benchmarks/inference/best inference provider for ai transformation
enterprise AI · measured provider trade-offs · measured Sep 3, 2026

Best LLM inference provider for enterprise AI programs

What the benchmark found. Baseten led end-to-end p99 latency at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The scorecard keeps latency, tokens per second, accuracy, and failures separate rather than blending them into one synthetic score. The benchmark informs short operational workloads; enterprise selection still requires workload replay and non-performance diligence. Measured across 10 LLM inference providers serving GLM 5.3 Flash, 600 requests each, Sep 3, 2026.

Providers measured. Baseten, DeepInfra, Fireworks AI, Modal, Nebius, Novita AI, Parasail, Telnyx, Together AI, and Z.AI, each serving GLM 5.3 Flash behind an OpenAI-compatible chat-completions endpoint.

What this page compares. An enterprise AI program rarely has one inference requirement. Early production workloads often mix document lookup, operational classification, and structured extraction, so this page uses the balanced pooled benchmark to frame provider selection across latency, exact output yield, streaming behavior, and service failures.

How to read the result. Treat the table as measured technical diligence, not a universal procurement score. Define separate acceptance thresholds for task success, failure rate, complete-response latency, and visible streaming; then validate security, data residency, support, capacity, governance, and commercial terms outside this benchmark.

Workload and configuration. Every provider received the same 600 deliberately easy structured tasks: 200 meeting-note lookups, 200 ticket-triage classifications, and 200 contract-term extractions. GLM 5.3 Flash ran with streaming enabled, reasoning low, temperature 0, top-p 1, a 256-token output ceiling, one attempt, a 20-second timeout, and per-provider concurrency one.

APIs used. The runner called each provider's OpenAI-compatible chat-completions surface with the model identifiers below; the links open the provider documentation used to verify each adapter.

[01] measured comparison

How do LLM inference providers compare across common enterprise workloads?

This focused table adds deltas from both the latency and accuracy leaders while retaining time to first token, tokens per second p95, and failure rate. It differs from the full benchmark and deliberately avoids a synthetic enterprise score.

benchmarks/inference/best-inference-provider-for-ai-transformationreviewed run
LLM inference providers compared on end-to-end latency, time to first token, tokens per second, task success and failures
Provider / APIEnd-to-end p99Δ vs fastestTTFT p95Tokens/s p95Task successΔ vs most accurateFailure rate
Basetenzai-org/GLM-5.3-Flash ↗3.23 s0 ms1.16 s61.6 tok/s95.8%-3.7 pp0.0%
Modalzai-org/GLM-5.3-Flash ↗3.28 s+50 ms1.09 s84.7 tok/s99.3%-0.2 pp0.0%
Telnyxzai-org/GLM-5.3-Flash ↗3.48 s+250 ms1.34 s67.5 tok/s99.5%0.0 pp0.0%
Novita AIzai-org/glm-5.3-flash ↗7.25 s+4020 ms3.41 s29.6 tok/s94.2%-5.3 pp4.0%
Z.AIglm-5.3-flash ↗8.59 s+5362 ms3.14 s27.8 tok/s97.5%-2.0 pp0.2%
Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗10.5 s+7259 ms5.83 s27.9 tok/s99.5%0.0 pp0.0%
Parasailzai-org/GLM-5.3-Flash ↗11.5 s+8283 ms5.54 s22.8 tok/s98.3%-1.2 pp1.3%
Together AIzai-org/GLM-5.3-Flash ↗12.4 s+9221 ms2.26 s34.0 tok/s98.5%-1.0 pp1.0%
Nebiuszai-org/GLM-5.3-Flash ↗15.0 s+11742 ms7.11 s78.8 tok/s84.8%-14.7 pp14.8%
DeepInfrazai-org/GLM-5.3-Flash ↗17.1 s+13842 ms6.41 s9.0 tok/s95.5%-4.0 pp4.0%

