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Same-model provider benchmark

Ambient vs Weights & Biases: LLM provider comparison

Compare Ambient and Weights & Biases on 3 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.

Ambient

5 indexed models

Headquarters
N/a
Server regions
N/a
Model types
5 text

Weights & Biases

19 indexed models

Headquarters
United States
Server regions
United States
Model types
19 text

At a glance

Metric winners

There is no overall score. Each winner answers one specific question using only directly comparable data.

Fastest on shared models

1.61× typical advantage

3 exact models with complete recent speed data

Lowest token cost

$1.5133 / 1M

3 exact models using a 1K-input/500-output mix

Most models available

19 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricAmbient(0)Weights & Biases(0)
SpeedAmbient9.10 sWeights & Biases5.65 s
TTFTAmbient0.54 sWeights & Biases0.40 s
TPSAmbient58.0 tok/sWeights & Biases94.0 tok/s
PriceAmbient$1.6333 / 1MWeights & Biases$1.5133 / 1M
UptimeAmbient99.78%Weights & Biases99.88%
Green value Better comparable resultRed value Worse comparable result

Visual comparison

Price and performance charts

AmbientWeights & Biases
500-token response by shared model

Estimated seconds using recent median response-start and output-speed data. Lower is better.

Weights & Biases has the lower typical same-model response ratio across 3 measured models.

Blended token price by shared model

USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.

Weights & Biases has the lower typical price ratio across 3 priced shared models.

Shared text models

3 exact models · newest first

ModelAmbient(0)Weights & Biases(0)
GLM 5.2Winner · Weights & Biases
Ambient
Price— Best comparable value
$2.1667 / 1M
Speed— Worst comparable value
7.58 s
TTFT— Worst comparable value
0.54 s
TPS— Worst comparable value
71.0 tok/s
Uptime— Worst comparable value
99.78%
Context
101.4K
Route
ambient/fp8
Weights & Biases
Price— Worst comparable value
$2.3933 / 1M
Speed— Best comparable value
5.25 s
TTFT— Best comparable value
0.40 s
TPS— Best comparable value
103.0 tok/s
Uptime— Best comparable value
99.95%
Context
262.1K
Route
wandb/fp4
Kimi K2.7 CodeWinner · Weights & Biases
Ambient
Price— Worst comparable value
$2.1333 / 1M
Speed— Worst comparable value
9.10 s
TTFT— Worst comparable value
0.48 s
TPS— Worst comparable value
58.0 tok/s
Uptime— Best comparable value
100.00%
Context
262.1K
Route
ambient/int4
Weights & Biases
Price— Best comparable value
$1.96 / 1M
Speed— Best comparable value
5.65 s
TTFT— Best comparable value
0.33 s
TPS— Best comparable value
94.0 tok/s
Uptime— Worst comparable value
99.63%
Context
262.1K
Route
wandb/int4
DeepSeek V4 FlashWinner · Weights & Biases
Ambient
Price— Worst comparable value
$0.6 / 1M
Speed— Worst comparable value
42.52 s
TTFT— Worst comparable value
0.85 s
TPS— Worst comparable value
12.0 tok/s
Uptime— Worst comparable value
98.98%
Context
1M
Route
ambient/fp4
Weights & Biases
Price— Best comparable value
$0.1867 / 1M
Speed— Best comparable value
13.88 s
TTFT— Best comparable value
0.72 s
TPS— Best comparable value
38.0 tok/s
Uptime— Best comparable value
99.88%
Context
1M
Route
wandb/fp8

A per-model winner combines blended price and estimated 500-token response time with equal proportional weight. Ties and rows missing either measurement receive no badge. Comparison data calculated . Values use one deterministic route per provider and model; missing measurements remain visible as N/a.

Ambient vs Weights & Biases analysis

How Ambient and Weights & Biases compare for AI inference

Ambient and Weights & Biases share 3 indexed text models, including GLM 5.2, Kimi K2.7 Code, DeepSeek V4 Flash. Weights & Biases has the stronger typical response-time result on the directly measured set.

Same-model speed evidence

3 shared models currently have complete response-start and output-speed measurements on both providers. The speed comparison uses per-model ratios before taking the median, so naturally faster model catalogs do not improve the result.

Token pricing on one workload

3 shared models have complete input and output prices on both providers. Prices use the same 1,000-input/500-output-token mix and are normalized to one million tokens for readability.

Catalog and deployment differences

Ambient has 5 indexed models and lists no published server regions; Weights & Biases has 19 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.