 (Qwen/Qwen3.7-Plus)	Input $0.4, output $1.6 up to 256,000 input tokens; $1.2 and $4.8 up to 1,000,000	Together models, TokenLab model API; 2026-10-03
deepseek-v4-pro per 1M tokens	Input $1.32, output $3.96 (deepseek-ai/DeepSeek-V4-Pro-0813)	Off-peak $0.66 and $1.98; peak $1.32 and $3.96	Together models, TokenLab model API; 2026-10-03

Three caveats apply to the price rows:

• TokenLab's peak window is 09:00-12:00 and 14:00-18:00 Beijing time. The Together page lists one DeepSeek price and a specific "0813" build, so the two rows may not describe the identical model.
• Cache-read prices are also equal on glm-5.2: $0.26 per 1M tokens on both sides.
• glm-5.2, deepseek-v4-pro, and qwen3.7-plus show verified: false, official: true on TokenLab. That matters for the delivery section below.

Here is a worked estimate for 10M input and 2M output tokens of qwen3.7-plus, with every prompt under 256,000 tokens:

• TokenLab: 10 × $0.4 + 2 × $1.6 = $7.20.
• Together: 10 × $0.32 + 2 × $1.28 = $5.76.

Both figures are estimates from list prices. They exclude caching and taxes. TokenLab's docs say the lowest price per token is not always the lowest cost per completed task, so compare final cost in Usage on your own prompts.

The earlier version of this article also listed TokenLab rates for gpt-5.5, claude-sonnet-5, and deepseek-v4-pro that do not match the live model API. On 2026-10-03 the API showed these prices:

Model	Input per 1M	Output per 1M	Max input tokens	Source
gpt-5.5	$1.5	$9	1,000,000	model API
claude-sonnet-5	$0.9	$4.5	1,000,000	model API

The docs say not to hard-code a copied price table, and we agree. We dropped the rows for models whose prices we could not confirm.

Delivery options and retries on TokenLab

TokenLab documents three delivery options, picked per request with the X-TokenLab-Delivery-Policy header (auto, verified, or official). A request header overrides the API key setting, which overrides the Workspace default (API reference, observed 2026-10-03).

• TokenLab Verified is delivery verified by TokenLab, at TokenLab prices.
• Official is based on the model maker's public price.
• Auto uses Verified when available, then Official if needed. You pay for the option that completes the request.

Each completed request is charged once, for the option that produced the result (billing). An invalid header returns 400. If the requested option is unavailable, you get 503 delivery_tier_unavailable with a request ID. When the operation has no supply, retryable is false, and you should not repeat the request unchanged.

The docs describe retries by status code (error handling, rate limits):

Status	Documented action
400, 401, 402, 403, 404, 413	Do not repeat; change the request, key, balance, permissions, or input
429	Retry after the Retry-After delay; use exponential backoff with jitter if it is missing
500-504	Retry only when retryable is true; respect retry_after and cap attempts

Two more details helped us. The quickstart clients set max_retries=0, so retry policy stays in your code. Async create requests (video, music, 3D) should not be retried before you check whether a task already exists. We found no documented gateway latency figure or automatic cross-model failover, so we removed the old 15-40ms claim and the failover promise.

Migration snippet: one completion on each API

We used glm-5.2 because both vendors list it. Together's model string follows <provider>/<model_name>; TokenLab IDs carry no provider prefix.

import os
from openai import OpenAI

together = OpenAI(
    api_key=os.environ["TOGETHER_API_KEY"],
    base_url="https://api.together.ai/v1",
)
tokenlab = OpenAI(
    api_key=os.environ["TOKENLAB_API_KEY"],
    base_url="https://api.tokenlab.sh/v1",
    timeout=30.0,
    max_retries=0,
)

messages = [{"role": "user", "content": "Reply only with OK."}]

a = together.chat.completions.create(model="zai-org/GLM-5.2", messages=messages)
b = tokenlab.chat.completions.create(model="glm-5.2", messages=messages)

print(a.choices[0].message.content)
print(b.choices[0].message.content)

Sources: Together OpenAI compatibility and TokenLab quickstart, both observed 2026-10-03. Confirm the model ID with GET /v1/models before you send traffic. If you come from OpenRouter, the migration guide says to swap the base URL and drop the provider prefix from model IDs.

Who should stay, and who should pick a Together AI alternative

Stay on Together AI if you need what its docs list and TokenLab's docs do not:

• The Together-native fine-tuning API and Batch API.
• A dedicated model inference catalog.
• Provisioned throughput, which reserves capacity under a defined SLA.

Together's OpenAI compatibility page marks fine_tuning.jobs.* and batches.* as not supported in OpenAI shape, so those workloads use Together's own APIs. We found nothing in the evidence about hosting custom weights on TokenLab. Check the Models page at tokenlab.sh/models or ask support@tokenlab.sh before you plan around it.

TokenLab fits better when your needs match these documented differences:

• You want one balance across models, with no subscription or minimum spend.
• You need Anthropic Messages fields (thinking, Claude tool use) or Gemini-native contents on the same key.
• You want to choose Verified, Official, or Auto delivery per request.
• You want the OpenAI Responses API on models that list openai_responses.

Imagine a team already calling gpt-5.5 and claude-sonnet-5. Both models list openai_chat_completions as an accepted format, so one OpenAI client covers both. A third vendor's open-weight model, such as glm-5.2, rides the same client and the same balance. A hybrid also works: fine-tuned traffic stays on Together, and everything else goes through one TokenLab key. Our compare page has more on routing and coverage.

Beyond text: images, video, and code

TokenLab documents /v1/images/generations, /v1/videos/generations, /v1/music/generations, and /v1/3d/generations. Async jobs return a task_id and poll_url, and billing reconciles through billing_transaction_id. Together lists its own image models and says video is Together-native. We did not compare media prices because the evidence has no matching TokenLab media rates. For model picks, see our guides to the best AI image models API 2026, the best AI video models API 2026, and the best AI models for coding 2026. Catalog availability, for example kimi-k2.7-code, is not a benchmark result. Test on your own tasks.

FAQ

Can I keep my OpenAI SDK code when I move from Together AI to TokenLab?

Mostly, yes. Change base_url to https://api.tokenlab.sh/v1, swap the key, and pick a model ID from GET /v1/models. TokenLab's guide says existing retry, timeout, and streaming code can usually stay. Fields unique to another API format are not guaranteed on Chat Completions.

Does TokenLab fail over to another provider automatically?

The docs we read describe delivery options, not model-to-model failover. Auto tries TokenLab Verified, then Official, and you pay for the option that completes the request. If neither has supply, you get 503 delivery_tier_unavailable, and retryable tells you whether to retry.

Is TokenLab cheaper than Together AI for the same model?

Not always. On 2026-10-03 glm-5.2 was $1.4 input and $4.4 output on both. qwen3.7-plus was lower on Together ($0.32 and $1.28 versus $0.4 and $1.6 on TokenLab). Compare final cost on your own workload.

Do I have to move all my traffic at once?

No. The two APIs use separate base URLs and keys, so you can send one workload through TokenLab and leave the rest on Together. Move a single model first and check its cost in Usage.

Compare your current Together AI workload against TokenLab on the compare page, or read our OpenRouter comparison and the model list.