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Hosted open-weight model inference

Run Llama/Qwen/DeepSeek/etc. without owning GPUs, with production DX.

At a glance — Oct 2026

Updated 2026-10-27 · forward this page: /jtbds/open-model-inference#brief

Scores as of 2026-09-14 (43 days old; Depot marks scores stale after 30 days). The pick is still Depot's current editorial pick; it has not been re-reviewed since. Prices below carry their own dates.

Depot Pick
Fireworks AI · 8.35/10 for this job (curated estimate, high confidence)

Catalog freshness, tokens/sec, OpenAI-compat DX, and pricePerf.

Runner-up
Together AI · 8.20/10

Scores higher than the pick on: Price / performance, Maturity / track record.

When not to use the pick
Need Hub-native custom model endpoints (HF) or ultra-low latency on narrow SKUs (Groq).
Lock-in / migration
Pick exit signals: serves open-weight models (provider tag `open-models`). Self-host fallback listed for this job: Qwen3-0.6B-Base, DeepSeek-V3.2 weights, Gemma models under the Gemma Terms of Use.
Sourced price (usd_per_million_input_tokens)

Pick: no sourced price in this job's unit (no_matching_sourced_price).

Cheapest matching SKU: Chutes Qwen3.8-27B (Qwen/Qwen3.8-27B-TEE) $0.039 — source, as of 2026-10-27, re-checked 2026-10-27. Not the pick; not quality-adjusted.

Price history: first seen 2026-10-03 · last changed 2026-10-19 · history JSON

Free option
  • DeepSeek-R1 weights (oss) — source, as of 2026-10-25
  • DeepSeek-V3.2 weights (oss) — source, as of 2026-10-25
  • exo (oss) — source, as of 2026-10-25
Failure modes to watch
  • Runaway spend: Set a budget and a tool-call cap per task. Alert when spend jumps. Stop the run at the cap instead of summarizing a partial trace as success.
  • Context overflow: If the input exceeds the window, say so and retrieve a narrower slice. Do not answer from a truncated head as if it were the whole source.
  • Lost in the middle: Ask the agent to name the passage it used. Spot-check questions whose only support sits in the middle of the context, not only at the edges.
  • Non-determinism: Run the task more than once and compare the end state, not a single transcript. Record the trial count next to any success rate you cite.
  • Schema drift: Validate every tool call and every loaded table against the current schema. Quarantine mismatches instead of coercing them.
  • Silent truncation: Tools must return whether the payload was truncated and how long the original was. Fail the task if the answer depends on the missing span.
Reported outcomes (submitted reports only)

This job: loading… JSON · reports never change scores · seed/test records excluded

Depot Pick (Fireworks AI) on this job: loading… JSON · reports never change scores · seed/test records excluded

Cite: Agent Provider Depot, Depot Pick for open-model-inference: fireworks, scores as of 2026-09-14. Agents: GET /api/decide?jtbd=open-model-inference

Free Hosted open-weight model inference options · All sourced prices, cheapest first · for local-offline agents

Depot Pick

Fireworks AI

Catalog freshness, tokens/sec, OpenAI-compat DX, and pricePerf.

Pricing (directional): Serverless per-token + on-demand GPU; day-0 OSS model support; post-training stack. Free credits often available.

Cite: Depot Pick for open-model-inference: fireworks
Overall
8.3
curated_estimate
confidence: high · as of 2026-09-14
Capability fit
8.6
Price / performance
8.8
Latency / reliability
8.4
Developer & agent DX
9.0
Privacy / data retention
7.5
Lock-in risk (higher = worse)
3.5
Maturity / track record
8.0

Lock-in risk: higher = more lock-in (worse). Overall already inverts this dimension.

Sources: Fireworks AI: https://fireworks.ai · Best LLM API Providers (Fireworks blog): https://fireworks.ai/blog/best-llm-api-providers

Top pick for production open-model serving with real DX. Prefer over random GPU DIY when you want tokens not ops.

Quote for Hosted open-weight model inference

Depot Pick stays Fireworks AI. Prices below are sourced SKU rows only (as of 2026-10-27). Input and output are not blended. GET /api/quote?jtbd=open-model-inference

Pick is not a price winner. Cheapest sourced input $/1M among candidates is chutes at 0.039. The pick has no priceSortEligible input row.