Provider by provider, in the order above

  • Baseten: 331 ms median, 3.23 seconds end-to-end p99, time to first token p95 1.16 seconds, 240.5 tokens per second, 95.8% task success, 0.0% failures.
  • Modal: 574 ms median, 3.28 seconds end-to-end p99, time to first token p95 1.09 seconds, 222.2 tokens per second, 99.3% task success, 0.0% failures.
  • Telnyx: 669 ms median, 3.48 seconds end-to-end p99, time to first token p95 1.34 seconds, 184.3 tokens per second, 99.5% task success, 0.0% failures.
  • Novita AI: 1.74 seconds median, 7.25 seconds end-to-end p99, time to first token p95 3.41 seconds, 60.6 tokens per second, 94.2% task success, 4.0% failures.
  • Z.AI: 1.75 seconds median, 8.59 seconds end-to-end p99, time to first token p95 3.14 seconds, 56.3 tokens per second, 97.5% task success, 0.2% failures.
  • Fireworks AI: 1.95 seconds median, 10.49 seconds end-to-end p99, time to first token p95 5.83 seconds, 58.2 tokens per second, 99.5% task success, 0.0% failures.
  • Parasail: 1.59 seconds median, 11.51 seconds end-to-end p99, time to first token p95 5.54 seconds, 52.4 tokens per second, 98.3% task success, 1.3% failures.
  • Together AI: 609 ms median, 12.45 seconds end-to-end p99, time to first token p95 2.26 seconds, 151.6 tokens per second, 98.5% task success, 1.0% failures.
  • Nebius: 1.45 seconds median, 14.97 seconds end-to-end p99, time to first token p95 7.11 seconds, 269.3 tokens per second, 84.8% task success, 14.8% failures.
  • DeepInfra: 1.83 seconds median, 17.07 seconds end-to-end p99, time to first token p95 6.41 seconds, 69.4 tokens per second, 95.5% task success, 4.0% failures.
[02] workflow fit

Which enterprise workstreams does the dataset represent?

  • Knowledge access. Meeting-note lookup represents extracting one actionable fact from context already supplied to the model.
  • Operations automation. Ticket triage represents bounded decisions that route work into existing business processes.
  • Document digitization. Contract extraction represents converting unstructured business text into typed system-of-record fields.
[03] how it was measured

How is LLM inference provider latency and accuracy measured here?

  • Same model. Every endpoint serves GLM 5.3 Flash; the provider layer is the only variable.
  • Same request. One-turn, non-tool chat completion in JSON-object mode, streaming on, temperature 0, top-p 1, reasoning low, 256-token ceiling, 20-second timeout, concurrency one.
  • Client clock, pinned tokenizer. Time to first token, end-to-end latency and tokens per second come from the client; visible answer tokens are counted with a pinned tiktoken o200k_base tokenizer, never provider-reported usage.
  • Exact task success. An answer counts only when it is schema-valid and exactly correct under the task rubric. Timeouts, HTTP errors and malformed responses stay in the denominator.
  • Serverless endpoints only. Dedicated capacity is outside the comparison. Runner and per-request records: openbenchmarks-labs/inference ↗.
[04] faq

Enterprise inference diligence

Can one benchmark name the best provider for an entire AI program?

No. A company-wide program spans workloads and requirements that cannot be reduced to one provider rank. Baseten led end-to-end p99 latency at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The scorecard keeps latency, tokens per second, accuracy, and failures separate rather than blending them into one synthetic score. The benchmark informs short operational workloads; enterprise selection still requires workload replay and non-performance diligence.

Which measured gates should an enterprise pilot set before provider selection?

Set workload-specific floors for exact task success and operational failure rate, then latency budgets for blocking and interactive paths. Replay representative company data before moving from this shortlist to a production decision.

What procurement and governance questions remain outside these results?

The benchmark does not assess security controls, privacy terms, data residency, governance, support, reserved capacity, enterprise pricing, procurement risk, or performance on the company's private workloads.

[05] related comparisons
[06] source benchmark

Read the complete LLM inference provider benchmark

Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the LLM Inference Provider Benchmark →

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