Wafer vs. Together AI

Together AI is a broad AI cloud with serverless inference, GPU clusters, fine-tuning, and batch, while Wafer builds a dedicated endpoint around your TTFT, throughput, and end-to-end latency targets and keeps re-tuning it after launch.

Introduction and methodology note

Wafer is an inference platform for performance-sensitive production workloads: products where time to first token (TTFT), output tokens per second (TPS), or end-to-end (E2E) response time decides the user experience or the unit economics. It profiles your real request mix, builds a dedicated endpoint around your model and performance targets, and keeps testing batching, decoding, quantization, engine, kernel, and hardware configurations as traffic changes. It also runs a pay-per-token serverless API, and reports 10T+ tokens of continual inference.

Together AI positions itself as "The AI Native Cloud": serverless and batch inference, provisioned throughput, dedicated model and container inference, GPU clusters, fine-tuning, and sandboxes. Both list GLM-5.3 through OpenAI-compatible APIs, so list prices compare on the same model name. They diverge on scope and on who tunes. Together sells a wide platform and says its workload-specific optimization cuts cost. Wafer is narrower: dedicated endpoints tuned to whichever metric your product depends on, TTFT, TPS, or E2E.

Methodology. Wafer figures come from Wafer's own published pages, linked inline, and were re-verified on October 29, 2026. Together AI figures come from Together's own pricing, docs, and homepage, are stated as plain text, and were re-verified on October 29, 2026. Together's pricing page shows batch prices that match its serverless list prices for overlapping models while its batch page advertises up to 50% savings, so batch prices are not quoted. TTFT, TPS, and E2E claims are self-reported by each vendor or by customers who measured them, usually on different models. Test both on your own prompts, output lengths, and concurrency.

TL;DR

• On GLM-5.3 list prices, Wafer charges $0.22 input and $3.39 output per 1M tokens against Together's $1.40 and $4.40, about 65% less per typical request (derived below).
• Wafer publishes TTFT, TPS, and throughput-under-load figures (for example, 239 to 337 ms p50 TTFT at 1 to 8 RPS, about 15,000 tok/s per node); Together publishes relative claims ("up to 2x faster serverless inference") but no TTFT or TPS figures on the pages reviewed.
• Choose Wafer when a TTFT, TPS, or E2E target is the requirement and you want the endpoint tuned for you; choose Together when you need a full AI cloud with clusters, fine-tuning, and batch.

Quick comparison

Where the vendors differ: breadth vs a tuned metric

Together is ahead on breadth and on reserved capacity with an SLA. Wafer is lower on GLM-5.3 list price and publishes the absolute numbers you would test against.

Per-request cost (derived, GLM-5.3, 10,000 input and 1,500 output tokens, no caching, list prices):

• Wafer: 10,000 x $0.22 / 1M = $0.0022, plus 1,500 x $3.39 / 1M = $0.005085, total $0.007285.
• Together: 10,000 x $1.40 / 1M = $0.014, plus 1,500 x $4.40 / 1M = $0.0066, total $0.0206.
• Difference: (0.0206 - 0.007285) / 0.0206 = 64.6% lower on Wafer.

At 1,000 input and 5,000 output tokens, Wafer is $0.01717 and Together is $0.0234, about 27% lower. Reasoning tokens bill as output on both.

Why teams choose Wafer over Together AI

Absolute numbers to test against

Together's speed claims are relative. Wafer's Qwen3.6-35B-A3B benchmark publishes the curve on one 8x MI355X node:

Success stayed at 100% through 50 RPS. These are Wafer's own measurements on one model, so use them as a baseline for your own test, not as a guarantee.

TTFT and E2E tuned for you, continually

Together documents deployment profiles that bundle hardware, quantization, and speculative decoding. Wafer's loop runs that search for you. Neon Health saw p50 client-observed TTFT fall from 800 ms to 550 ms on GLM-5.1 at about 25% higher peak load, against a 600 ms target, with a signed BAA and US-only data residency. Brilliant needs responses in under 2 seconds and reports about 250 ms TTFT and 300+ tok/s, which puts a 300-token reply at about 0.25 + 300 / 300 = 1.25 s E2E (derived). Both are customer-reported.

