Wafer vs. Fireworks AI

Fireworks AI gives you a broad open-model platform with deployment shapes, autoscaling, and fine-tuning you configure yourself, 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 offers a pay-per-token serverless API, and reports 10T+ tokens of continual inference.

Fireworks AI is an inference and training platform with Serverless (Standard, Priority, and Fast modes), On-Demand deployments, contracted Reserved Throughput, a large model library, and managed fine-tuning. Both expose OpenAI-compatible APIs and both serve GLM-5.3, so list prices compare like for like. They diverge on who does the tuning. Fireworks documents deployment shapes (Fast, Throughput, and Minimal), speculative decoding, quantization, and autoscaling as options you set. Wafer's agents run that search against your traffic, aimed at whichever metric you name: TTFT, TPS, or E2E.

Methodology. Wafer figures come from Wafer's own published pages, linked inline, and were re-verified on October 29, 2026. Fireworks figures come from Fireworks's own published pricing and documentation pages, are stated as plain text, and were re-verified on October 29, 2026. Fireworks's pricing page did not list per-token serverless rates, so GLM-5.3 rates come from its serverless pricing docs and models page. TTFT, TPS, and E2E claims are self-reported by each vendor or by customers; test both on your own prompts, output lengths, and concurrency.

TL;DR

• On GLM-5.3 serverless list prices, Wafer charges $0.22 input and $3.39 output per 1M tokens against Fireworks's $1.40 and $4.40, about 65% less per typical request (derived below).
• On performance, Brilliant reports 300+ output tok/s and about 250 ms TTFT on Wafer, against a previously reported 85 to 100 tok/s on Fireworks GLM-5.2 Fast; Fireworks is stronger on model breadth, managed fine-tuning, and 50%-off batch.
• Choose Wafer when a TTFT, TPS, or E2E target is the requirement and you want someone else running the tuning loop; choose Fireworks when you want to own the configuration and need its broader platform.

Quick comparison

Where the vendors differ: who tunes for which metric

Fireworks's shapes are named for the trade-off they make: Fast (low latency), Throughput (cost per token at scale), or Minimal (lowest cost). You pick one, set the accelerator, and benchmark. Wafer starts from the metric and searches configurations until the endpoint meets it.

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.
• Fireworks: 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.

Output-heavy runs narrow the gap: at 1,000 input and 5,000 output tokens, Wafer is $0.01717 and Fireworks $0.0234, about 27% lower. Fireworks's scale-to-zero stops idle spend on bursty traffic, at the cost of cold starts.

Why teams choose Wafer over Fireworks AI

TPS that changes the E2E budget

Brilliant's AI tutor has to respond in under 2 seconds. On a dedicated GLM-5.2 endpoint, Wafer tuned prompt-prefix caching, speculative decoding, and a low-concurrency configuration, and Brilliant reports about 250 ms TTFT and 300+ output tok/s. The team had previously reported 85 to 100 tok/s on Fireworks GLM-5.2 Fast. For a 300-token reply (derived, steady decode, network excluded):

The faster responses let Brilliant remove its prefetching layer and cut inference cost 50%. The Fireworks figure has no stated date and the configurations differ, so treat it as directional. Fireworks's docs now say its Fast serverless mode aims for 100+ tok/s; at 100 tok/s, decode for the same reply is still 3.0 s.

A TTFT target held under load

Neon Health cut p50 client-observed TTFT from 800 ms to 550 ms on GLM-5.1 at about 25% higher peak load, against a 600 ms contract target, with a signed BAA and US-only data residency. Fireworks documents the controls to chase that number yourself; Wafer measured Neon's traffic and tuned to it.

Same model, lower serverless price

Both list GLM-5.3 with a 1,048,576-token context. 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 and cache hit rate before assuming the full gap.

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

Best use cases for Wafer

Voice agents and real-time assistants

Short turns are TTFT-bound. Wafer writes a p50 TTFT target into the engagement and tunes to it, as in Neon's healthcare voice deployment. YC's voice simulator measured 379 ms average LLM latency per turn on Wafer across 4,168 turns.

How Fireworks compares: its Fast shape and Fast serverless mode (100+ tok/s target) aim at interactive latency, but you select and validate it yourself and no latency target is stated on the pages reviewed.

Long outputs and throughput per node

When output length makes TPS the bottleneck, or cost depends on tokens per node, Wafer tunes for aggregate throughput at a TTFT ceiling. Its Qwen3.6-35B-A3B benchmark reports about 15,000 tok/s from one 8x MI355X node at TTFT p95 under 4 s, with 100% success through 50 RPS.

How Fireworks compares: its Throughput shape targets the same goal, configured and benchmarked by you.

Coding and agentic products

Agents chain calls, so E2E per step compounds. Prefix caching and speculative decoding tuned to your prompt shapes cut each step. Wafer's Responses and Messages endpoints accept Codex and Claude Code style clients.

How Fireworks compares: it also supports Anthropic-compatible clients, markets a Nexus router for coding spend, and lists Cursor and Sourcegraph as customers.

Best use cases for Fireworks AI

Broad model coverage and fine-tuning

Fireworks documents a large model library across text, vision, audio, image, and embeddings, plus managed supervised fine-tuning. If one vendor needs to cover embeddings, vision, and a tuned model, that breadth is Fireworks's strength.

Batch and bursty workloads

Batch jobs run at 50% off serverless prices, with 12, 24, 48, or 72 hour windows and files up to 80 GiB. On-demand deployments scale to zero after an hour idle. For offline jobs where TTFT and E2E do not matter, that cost structure beats a performance-tuned endpoint.

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. Dedicated Inference carries a 99.9% monthly availability SLA with credits of 10%, 15%, or 30%, capped at 40%, 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. Each candidate must keep correctness before it ships. On GLM-5.2, Wafer reports 2,626 tok/s per node at 2.4 RPS on a 20K-in, 1K-out workload, at 0.81 s p50 TTFT.

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: 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 Fireworks AI to Wafer

1. Create a Wafer key in the app and add credits.
2. Change base_url and model. Fireworks uses accounts/fireworks/models/glm-5p3; Wafer uses GLM-5.3.
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.fireworks.ai/inference/v1", api_key=FW_KEY)
r = client.chat.completions.create(model="accounts/fireworks/models/glm-5p3", messages=msgs)

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:

• Streaming usage: Fireworks returns usage in the final streamed chunk; confirm how your code reads usage on Wafer, since your TPS measurement depends on it.
• Fireworks-only features: managed fine-tuning, the batch API, and its broader model library have no Wafer equivalent on the pages reviewed.
• Partial migration: often best. Keep Fireworks for batch, fine-tuned models, and embeddings; move the TTFT-, TPS-, or E2E-critical path 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 Fireworks AI? Brilliant reports 300+ tok/s and about 250 ms TTFT on Wafer against a previously reported 85 to 100 tok/s on Fireworks GLM-5.2 Fast. For a 300-token reply, that is about 1.25 s E2E on Wafer against over 3 s of decode alone on Fireworks (derived). The Fireworks figure is undated and the setups differ. Benchmark both.

Is Wafer cheaper than Fireworks AI? On GLM-5.3 serverless 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.

Does Wafer work with Anthropic and Responses API 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.

Does Wafer's SLA cover latency? The public SLA covers 99.9% monthly availability on Dedicated Inference and lists no latency target. Neon reports a TTFT target in its own contract. Fireworks says serverless has no SLA for latency or availability; Reserved Throughput carries a throughput SLA.

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

Related Articles

• Oct 2026: Wafer vs. Together AI
• Oct 2026: Wafer vs. Modal
• Oct 2026: Wafer vs. Cerebras