  • Embeddings 150M-350M params usd_per_million_input_tokens $0.016 source as of 2026-10-03
  • Qwen3 8B usd_per_million_input_tokens $0.1 source as of 2026-10-03
  • Embeddings up to 150M params usd_per_million_input_tokens $0.008 source as of 2026-10-03
  • serverless chat models usd_per_million_input_tokens amount unavailable source as of 2026-10-03
  • B200 180 GB usd_per_gpu_hour $13 source as of 2026-10-27
  • B300 288 GB usd_per_gpu_hour $15 source as of 2026-10-27
  • GB300 288 GB usd_per_gpu_hour $20 source as of 2026-10-27
  • H100 80 GB usd_per_gpu_hour $8 source as of 2026-10-27
  • H200 141 GB usd_per_gpu_hour $8 source as of 2026-10-27
  • Managed training LoRA SFT up to 16B usd_per_million_train_tokens $0.5 source as of 2026-10-03

13 of 25 candidates have no sourced price row. 16 are excluded from the Input $/1M comparison (chat_completion_input). min sourced input SKU among candidates; not quality-adjusted

Sourced Input $/1M for Hosted open-weight model inference

Candidates for this job that already have a matching sourced price (chat_completion_input), cheapest first. As of 2026-10-27. Each figure is one SKU in that unit, not a blend of input and output, not a cross-modal rate, and not a Depot score. min sourced input SKU among candidates; not quality-adjusted The Depot Pick is not a price winner.

ProviderInput $/1MSKUSourceAs of
Chutes$0.039Qwen3.8-27B (Qwen/Qwen3.8-27B-TEE)pricing page2026-10-27
Groq$0.075openai/gpt-oss-20bpricing page2026-10-03
DeepInfra$0.1Llama-3.3-70B-Instruct-Turbopricing page2026-10-03
FriendliAI$0.14gemma-4-31B-itpricing page2026-10-03
Together AI$0.15GLM-5.3-Flashpricing page2026-10-03
SambaNova$0.22gpt-oss-120bpricing page2026-10-03
Cerebras$0.35gpt-oss-120bpricing page2026-10-03
Moonshot (Kimi)$0.95kimi-k2.6pricing page2026-10-20
OpenRouter$2anthropic/claude-sonnet-5.5pricing page2026-10-03

Runners-up

When NOT to use the top pick

Need Hub-native custom model endpoints (HF) or ultra-low latency on narrow SKUs (Groq).

Full ranking (score table)

Ranked with this JTBD's weights. Scores are curated estimates unless marked otherwise. Agents: prefer /api/decide then this table for tie-breaks.

#ProviderOverallFit$/perfLatencyDXAs of
1Fireworks AIDepot Pick8.38.68.88.49.02026-09-14
2Together AI8.28.59.08.08.42026-09-14
3Scaleway Generative APIs8.17.88.68.08.22026-09-14
4FriendliAI8.08.48.08.48.22026-10-01
5IONOS AI Model Hub8.07.88.48.07.82026-09-14
6OpenRouter8.08.87.87.69.22026-09-14
7Infomaniak AI8.07.48.08.27.82026-09-14
8Groq8.07.89.08.28.82026-09-14
9DeepInfra8.08.09.47.58.52026-09-14
10Hugging Face Inference8.08.47.27.48.62026-09-14
11DeepSeek8.08.89.87.88.02026-09-14
12Moonshot (Kimi)7.98.68.47.88.22026-09-14
13Predibase7.98.48.07.88.22026-09-14
14Replicate7.88.46.08.29.42026-09-14
15OVHcloud AI Endpoints7.77.28.87.87.22026-09-14
16NVIDIA NIM / NGC7.78.67.08.06.82026-10-19
17Cerebras7.68.08.27.87.52026-09-14
18SambaNova7.58.07.88.26.82026-09-14
19Novita AI7.47.29.07.07.82026-09-14
20STACKIT AI Model Serving7.47.08.07.86.52026-09-14
21SiliconFlow7.37.49.26.87.62026-09-14
22Lepton AI7.37.28.27.27.82026-09-14
23Hyperbolic7.37.08.86.88.02026-09-14
24Chutes7.37.49.26.87.62026-09-14
25Infermatic6.55.58.05.87.02026-09-14

Score methodology for this job

Catalog freshness, tokens/sec, OpenAI-compat DX, and pricePerf.

  • capabilityFit: 25%
  • pricePerformance: 18%
  • latencyReliability: 15%
  • developerDx: 15%
  • privacyPosture: 10%
  • lockInRisk: 7%
  • maturity: 10%