Same model, lower list price

On GLM-5.3, Wafer's input is $0.22 against $1.40, cached input $0.1775 against $0.26, and output $3.39 against $4.40. Check your real input-to-output ratio with the math above.

If a TTFT, TPS, or E2E number is your acceptance test, start a dedicated endpoint benchmark with Wafer and run it against your current Together AI results.

Best use cases for Wafer

Voice agents and real-time assistants

Short turns are TTFT-bound. Wafer tunes p50 TTFT for your traffic and holds it as call volume rises. In YC's voice simulator, GLM-5.2 on Wafer averaged 379 ms LLM latency per turn across 4,168 turns.

How Together AI compares: it offers dedicated model inference with your choice of hardware and speculative decoding, but you configure it and no latency target is stated on the pages reviewed.

High-throughput serving where cost tracks tokens per node

Wafer tunes aggregate TPS at your TTFT ceiling. On GLM-5.2, it reports 2,626 tok/s per node at 2.4 RPS on a 20K-in, 1K-out workload with a 60% cache hit rate, at 0.81 s p50 and 2.22 s p95 TTFT.

How Together AI compares: Provisioned Throughput reserves capacity with a 99% uptime SLA, which suits teams that want a throughput commitment without tuning.

Coding and agentic products

E2E compounds across agent calls. Wafer's Responses and Messages endpoints accept Codex and Claude Code style clients, and prefix caching tuned to your prompts cuts every step.

How Together AI compares: it lists Cursor and Cognition among customers and publishes a coding-agents build guide.

Best use cases for Together AI

One vendor for inference, fine-tuning, and GPU clusters

Together sells serverless and batch inference, fine-tuning (for example Qwen3.8 27B at $1.05 per 1M tokens for SFT), and H100 clusters at $3.99 per GPU-hour on demand ($1.99 preemptible, $3.19 reserved for 181+ days). If you train and serve in one place, Wafer does not compete on breadth.

Batch and committed capacity

Together advertises batch inference up to 30 billion tokens per model and Provisioned Throughput with reserved throughput and a 99% uptime SLA. For offline jobs where TTFT and E2E do not matter, batch is the cheaper path.

Platform overview of Wafer

Dedicated endpoints with performance targets

You supply model, traffic shape, and SLO (a TTFT, TPS, or E2E target); Wafer builds the endpoint and keeps optimizing it after launch. Dedicated Inference carries a 99.9% monthly availability SLA with credits of 10%, 15%, or 30%, capped at 40% of the monthly amount, per the SLA page. The public SLA lists no latency target.

Continual inference loop

Wafer profiles whether latency or throughput is limited by scheduling, decoding, kernels, memory pressure, or hardware fit, tests candidates, deploys the measured winner, and repeats. In one DigitalOcean engagement, the loop raised DeepSeek V3.2 aggregate throughput at concurrency 64 from 548 to 2,165 tok/s (internal testing).

Serverless OpenAI-compatible API

The serverless docs list POST /v1/chat/completions, /v1/responses, /v1/messages, and GET /v1/models. Busy models return 429 server_overloaded with Retry-After, and errors can arrive mid-stream, per the errors page.

from openai import OpenAI
client = OpenAI(base_url="https://pass.wafer.ai/v1", api_key="YOUR_WAFER_API_KEY")
r = client.chat.completions.create(model="GLM-5.3",
    messages=[{"role": "user", "content": "Hello!"}], max_tokens=128)

Models and capabilities

The models page documents GLM-5.3: 1,048,576-token context, tools, JSON schema output, n greater than 1, no vision, and reasoning efforts none, low, high, max. Reasoning tokens bill as output and add to TTFT and E2E, so set the effort per route.

Zero data retention

Send Wafer-ZDR: required. Models without support return HTTP 422 model_zdr_not_supported. See the ZDR docs.

curl https://pass.wafer.ai/v1/chat/completions \
  -H "Authorization: Bearer $WAFER_API_KEY" -H "Wafer-ZDR: required" \
  -d '{"model":"GLM-5.3","messages":[{"role":"user","content":"Hi"}]}'

Measuring TTFT, TPS, and E2E on your own prompts

Run the same script against both base URLs. TTFT is time to the first content token; TPS is output tokens over decode time.

import time
t0 = time.perf_counter(); first = None; n = 0
for c in client.chat.completions.create(model="GLM-5.3", messages=msgs,
        stream=True, stream_options={"include_usage": True}):
    if c.choices and c.choices[0].delta.content and first is None: first = time.perf_counter()
    if c.usage: n = c.usage.completion_tokens
end = time.perf_counter()
print(f"TTFT {first-t0:.3f}s  TPS {n/(end-first):.0f}  E2E {end-t0:.3f}s")

How to choose

Switching from Together AI to Wafer

1. Create a Wafer key in the app and add credits.
2. Change base_url and model. Together uses org/Model IDs; Wafer uses names such as GLM-5.3. Call GET /v1/models for the list.
3. Re-run quality and performance tests with the measurement script above, at your real output lengths and concurrency.
4. Add 429 retry handling for server_overloaded.

Before:

client = OpenAI(base_url="https://api.together.ai/v1", api_key=TOGETHER_KEY)
r = client.chat.completions.create(model="moonshotai/Kimi-K3", messages=msgs)

The before snippet calls Kimi K3 on Together; the after snippet switches to GLM-5.3, so re-test quality as well as speed.

After:

client = OpenAI(base_url="https://pass.wafer.ai/v1", api_key=WAFER_KEY)
r = client.chat.completions.create(model="GLM-5.3", messages=msgs)

What to watch:

• Together-only products: fine-tuning, GPU clusters, batch inference, and sandboxes have no Wafer equivalent on the pages reviewed.
• Custom models: Together lets you deploy fine-tuned models and LoRA adapters; confirm the equivalent path with Wafer before migrating them.
• Model availability: Together also lists Kimi K3; check Wafer's GET /v1/models before moving those routes.
• Partial migration: often best. Keep Together for training, batch, and clusters; move the TTFT-, TPS-, or E2E-critical endpoint to Wafer.

Neon's case states a dedicated endpoint can reach production in under two weeks. That is a stated capability, not a guarantee.

FAQ

Is Wafer faster than Together AI? No same-model benchmark is published by either vendor. Wafer publishes 337 ms single-stream TTFT and 430 tok/s on Qwen3.6-35B-A3B, and its customers report 550 ms p50 TTFT (Neon) and 300+ tok/s (Brilliant). Together's pages give relative claims, not TTFT or TPS figures. Benchmark your own prompts.

Is Wafer cheaper than Together AI? On GLM-5.3 list prices, yes: $0.007285 against $0.0206 for a 10,000-in, 1,500-out request, about 65% lower. At 1,000 in and 5,000 out the gap is about 27%.

What throughput can a Wafer endpoint sustain? Wafer reports about 15,000 tok/s of aggregate output from one 8x MI355X node on Qwen3.6-35B-A3B at TTFT p95 under 4 s, and 2,626 tok/s per node on GLM-5.2. Both are Wafer's own measurements.

Which has the better SLA? Wafer's Dedicated SLA states 99.9% monthly availability with credits of 10%, 15%, or 30%. Together states a 99% uptime SLA on Provisioned Throughput and none for dedicated model inference on the pages reviewed. They cover different products, and neither public SLA lists a latency target.

Does Wafer work with Anthropic and Responses clients? Yes. Wafer serves /v1/messages for Anthropic SDK clients including Claude Code and /v1/responses for Codex-style clients. The Responses endpoint is stateless.

Which hardware does Wafer use? Per its AI info page, NVIDIA and AMD GPUs. Its published benchmarks run on AMD MI355X. Validate availability per model.

Want TTFT, TPS, and E2E numbers on your own traffic? Get started on Wafer and benchmark a dedicated endpoint against your current Together AI results.

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